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Master’s Thesis

Analysis of Operational Performance of Old Baneshwor Intersection in Kathmandu for Vehicular Traffic

Gopi Chandra Shrestha

Nepal Engineering College Changunarayan, Bhaktapur Pokhara University Nepal

June, 2018

Analysis of Operational Performance of Old Baneshwor Intersection in Kathmandu for Vehicular Traffic

By Gopi Chandra Shrestha (TEAM 013-1209)

A thesis submitted in partial fulfilment of the requirements for the degree of Masters of Science (M.Sc.) in Transportation Engineering and Management awarded by Pokhara University

Nepal Engineering College Changunarayan, Bhaktapur Pokhara University Nepal

June, 2018

ABSTRACT

Old Baneshwor intersection is one of the major traffic junctions in Kathmandu Valley used from the north-eastern zones to access the central and southern zones of the Valley. It is a four way intersection with a high traffic demand flow and controlled by traffic police during peak traffic flow periods. In spite of widening of the road corridors intersecting at this junction, one can usually observe congestion and long queues of vehicles waiting in queues to cross the intersection for a considerable time during the high traffic volume. It seems that no consideration has been given in the proper and adequate improvement and operational management of the intersection. The main objective of this research is to investigate operational performance of the Old Baneshwor intersection for vehicular traffic at present and explore into various viable improvements to enhance its operational performance for vehicular traffic. Classified traffic volume study was conducted by recording video from 8:00 to 11:00 in the AM and from 4:00 to 7:00 in the PM for three typical weekdays at the intersection and upstream of queues in each approach of the intersection. Studies of prevailing Saturation flow, phasing and timing data based on observation of the traffic police controlling the intersection, negotiation speeds of various turning movements were conducted with the recorded video footage. Studies of approach cruise speeds, back of queues were conducted manually at the field. The collected data were organized, critically analysed in MS-Excel and prepared for the analysis of performance of the intersection. The maximum total departure volume from the intersection was found out to be 5387 veh/h (2370 PCU/h) from 10:00 to 11:00 in the AM. Total demand (vehicle arrival) volume during the peak hour reached up to 5545 veh/h (2419 PCU/h). Motorcycles were found out to be 71.3% in the total traffic mix. The percentage of heavy vehicles was 1.0 % during the study period. SIDRA Intersection 5.1 was used for developing the traffic models of the intersection. The model was calibrated to represent the particular intersection conditions as closely as possible. Local calibration of the default basic saturation flow was performed for the existing base case model. Validation of the intersection traffic model of the existing base case was carried out by using 95 percentile back of queue and degree of saturation. Validation of the model was accepted based on degree of saturation. It was assumed in analysis that there was not any parking or vehicle stopping within 75 m from the stop line in the approach and exit lanes in each intersection leg. The performance of the intersection was studied at the present condition with traffic police control without any improvement and with various six options of combinations of lane configurations and signal phasing for traffic signalization of the intersection without geometric improvement. Further, analysis of the intersection with geometric improvement was performed for future year performance. A sustainable traffic growth rate of 2 % per year was adopted for future year analysis. The performance measures for evaluation were mainly capacity, degree of saturation (DoS), average delay, LOS, back of queue (BOQ), and overall performance index (PI).

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The overall performance level of the intersection at present condition with traffic police control without any improvement was found to be at LOS F, oversaturated with DoS = 1.16, average overall intersection delay of 98.3 sec/veh. Fixed time signal method was used for traffic signalization. The analysis of alternative options showed that inadequate lane assignments in the approaches of the Gausala and Sinamangal legs with higher traffic demand and inefficient phasing of the traffic police during the peak hour were the main causes of poor performance of the intersection at present with traffic police control. Hence, it was concluded that improvement in lane configuration with rearrangement of lane assignments and minor adjustment of lane widths within the existing carriageway and optimum traffic signal phasing and timing are necessary for improving the operational performance of the intersection for vehicular traffic. Among the six options A1, A2, B1, B2, C1, and C2 that were evaluated with signalization and adjustment of lane assignments,option C2 had the least PI = 116.5 and least delay of 32.6/veh. Hence, it was concluded this option is best performing in terms of all performance parameters for signalization at the base year. Further evaluation of option C2 for the design life of 5 years revealed that the intersection in this option will perform at an unacceptable LOS E at the end of 5 years. So, a geometric improvement in the option C2 was proposed by increasing the radii of the existing very sharp corner kerbs to 9 m to 15 m for more efficient traffic operation. Analysis of this geometric improvement for various future years showed intersection performance is enhanced to an acceptable LOS C with the worst lane performing at LOS D by the year 2028. So grade separation will not be required up to year 2028 provided the traffic growth rate is maintained at a sustainable rate of2 % per year, which can be achieved by implementing walking, cycling and public transport favoured land use and urban transport policies. It is recommended to improve the intersection with rearrangement of lane assignments and minor adjustment of lane widths as per the lane configuration C with installation of traffic signalization for short term up to the year 2020. Then the proposed geometric improvement by increasing the radii of the corner turning kerbs along with traffic signalization is recommended to be implemented up to year 2028.

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Declaration

I hereby declare that this study entitled Analysis of Operational Performance of Old Baneshwor Intersection in Kathmandu for Vehicular Traffic is based on my original research work. Related works on the topic by other researchers have been duly acknowledged. I owe all the liabilities relating to the accuracy and authenticity of the data and any other information included hereunder.

Signature: Name of the student: Gopi Chandra Shrestha Date:

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Recommendation

This is to certify that this thesis entitled Analysis of Operational Performance of Old Baneshwor Intersection in Kathmandu for Vehicular Traffic prepared and submitted by Gopi Chandra Shrestha, in partial fulfilment of the requirements of the degree of Master of Science (M.Sc.) in Transportation Engineering and Management awarded by Pokhara University, has been completed under my supervision. I recommend the same for acceptance by Pokhara University.

Signature: Name of supervisor: Er. Subash Dhungel Independent Consultant Traffic-Transportation & Road-safety Date:

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Certification

This thesis entitled Analysis of Operational Performance of Old Baneshwor Intersection in Kathmandu for Vehicular Traffic prepared and submitted by Gopi Chandra Shrestha has been examined by us and is accepted for the award of the degree of Master of Science (M.Sc.) in Transportation Engineering and management by Pokhara University.

Prof. Dr. Padma Bahadur Shahi External Examiner

………………. Signature

……………. Date

Er. Subash Dhungel Independent Consultant Traffic-Transportation & Road-safety Supervisor

……………….

…………….

Signature

Date

Prof. Dr. Khem Raj Sharma Director nec-CPS

………………. Signature

……………. Date

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Acknowledgements

First of all, I would like to express my sincere gratitude and appreciation towards my thesis supervisor Er. Subash Dhungel (Independent Traffic-Transportation & Roadsafety Consultant) for his valuable support, constant guidance, critical comments and encouragement throughout the thesis without which this work would not have been possible. He is a constant source of inspiration and working under him is an unforgettable experience. I express my gratitude to all the personnel of Nepal Engineering College, who helped me during this thesis work particularly, Prof. Dr. Khem Raj Sharma (Director, necCPS), Associate Prof. Dr. Thusitha Chandani Shahi (TEAM Coordinator, nec-CPS), ER. Rabindra Pokhrel (Assistant Professor, nec-CPS), Dr. Asish Ghimire (Research Coordinator). I am also very grateful to Er. Prashant Malla (Directorof AVIYAAN Consultancy and Softwel P. ltd) for providing me equipment like the CC cameras and video recorders along with the Traffic Count software and Excel Macro program for traffic volume study, without which this work would not have been possible. I appreciate the support of the consultancy’s employees: Er. Suraj Kacchyapati, and Er. Raj Mohan Sijakhwa for supporting me in setting up the CC cameras and guiding me in using the Traffic Count Software and Excel macro program inthe traffic volume data reduction. My thanks also go to all the house owners, who permitted me to fix the CC cameras on top of their houses for recording the traffic video. Lastly, I would like to thank my all friends Sanam Khanal, Prakash Dangal, Saroj Chaudahary, Om Narayan Chaudhary, Ram Karki, Ujwal Shrestha, Raj Karmacharya, Umesh Hyaumikha, and my son Aditya Chandra Shrestha for their unforgettable and laborious support in field data collection and data entry, and thanks to my friends of TEAM program for their invaluable support and feedback during this study. Lastly, I would like to thank my family for their constant support and patience throughout my study period.

Gopi Chandra Shrestha TEAM 013-1209

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Table of contents

Title Page ABSTRACT.......................................................................................................................... ii Declaration ........................................................................................................................... iv Recommendation .................................................................................................................. v Certification ......................................................................................................................... vi Acknowledgements ............................................................................................................. vii Table of contents ................................................................................................................ viii List of tables......................................................................................................................... xi List of figures ..................................................................................................................... xiii List of Appendices .............................................................................................................. xv Abbreviations and Acronyms ............................................................................................ xvi Chapter 1 ............................................................................................................................... 1 INTRODUCTION ................................................................................................................ 1 1.1 Background ............................................................................................................... 1 1.2 Problem Statement .................................................................................................... 2 1.3 Research Questions ................................................................................................... 2 1.4 Research Objectives .................................................................................................. 2 1.5 Significance of the Study .......................................................................................... 3 1.6 Scope and Limitation of the Study ............................................................................ 3 1.6.1 Scope of the Study......................................................................................... 3 1.6.2 Limitation of the Study ................................................................................. 3 Chapter 2 ............................................................................................................................... 4 LITERATURE REVIEW ..................................................................................................... 4 2.1 Performance Measures .............................................................................................. 4 2.2 Road Capacity ........................................................................................................... 4 2.3 Signalized Intersection Capacity ............................................................................... 4 2.4 Level of Service (LOS) ............................................................................................. 5 2.5 Beyond LOS F .......................................................................................................... 6 2.6 Signalized Intersection Flow Characteristics ............................................................ 6 2.7 Demand Flow Rate.................................................................................................... 6 2.8 Saturation Flow Rate at Signalized Intersection ....................................................... 7 2.9 Traffic Signal Controller Characteristics ................................................................ 10 2.10 Concepts of Delay at Signalized Intersection ......................................................... 10 2.10 Peak hour factor (PHF) ........................................................................................... 14 2.11 SIDRA intersection Software.................................................................................. 14 2.12 Model Calibration in SIDRA intersection .............................................................. 15 2.13 Review of relevant previous theses and study reports on intersection evaluation. ............................................................................................................... 16 2.13.1 Comparison of Intersection Capacity with Traffic flow in Kabul Metropolitan Area for 2008, 2014, and 2025 ............................................. 16 2.13.2 Detailed traffic study and design for grade separated intersections at five major junctions in Kathmandu............................................................. 16

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2.13.3 Kathmandu Sustainable Urban Transport Project (KSUTP)....................... 19 2.13.4 Development of Traffic diversion algorithm for the possible reduction of Traffic demand at intersection: a case study of Thapathali intersection .................................................................................................. 20 2.13.5 Comparison of Probable Congestion Reduction Approaches at New Baneshwor Intersection in Kathmandu ....................................................... 20 Chapter 3 ............................................................................................................................. 21 RESEARCH METHODOLOGY........................................................................................ 21 3.1 Research Design ...................................................................................................... 21 3.2 Research Approach ................................................................................................. 22 3.3 Study Area............................................................................................................... 23 3.4 Sample Size and Sample Selection ......................................................................... 23 3.5 Data Collection ....................................................................................................... 23 3.5.1 Intersection Geometry ................................................................................. 25 3.5.2 Traffic Volume and Pedestrian Volume...................................................... 25 3.5.3 Passenger Car Unit (PCU)........................................................................... 27 3.5.4 Prevailing saturation flow rate .................................................................... 28 3.5.5 95 percentile back of queue ......................................................................... 28 3.5.6 Vehicle Composition ................................................................................... 29 3.5.7 Size of Vehicle and Queue Space ............................................................... 29 3.5.8 Approach and Exit Cruise Speeds ............................................................... 30 3.5.9 Negotiation Speed, Distance, and Radius ................................................. 31 3.5.10 Phasing and Timing Data .......................................................................... 31 3.5.11 Traffic Growth rate.................................................................................... 32 3.6 Processing of Data .................................................................................................. 32 3.7 Identification of Peak hour and Peak hour factor for the analysis .......................... 32 3.8 Calibration of SIDRA Intersection Model .............................................................. 33 3.9 Validation of base model ........................................................................................ 34 3.10 Data Analysis .......................................................................................................... 35 3.10.1 Evaluation of Operational Performance Measures...................................... 35 3.7 Research Matrix ...................................................................................................... 38 Chapter 4 ............................................................................................................................. 39 RESULTS AND DISCUSSION ......................................................................................... 39 4.1 Intersection Geometry ............................................................................................. 39 4.2 Land use and Building uses .................................................................................... 40 4.3 Traffic and Pedestrian Volume ............................................................................... 40 4.4 Traffic Volume – Directional Vehicular Flows ...................................................... 42 4.5 Traffic demand (arrival) flow ................................................................................. 46 4.6 Peak hour and Peak hour factor .............................................................................. 47 4.7 Traffic Composition ................................................................................................ 47 4.8 Cruise Speeds .......................................................................................................... 48 4.9 Intersection Path Data Parameters .......................................................................... 49 4.10 Prevailing (field measured) Saturation Flow Rate observation .............................. 49 4.11 Back of queue observation ...................................................................................... 50 4.12 Phasing and Signal timing....................................................................................... 50 4.13 Calibration of Basic Saturation Flow ...................................................................... 51 4.14 Validation of base case model ................................................................................ 52

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4.15 Evaluation of Operational Performance of the Intersection at present under traffic police control ................................................................................................ 53 4.16 Evaluation of various Lane Configuration Options with signalization of the Intersection for the base year 2018 ......................................................................... 56 4.16.1 Evaluation of Option A1 (Lane configuration A, 2 signal phasing plan, Default basic saturation flow used)............................................................. 59 4.16.2 Evaluation of Option A2 (Lane configuration A, 2 signal phasing plan, Local calibration of default basic saturation flow performed).................... 61 4.16.3 Evaluation of Option B1 (Lane configuration B, Protected right turns from Sinamangal and Gausala legs, 4 signal phasing plan)........................ 63 4.16.4 Evaluation of Option B2 (Lane configuration B, Protected and permitted right turns from Sinamangal and Gausala legs, Exclusive left turns allowed in all phases, 3 signal phasing plan) ..................................... 65 4.16.5 Evaluation of Option C1 (Lane configuration C, Protected and permitted right turns from Sinamangal and Gausala legs, Exclusive left turns allowed in all phases, 3 signal phasing plan) ..................................... 67 4.16.6 Evaluation of Option C2 (Lane configuration C, Protected and permitted right turns from Sinamangal and Gausala legs, Exclusive left turns allowed in all phases, 2 signal phasing plan) ..................................... 69 4.16.7 Comparison of Various Options .................................................................. 71 4.18 Evaluation of Option C2 for the future year 2023 (5 years design life) ................. 71 4.19 Geometric Improvement Proposed for the Intersection .......................................... 73 4.20 Evaluation of Performance of the Intersection with Proposed Geometric Improvement ........................................................................................................... 75 4.20.1 Evaluation of proposed geometric improvement for the base year 2018 .... 75 4.20.2 Evaluation of proposed geometric improvement for the year 2020 ............ 76 4.20.3 Evaluation of proposed geometric improvement for the year 2023 ............ 77 4.20.4 Evaluation of proposed geometric improvement for the year 2028 ............ 78 4.20.5 Summary of performance evaluation of the proposed geometric improvement of the Intersection for various years ..................................... 79 Chapter 5 ............................................................................................................................. 80 CONCLUSION AND RECOMMENDATIONS ............................................................... 80 5.1 Conclusion .............................................................................................................. 80 5.1 Recommendation .................................................................................................... 82 5.3 Scope for Future Works .......................................................................................... 82 REFERENCES ................................................................................................................... 83 APPENDICES .................................................................................................................... 85

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Listof tables

Title Page Table 2.1: Delay & v/c (HCM 2010) method for Level of Service definitions based on delay and v/c ratio for vehicles .......................................................... 5 Table 2.2: Default Basic Saturation flows in through car units (tcu) per hour................... 8 Table 3.1: Form of Movement Data exported from Traffic Count Software ..................... 2 Table 3.2: Passenger Car Unit (PCU) of various types of Vehicles ................................. 28 Table 3.3: Vehicle Types .................................................................................................. 29 Table 3.4: Vehicle Dimensions ........................................................................................ 30 Table 3.5: Values of vehicle length and Queue space parameters adopted ...................... 30 Table 3.6: Short-base lengths ........................................................................................... 30 Table 3.7: GEH statistic values and its indications .......................................................... 34 Table 3.8: Research Matrix .............................................................................................. 38 Table 4.1: Hourly Intersection Departure Volume for 3 days .......................................... 40 Table 4.2: Fifteen Minute Interval Intersection Departure Volume for 3 days ................ 41 Table 4.3: AM peak hour Turning Movement Volumes for input in SIDRA Intersection ...................................................................................................... 44 Table 4.4: Peak hour Demand (Vehicle arrival) Volume for each leg ............................. 46 Table 4.5: Summary of Approach Cruise Speed Study .................................................... 48 Table 4.6: Path Data of various Movements at the Intersection in the Existing Base Case Condition ....................................................................................... 49 Table 4.7: Summary of lane saturation flow study at the existing intersection................ 50 Table 4.8: Summary of back of queue survey during the AM Peak hour ........................ 50 Table 4.9: Summary traffic police assigned phase time observations .............................. 51 Table 4.10: Phase Timing Results based on observation of Traffic Police Control ........... 51 Table 4.11: Comparison of Field measured and Model estimated Saturation Flows with GEH statistics .......................................................................................... 52 Table 4.12: Comparison of observed and model estimated 95th percentile back of queue ............................................................................................................... 52 Table 4.13: Comparison of observed and model estimated degree of saturation (DoS) for the AM peak hour ...................................................................................... 53 Table 4.14: Summary of operational performance of the intersection at present under traffic police control ........................................................................................ 53 Table 4.15: Summary of performance of the intersection for Option A1 ........................... 59 Table 4.16: Summary of performance of the intersection for Option A2 ........................... 61 Table 4.17: Summary of performance of the intersection for Option B1 ........................... 63 Table 4.18: Summary of performance of the intersection for Option B2 ........................... 65 Table 4.19: Summary of performance of the intersection for Option C1 ........................... 67 Table 4.20: Summary of performance of the intersection for Option C2 ........................... 69 Table 4.21: Comparison of Overall Performance Measures of the Intersection for various options (For the base year 2018) ........................................................ 71 Table 4.22: Summary of performance evaluation of Option C2 (Year 2023) .................... 72 Table 4.23: Details of the corner kerb radius ...................................................................... 74 Table 4.24: Evaluation results of geometric improvement for the base year 2018 ............ 75 Table 4.25: Evaluation results of geometric improvement for the year 2020 .................... 76

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Table 4.26: Evaluation results of geometric improvement for the year 2023 .................... 77 Table 4.27: Evaluation results of geometric improvement for the year 2028 .................... 78 Table 4.28: Summary of performance evaluation of proposed geometric improvement of the intersection for various years .................................................................. 79

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List of figures

Title Page Figure 2.1: Saturation flow and the related signal timing parameters ................................. 8 Figure 2.2: Definition of delay experienced by a vehicle stopping at traffic signals ........ 11 Figure 2.3: Delays experienced by vehicles in oversaturated conditions .......................... 11 Figure 2.5: Maximum daily entering volume thresholds for primary and secondary roads at-grade ................................................................................................... 18 Figure 3.1: Flow Chart of Research Methodology ............................................................ 21 Figure 3.2: Flow chart of research approach .................................................................... 22 Figure 3.3: Location of Old Baneshwor Intersection ........................................................ 24 Figure 3.4: A plan showing the locations of CC Camera set up ....................................... 26 Figure 3.5: Infrared CC Camera to capture the video ....................................................... 26 Figure 3.6: Digital Video Recorder to record the video captured by CC Camera ............ 26 Figure 3.7: Screen shot of the user interface of “Traffic Count” Software ...................... 27 Figure 3.8: Movement Path Data Definitions ................................................................... 32 Figure 4.1: Present Intersection Geometry of Old Baneshwor Intersection ..................... 39 Figure 4.2: Hourly Intersection Departure Volume (average of three days) ..................... 42 Figure 4.3 Fifteen Minute Interval Intersection Departure Volumes (Average of 3 days) .................................................................................................................. 42 Figure 4.4: AM Peak Hour Departure Volumes and Turning Movement at Old Baneshwor Intersection (in veh/h) .................................................................... 43 Figure 4.5: AM peak hour Turning Movement Volumes classified as HV and LV for input in SIDRA Intersection base case model .................................................. 44 Figure 4.6: Classified Volume of directional vehicular movements (AM peak hour) ....... 45 Figure 4.7: AM Peak hour directional vehicular flows ...................................................... 45 Figure 4.8: PM Peak hour directional vehicular flows ...................................................... 45 Figure 4.9: Hourly Intersection Total Demand (Vehicle arrival) Volume observed at u/s of the intersection queues ........................................................................... 46 Figure 4.10: Flow variation within the Peak Hour in PCU/15 min ................................... 47 Figure 4.11: Flow variation within the Peak Hour in vehicles/15min ............................... 47 Figure 4.12: Traffic composition at Old Baneshwor Intersection by vehicle types .......... 47 Figure 4.13: Traffic composition at Old Baneshwor Intersection by vehicle class ........... 48 Figure 4.14: Phasing Plan assigned by Traffic Police in existing condition ..................... 51 Figure 4.15: Lane Configuration and LoS of various lanes of the intersection under police control at present in the AM Peak hour ................................................. 54 Figure 4.16: Peak hour flow, Capacity, Degree of saturation, average control delay, 95 percentile back of queue, and LoS at the present condition under police Control ............................................................................................................. 55 Figure 4.17: Lane Configuration A ..................................................................................... 56 Figure 4.18: Lane Configuration B ..................................................................................... 57 Figure 4.19: Lane Configuration C ..................................................................................... 58 Figure 4.20: Phasing Summary for Option A1 ................................................................... 59 Figure 4.21: Lane Configuration and LOS summary of Option A1 ................................... 60 Figure 4.22: Phasing summary of Option A2 ..................................................................... 61 Figure 4.23: Lane Configuration and LOS summary of Option A2 ................................... 62

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Figure 4.24: Phasing Summary of Option B1..................................................................... 63 Figure 4.25: Lane Configuration and LOS summary of Option B1 ................................... 64 Figure 4.26: Phasing Summary of Option B2..................................................................... 65 Figure 4.27: Lane Configuration and LOS summary of Option B2 ................................... 66 Figure 4.28: Phasing Summary of Option C1..................................................................... 67 Figure 4.29: Lane Configuration and LOS summary of Option C1 ................................... 68 Figure 4.30: Phasing Summary of Option C2..................................................................... 69 Figure 4.31: Lane Configuration and LOS summary of Option C2 ................................... 70 Figure 4.32: Phasing Summary of Option C2 for the year 2023 ........................................ 72 Figure 4.33: Lane Configuration and LOS summary of Option C2 (Year 2023) ............... 73 Figure 4.34: Lane configuration-C with improvement of corner kerb radii ....................... 74 Figure 4.35: LOS summary of geometric improvement for the base year 2018 ................ 75 Figure 4.36: LOS summary of geometric improvement for the year 2020 ........................ 76 Figure 4.37: LOS summary of geometric improvement for the year 2023 ........................ 77 Figure 4.38: LOS summary of geometric improvement for the year 2028 ........................ 78

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List of Appendices

Title Page Appendix-1.1: Fifteen Minute Classified Counts of Turning movements in the AM Peak Hour (Average of 3 days) ................................................................... 86 Appendix-1.2: Fifteen Minute Intersection Counts of Turning movements (Average of three days)............................................................................................... 87 Appendix-1.3: Fifteen Minute Intersection Counts of Turning movements on Monday, May 7, 2018 ................................................................................................ 88 Appendix-1.4: Fifteen Minute Intersection Counts of Turning movements on Tuesday, May 8, 2018................................................................................. 89 Appendix-1.5: Fifteen Minute Intersection Counts of Turning movements on Wednesday, May 9, 2018............................................................................ 90 Appendix-1.6: 15 minute Interval Intersection Total Demand (Arrival) Volume at the U/S of intersection Queues ......................................................................... 91 Appendix-1.7:Hourly Intersection Total Demand (Arrival) Volume at the U/S of intersection Queues ..................................................................................... 92 Appendix-1.8:Fifteen Minute Interval Vehicle Arrival Counts (Counted at upstream of the Intersection queues) on Monday, May 7, 2018 ................................ 93 Appendix-1.9:Fifteen Minute Interval Vehicle Arrival Counts (Counted at upstream of the Intersection queues) on Tuesday, May, 2018 ................................... 94 Appendix-1.10:Fifteen Minute Interval Vehicle Arrival Counts (Counted at upstream of the Intersection queues)on Wednesday, May 9, 2018 ............................ 95 Appendix-2.1:Approach Cruise Speed Survey of Gausala (North) Approach ................... 96 Appendix-2.2:Approach Cruise Speed Survey of Maitidevi (West) Approach ................. 99 Appendix-2.3:Approach Cruise Speed Survey of Sinamangal (East) Approach ............. 102 Appendix-2.4:Approach Cruise Speed Survey of New Baneshwor (South) Approach ... 105 Appendix-3.1:Field Measured Back of Queue in Sinamangal Approach AM Peak Hour) ......................................................................................................... 108 Appendix-3.2:Field Measured Back of Queue in Gausala Approach (AM Peak Hour) .. 109 Appendix-3.3:Field Measured Back of Queue in Maitidevi Approach (AM Peak Hour) ......................................................................................................... 110 Appendix-3.4:Field Measured Back of Queue in New Baneshwor Approach (AM PeakHour) ................................................................................................. 111 Appendix-4.1: Saturation Flow Study of shared lane (TR) in New Baneshwor approach .................................................................................................... 112 Appendix-4.2: Saturation Flow Study of left turn lane in Gausala approach ................... 113 Appendix-4.3: Saturation Flow Study of shared lane (TR) in Gausala approach ............ 114 Appendix-4.4: Saturation Flow Study of shared lane (LTR) in Sinamangal approach .... 115 Appendix-4.5: Saturation Flow Study of shared lane (TR) in Maitidevi approach .......... 116 Appendix-5.1:Observation of Phase-A Timing of Traffic Police ..................................... 117 Appendix-5.2:Observation of Phase-B Timing of Traffic Police ..................................... 118 Appendix-6.0: Photographs .............................................................................................. 119

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Abbreviations and Acronyms

ADB ARR ARRB BOQ CBD DoR DOS DPTI DVR GEH GoN h HCM HV IRC SP ITE JICA KSUTP KVDA KVRIP LOS LV MC MoPIT nec-CPS NRS ORN PCU PFF PHF PI RMSNE sec

Asian Development Bank Australian Road Research Australian Road Research Board Back of queue Central Business District Department of Roads Degree of saturation Department of Planning, Transport and Infrastructure Digital Video Recorder Geoffrey E. Havers Government of Nepal hour Highway Capacity Manual Heavy Vehicle Indian Roads Congress Special Provision Institute of Transportation Engineers Japan International Cooperation Agency Kathmandu Sustainable Urban Transport Project Kathmandu Valley Development Authority Kathmandu Valley Road Improvement Project Level of Service Light Vehicle Motorcycle Ministry of Physical Infrastructure and Transport Nepal Engineering College-Centre for Postgraduate Nepal Road Standard Overseas Road Note Passenger car unit Peak Flow Factor Peak Hour Factor Performance Index Root Mean Square Normalised Error second

SIDRA

Signalized and Unsignalized Intersection Design and Research Aid

tcu TRB TRL UK USA v/c ratio

though car unit Transportation Research Board Transport Research Laboratory United Kingdom United States of America Volume-to-Capacity Ratio

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veh

Vehicle

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Chapter 1 INTRODUCTION 1.1

Background

An intersection is a road junction where two or more roads either meet or cross at grade. This intersection includes the areas needed for all modes of travel: pedestrian, bicycle, motor vehicle, and transit (including buses, streetcars, and street-running light rail). Thus, the intersection includes not only the pavement area, but typically the adjacent sidewalks and pedestrian curb cut ramps(Mathew, 2017). All the road junctions designated for the vehicles to turn to different directions to reach their desired destinations. Traffic intersections are complex locations on any highway. This is because vehicles moving in different direction want to occupy same space at the same time. In addition, the pedestrians also seek same space for crossing. Drivers have to make split second decision at an intersection by considering his route, intersection geometry, speed, and direction of other vehicles etc. A small error in judgment can cause severe accidents. It causes delay which depends on type, geometry, and type of control. Overall traffic flow depends on the performance of the intersections. It also affects the capacity of the road. Therefore, both from the accident perspective and the capacity perspective, the study of intersections are very important for the traffic engineers. Apart from many factors contributing to poor vehicular traffic flow along the main corridors, the poor performance of main intersections either due to oversaturation (demand volume exceeding the capacity), poor management, operation and inadequate geometric design of the intersection or improper traffic control to serve the existing traffic demand, contribute towards traffic congestion. This premise is the main rationale for this study and hence looks into the performance evaluation of the intersection. Roadway and street systems are ultimately controlled by the function of major intersections. It therefore becomes critical to evaluate the capacity of intersections (Kadiyali, 2012). Intersection failure directly reduces the number of vehicles that can be accommodated during the peak hours which have the highest demand and lowersthe roadway capacity of a corridor. For urban streets, their capacity is generally limited by the capacity of intersections, with segment characteristics seldom playing a major role in the determination of capacity (TRB, 2010). As a result of this strong impact on corridor function, intersection improvements can be a very cost-effective means of increasing a corridor’s traffic capacity. In some circumstances, correct intersection improvements may be able to eliminate or at least postpone costly corridor expansion projects.Though a substantial portion of total expense for roadway construction projects is required for design, construction, mobilization and adjacent area rehabilitation compared to the cost for only intersection improvement, a careful analysis must be made of the expected service life from the latter improvement option. With that in mind, it is important to determine how well the major intersections are functioning by evaluating their performance, (Camp Dresser & McKee Inc., 2010). TRB describes in Highway Capacity Manual (HCM 2010) that an intersection’s performance is described by the use of one or more quantitative measures that characterize 1

some aspect of the service provided to a specific road user group. Performance measures for the automobile modeinclude automobile volume-to-capacity ratio (v/c ratio, also called degree of saturation), automobile delay, and queue storage ratio (the maximum back of queue as a proportion of the available storage on the subject lane or link). Level of Service (LOS) is another important performance measure. It is useful for describing intersection performance to elected officials, policy makers, administrators, and the public. LOS is based on one or more of the performance measures such as volume-to-capacity ratio, automobile delay, or queue storage ratio. These measures serve as clues for identifying the source of problems and provide insight into the development of effective improvement strategies(TRB, 2010). The word automobile mode refers to travel by all motorized vehicles that can legally operate on the street, with the exception of local transit vehicles that stop to pick up passengers at the intersection (TRB, 2010). The word vehicles refer to motorized vehicles and include a mixed stream of automobiles, motorcycles, trucks, and buses. 1.2

Problem Statement

One can observe that traffic flows in the road network in Kathmandu Valley still face unacceptable congestion and delay during peak hours in spite of Kathmandu Valley Road Improvement Project (KVRIP) implemented jointly by Department of Roads (DoR) and Kathmandu Valley Development Authority (KVDA). Old Baneshwor intersection is one of the major intersections in Kathmandu Valley used by traffic from the north-eastern zones to access the central and southern zones of the Valley. In 2016, DoR and KVDA under KVRIP project have jointly completed the widening of the road corridors which meet at the Old Baneshwor intersection. In spite of the road corridor widening, one can observe that this intersection has been facing congestion during peak hours with long queues of vehicles waiting in each approach of this intersection for a considerable period of time while the vehicles speed up on segments between intersections. Often times at peak hours, the queue of vehicles also influence the operation of upstream intersections with side roads resulting in a considerable traffic queues in the minor side roads as well. It seems no attention or consideration has been given in the proper adequate geometric improvement and operational management of the intersection so that an acceptable level of service is maintained at the intersection during the peak hours of traffic. 1.3

Research Questions

Based on the stated problem, the following research questions will be considered for this study: a) What are the capacity, operational performance, and LoS of the Old Baneshwor intersection for the vehicular traffic under the existing conditions? b) What will be the capacity, operationalperformance, and LoS of the intersection under the future traffic with various probable improvement options? 1.4

Research Objectives

The general objective of the study is to analyse operational performance and LoS at the Old Baneshwor intersection in Kathmandu. The specific objectives of the study are: 2

a) To determine thecapacity and various operational performance measures including LoS of the Old Baneshwor intersection for the vehicular traffic under the existing conditions. b) To determine the capacity and various operational performance measures including LoS of the intersection under the future traffic with various probable improvement options. 1.5

Significance of the Study

This research may be helpful for the concerned agencies for making proper decision on whether the traffic problem at the Old Baneshwor intersection is in severe condition and when and how to improve this intersection to address that problem. 1.6

Scope and Limitation of the Study

1.6.1 Scope of the Study Various operational performance measures of Old Baneshworintersection for the vehicular traffic such as Capacity, Volume-to-Capacity ratio, Delay, queue storage ratio, and LOS under the prevailing condition and under the various improvement options with future projected traffic conditions are determined using SIDRA intersection v5.1 software. By means of this study, existing deficiencies in the intersection will also be identified and intersection improvement measures will be proposed to enhance the level of service of the intersection to an acceptable level. 1.6.2 Limitation of the Study The analysis for the performance measures of the intersection for the pedestrians, bicycles, and transit modes (i.e., travel modes in which vehicles such as buses, streetcars, and streetrunning light rail stop at regular intervals along the roadway to pick up and drop off passengers)are not performed in this study. Some local design parameters required to be inputted in SIDRA 5.1 for the intersection analysis are not available and therefore, the default values adopted as per HCM 2010 and SIDRA intersection program are assumed. The corresponding result will therefore be slightly different from reality but this discrepancy is ignored. Operation of one intersection affects the operation of another intersection especially when they are in close proximity to each other. This study however ignores the impact of adjacent minor intersections on the operational performance of the subject intersection. This study conducts only the static analysis procedures as per HCM2010 and SIDRA intersection computer application which predict average operating conditions over a fixed time period and do not deal with transitions in operation from one system state to another. This study does not use traffic simulation tools which are effective in evaluating the dynamic evolution of traffic congestion problems on transportation systems and can model the variability in driver/vehicle characteristics. 3

Chapter 2 LITERATURE REVIEW 2.1

Performance Measures

The US Highway Capacity Manual (HCM) describes that an intersection’s performance is described by the use of one or more quantitative measures that characterize some aspect of the service provided to a specific road user group. Performance measures from the perspective of motorists include automobile volume-to-capacity ratio (v/c ratio), automobile delay, and queue storage ratio (the maximum back of queue as a proportion of the available storage on the subject lane or link). Level of Service (LOS) is also considered a performance measure. It is useful for describing intersection performance to elected officials, policy makers, administrators, and the public. LOS is based on one or more of the performance measures such as volume-to-capacity ratio, automobile delay, or queue storage ratio. These measures serve as clues for identifying the source of problems and provide insight into the development of effective improvement strategies. HCM encourages the analyst to consider the full range of these measures. 2.2

Road Capacity

TRB (2010) defines capacity in the HCM as the maximum sustainable hourly flow rate at which persons or vehicles can be expected to reasonably traverse a point or a uniform section of a lane or roadway during a given time period under prevailing roadway, environmental, traffic, and control conditions. Reasonable expectancy is the basis for defining capacity. The stated capacity for a given system elements is a flow rate that can be achieved repeatedly for peak periods of sufficient demand, as opposed to being the maximum flow rate that might ever be observed. System elements that have different prevailing conditions will have different capacities, and the maximum flow rate observed on a given system element may vary from day to day. The capacity of an urban street is related primarily to the signal timing and the geometric characteristics of the facility as well as to the composition of traffic on the facility. Geometrics are a fixed characteristic of a facility. Thus, while traffic composition may vary somewhat over time, the capacity of a facility is generally a stable value that can be significantly improved only by initiating geometric improvements(TRB, 2010). 2.3

Signalized Intersection Capacity

In HCM, analysis for capacity and LOS for each lane group is performed rather than for the entire intersection. A lane group is an individual or set of movements allocated to an approach lane of a signalized intersection and is assigned a phase of the signal cycle. As defined in HCM, the lane group capacity is the maximum hourly rate at which vehicles can reasonably be expected to pass through the intersection under prevailing traffic, roadway, and signalization conditions. The capacity of a given lane group is stated as in the following equation:

4

Where = capacity of lane group i (veh/h), = saturation flow rate

for

lane

group

i

(veh/h),

and

= effective green ratio for lane group (ratio of effective green to cycle length) 2.4

Level of Service (LOS)

Automobile LOS for an intersection is a qualitative measure developed by the transportation professionals to quantify driver perception for such elements as travel time, number of stops, total amount of stopped delay, and impediments caused by other vehicles (Camp Dresser & McKee Inc., 2010). It provides a scale that is intended to match the perception by motorists of the operation of the intersection. LOS provides a means for identifying intersections that are experiencing operational difficulties as well as providing a scale to compare intersections with each other. The LOS scale represents the full range of operating conditions. The scale ranges from LOS “A” which indicates little vehicle delay, if any, to LOS “F” which indicates substantial vehicle delay and traffic congestion. The LOS analysis is conducted according to the procedures outlined in the HCM. HCM 2010 describes automobile LOS for entire signalized intersection or an approach in terms of control delay per vehicle (typically over a 15 minute analysis period). Control delay for a signalized intersection is the delay caused by the signal. The LOS of a lane group however is based on both control delay and volume-to-capacity ratio. The delay experienced by a motorist is made up of a number of delays related to the traffic control, geometrics, traffic, and incidents. Specifically, Delay is a complex measure and depends on a number of variables, including the quality of progression, the cycle length, the green ratio, and the v/c ratio for the lane group. The volume-to-capacity ratio quantifies the degree to which a phase’s capacity is utilized by a lane group. Table 2.1 lists the Delay & v/c (HCM 2010) method for Level of Service definitions based on delay and v/c ratio for vehicles. Table 2.1: Delay & v/c (HCM 2010) method for Level of Service definitions based on delay and v/c ratio for vehicles Average delay per vehicle in seconds (d) 

Level of Service for v/c ≤ 1.0 

Signals 

A  B  C  D  E  F 

d ≤ 10  10 < d ≤ 20  20 < d ≤ 35  35 < d ≤ 55  55 < d ≤ 80  80 < d 

"SIDRA Roundabout LOS" option d ≤ 10 10 < d ≤ 20 20 < d ≤ 35 35 < d ≤ 50 50 < d ≤ 70 70 < d

Sign Control (HCM 2010 default for roundabouts) d ≤ 10 10 < d ≤ 15 15 < d ≤ 25 25 < d ≤ 35 35 < d ≤ 50 50 < d

Level of Service for v/c > 1.0  All intersection types  F  F  F  F  F  F 

(Akcelik & Associates Pty Ltd, 2012) The HCM2010 method requires the analysis of both capacity and LOS conditions to fully evaluate the operation of a signalized intersection. 5

2.5

Beyond LOS F

The HCM does not subdivide LOS F, but several measures are available to describe the severity of a LOS F condition. The HCM uses LOS F to define operations that have either broken down (i.e., demand exceeds capacity) or have exceeded a specified service measure value (or combination of service measure values) that most users would consider unsatisfactory. However, particularly for planning applications where different alternatives may be compared, analysts may be interested in knowing just how bad the LOS F condition is. Several measures are available to describe individually, or in combination, the severity of a LOS F condition (TRB, 2010): • • •

Demand‐to‐capacity ratios describe the extent to which capacity is exceeded during the analysis period (e.g., by 1%, 15%, etc.). Duration of LOS F describes how long the condition persists (e.g., 15 min, 1 h, 3 h). Spatial extent measures describe the areas affected by LOS F conditions. These include measures such as the back of queue (queue length) and the identification of the specific intersection approaches or system elements experiencing LOS F conditions.

2.6

Signalized Intersection Flow Characteristics

A traffic signal essentially allocates time among conflicting traffic movements that seek to use the same space. The way in which time is allocated significantly affects the operation and the capacity of the intersection and its approaches. Signal time design at an isolated intersection involves determination of the optimal cycle length and length of the various phases to complete a cycle. The optimal cycle length at an isolated intersection is traditionally the cycle length that minimizes the delay at the intersection. The formula for calculating the minimum cycle length in HCM 2010 is given as follows. ∑ Where, =minimum cycle length (sec) = critical v/c ratio for the intersection where ‘v’ is the flow at the lane group in vehicle per hour and ‘c’ is the capacity of the lane group in vehicle per hour = Cycle lost time (sec) ∑ = sum of the ratios of the actual flows to the saturated flows for critical lane group 2.7

Demand Flow Rate

HCM defines the demand flow rate for an intersection traffic movement as the count of vehicles arriving at the intersection during the analysis period divided by the analysis period. It is expressed as an hourly flow rate but may represent an analysis period shorter than 1 h. Demand flow rate represents the flow rate of vehicles arriving at the intersection. When measured in the field, this flow rate is based on a traffic count taken upstream of the 6

queue associated with the subject intersection. This distinction is important for counts during congested periods because the stop line count of vehicles departing from a congested approach will produce a demand flow rate that is lower than the true rate(TRB, 2010). 2.8

Saturation Flow Rate at Signalized Intersection

As described in HCM, saturation flow rate for a lane group is the maximum hourly flow from the lane group that can pass the intersection if given a perpetual green phase. It is determined on the basis of the minimum headway that the lane group can sustain across the stop line as the vehicles depart the intersection. Or, it is the maximum departure (queue discharge) flow rate achieved during the green period of traffic signals (Akcelik & Associates Pty Ltd, 2012). It is usually achieved after about 10 to 14 s of green, which corresponds to the front axle of the fourth to sixth passenger car crossing the stop line after the beginning of green (TRB, 2010). The most significant parameter that influences the design of signalized intersection and its signal plan is the “saturation flow”. Saturation flow is a key factor determining the capacity and level of Service (LOS) of a signalized intersection. If the saturation flow rate can be computed to the reasonable accuracy, the capacity of the signalized intersection can be evaluated (Chand et al., n.d.). Saturation flow rate is computed for each of the lane groups established for the analysis. If a default value is selected for base saturation flow rate, it must be adjusted for a variety of factors that reflect geometric, traffic and environmental conditions that prevails at the intersection(TRB, 2010). Alternatively, the actual saturation flow rate (i.e. prevailing saturation flow rate) reflecting the effects of existing geometric, traffic, environment conditions, and driver behaviour can be measured directly in the field. The computed adjusted saturation flow rate represents an estimate of the prevailing saturation flow rate. Any potential bias in the estimate is minimized by local calibration of the default base saturation flow rate(TRB, 2010). This is important in order to avoid double counting that may lead to significant overestimation or underestimation of saturation units.

7

(Akcelik & Associates Pty Ltd, 2012 p. 4-180) Figure 2.1: Saturation flow and the related signal timing parameters Table 2.2: Default Basic Saturation flows in through car units (tcu) per hour Environment Class  (Area type)  1 (Ideal) 

2 (Average to Poor) 

Default Basic Saturation flow, sb (tcu/h)  SIDRA HCM intersection  versions

Definition  Near ideal conditions for free movement of vehicles on both approach and exit sides indicated by good intersection geometry, long distances to upstream and downstream intersections, good visibility, small numbers of pedestrians, and little interference due to loading and unloading of goods vehicles, buses or parking turnover.  Average to poor conditions indicated by adequate to poor intersection geometry, usually closely-spaced intersection environment, possibly poor visibility, moderate to large numbers of pedestrians, and interference from standing vehicles, loading and unloading of goods vehicles, buses, parking turnover, and vehicles entering and leaving premises.

1950 

1900 

1800 

1750 

(Akcelik & Associates Pty Ltd, 2012 p. Part 4-184) Various Studies on Saturation flow at signalized intersections in mixed traffic conditions Tiwari, et al. (2011) studied the applicability of U.S. Highway Capacity Manual (USHCM) signalized intersection model for an Indian signalized intersection. His evaluation revealed that the model suffers from serious gap when applied to the Indian context, where 8

signalized intersections experience heavy and non-homogeneous traffic flow so that prediction of capacity is more sensitive to the vehicle mix than in Western countries where the passenger car group largely dominates traffic composition. Measures were proposed for the modification of the model, and factors were developed based on proposed modifications in order to make it more suitable for Indian conditions. The factors evaluated were the lane width adjustment factor and passenger car equivalency factors. Mean right-turn width used is 3.6 m while the width provided was 3.3m. Equivalency factors for motorized two-wheelers ranged from 0.4 to 1.3 and for motorized threewheelers from 5.6 to 9.1. Anusha, et al. (2012) studied the effects of Two-Wheelers on Saturation Flow and hence on the capacity of the signalized intersections in developing countries. They recognized that two-wheelers (TW) constitute a major proportion of urban traffic in developing countries and therefore their effect on the saturation flow at signalized intersections could be substantial. They attempted to study and analyse the effect of two-wheelers on the saturation flow of signalized intersections by collecting data at a few signalized intersections in Bangalore, India. A strong correlation was observed between the field measured saturation flow and the proportion of two-wheeler traffic, suggesting that twowheelers have significant impact and should be considered in the capacity analysis of signalized intersections. It was concluded that while intersection capacity varied directly with the increase in the volume of two-wheelers, it was inversely proportional to the increase in volume of all other categories of vehicles. The saturation flow estimated using the calibrated US-HCM 2000 model by incorporating the adjustment factor for twowheelers was closer to field measured values, which implied that the effects of twowheelers and approach volume are to be considered while modelling saturation flow in Indian conditions. Chand, et al. (n.d) carried out a study on saturation flow rate conducted at signalized intersections with mixed traffic condition in Delhi, India as part of research study for development of Indian Highway Capacity Manual (Indo-HCM) sponsored by Council of Scientific and Industrial Research (CSIR), India. In their analysis, it was reported that the design, the capacity, and operation of a signalized intersection critically depend on passenger car unit (PCU) and saturation flow. Operation and performance of signalized intersections is influenced by the roadway parameters, traffic condition, operating parameters and environmental conditions along with user’s behavioural characteristics, which significantly differ among locations. These factors have been traditionally measured, in most of the western countries, based on the research carried on test tracks and on public roads where traffic is typically car-dominated with vehicles moving in clearly defined lanes. The intersections on urban roads in India cater to heterogeneous motorized traffic along with slow-moving traffic including pedestrians. It was therefore necessary to consider passenger car unit (PCU) and saturation flow for mixed traffic conditions to evaluate the overall operation of signalized intersections (Chand et al., n.d.). In the study (Chand et al., n.d.), attempt was made to measure saturation flow in the field by actually measuring the flow at the stop line during saturated green phase and to study the impact of various influencing parameters such as road widths, traffic composition etc. based on actual field studies/experiments of the typical Indian traffic conditions. Models were developed for estimation of saturation flow for different approach widths and different percentage of two wheelers and cars at signalized intersections for non-lane based mixed traffic conditions of Delhi and Noida. The study further compared the results of saturation flow as obtained from the derived model and actual field saturation flow 9

obtained using field estimated PCU values with that obtained using the U.K model and PCU factors as per IRC-SP-41. It was found from the analysis that the derived model gives better results. 2.9

Traffic Signal Controller Characteristics

For a given lane group at a signalized intersection, three signal indications are displayed: green, yellow, and red. The red indication may include a short period during which all indications are red, referred to as an all-red interval, which with the yellow indication forms the change and clearance interval between two green phases(TRB, 2010). The signal cycle for a given lane group has two simplified components: effective green and effective red. Effective green time is the time that may be used by vehicles on the subject lane group at the saturation flow rate and is given by the following formula.

Where, = Effective green time for phase i (sec); = Green time for phase i (sec); = yellow time for phase i (sec); and = total lost time for phase i (sec) Effective red time is defined as the cycle length minus the effective green time Modern traffic signals allocate time in a variety of ways, from the simplest two-phase pretimed mode to the most complex multiphase actuated mode. There are three types of traffic signal controllers as described in HCM-2010: •

Pre-timed, in which a sequence of phases is displayed in repetitive order. Each phase has a fixed green time and change and clearance interval that are repeated in each cycle to produce a constant cycle length.

•

Fully actuated, in which the timing on all of the approaches to an intersection is influenced by vehicle detectors. Each phase is subject to a minimum and maximum green time, and some phases may be skipped if no demand is detected. The cycle length for fully actuated control varies from cycle to cycle.

•

Semi-actuated, in which some approaches (typically on the minor street) have detectors and some of the approaches (typically on the major street) have no detectors.

2.10 Concepts of Delay at Signalized Intersection Delay to a vehicle is the difference between interrupted and uninterrupted travel times through the intersection as seen in figure 2.2, which shows the delay experienced by a through vehicle stopping and starting at traffic signals (time-distance and speed-time diagrams representing the acceleration and deceleration manoeuvres of the vehicle are shown). The average delay predicted by SIDRA intersection is for all vehicles, queued and unqueued. Based on this definition, the total (aggregate) delay (vehicle-hours per hour) is the product of average delay and the total demand flow rate.

10

(Akcelik & Associates Pty Ltd, 2012 p. 4-33) Figure 2.2: Definition of delay experienced by a vehicle stopping at traffic signals

(Akcelik & Associates Pty Ltd, 2012 p. 4-34) Figure 2.3: Delays experienced by vehicles in oversaturated conditions SIDRA intersection delay is the average delay to vehicles arriving during a given flow period including the delay experienced after the end of the flow period which is possible 11

under oversaturated traffic conditions. This corresponds to the path-trace (instrumented car) method of measuring delays. An alternative delay measurement method is the queuesampling method which involves counting the number of vehicles in the queue at regular intervals, e.g. every 10 to 20 seconds. As described in SIDRA INTERSECTION user guide, delays obtained using the path-trace method agree with the queue sampling method of measurement for low to medium degrees of saturation (v/c ratios), but the difference between the two methods is significant for oversaturated conditions (degree of saturation > 1). Figure 2.3 shows the delays experienced by individual vehicles (horizontal lines) and the queue counts (vertical lines) for a deterministic oversaturation model. The delay experienced by the last vehicle departing during the current flow period, which arrives at point C (time T1) and departs at point E (time Tf) is d1. The delay experienced by the last vehicle arriving during the current flow period, which arrives at point C (time Tf) and departs at point D (time T2) is d2(Akcelik & Associates Pty Ltd, 2012). In figure 2.3, the total delay for vehicles arriving during the current flow period (duration Tf) is represented by the triangular area ACD. This includes the total delay experienced after the current flow period (area CDE). End of oversaturation is at point F (achieved due to a lower arrival rate after the current flow period). As described in SIDRA intersection user guide (Akcelik & Associates Pty Ltd, 2012), the following are useful delay definitions, which are presented with the help of figure 2.4 which depicts a vehicle turning left at an intersection where the approach and exit cruise speeds are the same (vac = vec), and the approach and exit negotiation speeds are the same (van = ven): Intersection control delay (dic): This is sum of stop-line and geometric delays (dic = dSL +dig), thus it includes all deceleration and acceleration delays experienced in negotiating the intersection. This is same as the overall delay with geometric delay. Stop-line delay (dSL): This is calculated by projecting the time-distance trajectory of a queued vehicle from the approach and exit negotiation speeds to the stop line (or give-way / yield line), which is shown as the time from C to F infigure 2.4. The stop-line delay is equivalent to queuing delay plus main stop-start delay, and is represented by the first two terms of the delay model (dSL = dq + dn = d1 + d2).

12

(Akcelik & Associates Pty Ltd, 2012 p. 4-35) Figure 2.4: Definition of control delay, geometric delay, stop-line delay, and stopped delay experienced by a turning vehicle at an intersection Geometric delay (dig): This is the delay experienced by a vehicle going through (negotiating) the intersection in the absence of any other vehicles, which is of particular interest for satisfactory modelling of the performance of roundabouts and sign controlled intersections. Intersection geometric delay is due to a deceleration from the approach cruise speed down to an approach negotiation speed (vac→van), travel at that speed (van), acceleration to an exit negotiation speed (van→ven), travel the rest of exit negotiation distance at constant exit negotiation speed (ven) and then acceleration to the exit cruise speed (ven→vec). Thus, this delay includes the effects of the physical (geometric) characteristics of the intersection (negotiation radius and distance, and the associated speeds), as well as the effects of basic control features (e.g. a stop sign vs. a give-way / yield sign). Queuing delay (dq): This is part of the stop-line delay that includes stopped delay and queue move-up delay but does not include the main stop-start delay (dq = ds + dqm = dSL dn). The queue move-up delay is not shown in the example given infigure 2.4. Stopped delay (di or ds): This is the stopped (idling) time at near-zero speed. It is the delay excluding all deceleration and acceleration delays (i.e. not including any geometric, stop13

start and queue move-up delays), thus it is equivalent to queuing delay less queue move-up delay (ds = dq - dqm). Queue move-up delay (dqm): This is the delay associated with queue move-ups, i.e. acceleration from zero speed to queue move-up speed and deceleration to zero speed. Main stop-start delay (dn): This is associated with deceleration from the approach negotiation speed to zero speed and acceleration back to the exit negotiation speed 2.10 Peak hour factor (PHF) The hourly volume during the maximum-volume hour of the day divided by the peak 15min flow rate within the peak hour; a measure of traffic demand fluctuation within the peak hour.

If 15-min periods are used, the PHF may be computed by 4 Where, = = =

Peak Hour Factor Hourly volume (veh/h), and volume during the peak 15 min of the peak hour (veh/15 min).

2.11 SIDRA intersection Software The Signalized and Unsignalized Intersection Design and Research Aid (SIDRA intersection) software is an intersection based program originally developed by the Australian Road Research Board (ARRB) as an aid for capacity, timing and performance analysis of isolated intersections (Akcelik & Associates Pty Ltd, 2012). Since 2000, Akcelik and Associates Pty Ltd has been managing and selling this application software worldwide and continuously improving it through their individual research. SIDRA intersection Software is an advanced micro-analytical traffic evaluation tool that employs lane-by-lane and vehicle drive-cycle models coupled with an iterative approximation method to provide estimates of capacity and performance statistics (delay, queue length, stop rate, etc.). SIDRA intersection traffic models can be calibrated for local conditions. According toAkcelik and Associates Pty Ltd (2012), SIDRA intersection Software can be used to: •

•

Analyse different types of signalized controllers (fixed-time / pre-timed and actuated), signalized pedestrian crossings, single point interchanges (signalized), roundabouts, roundabout metering, two-way stop sign control, all-way stop sign control, and giveway / yield sign-control; Generate estimates of capacity and performance characteristics such as delay, queue length, stop rate as well as operating cost, fuel consumption and pollutant emissions for all intersection types; 14

• • • • • • • • • • •

Analyse many design alternatives to optimize the intersection geometry, signal phasing and timings specifying different strategies for optimization; Handle intersections with up to 8 legs, each with one-way or two-way traffic, one-lane or multi-lane approaches, and short lanes, continuous lanes and turn bans as relevant; Determine signal timings (fixed-time / pretimed and actuated) for any intersection geometry allowing for simple as well as complex phasing arrangements; Carry out design life analysis to assess impact of traffic growth; Carry out a parameter sensitivity analysis for calibration, optimization, evaluation and geometric design purposes; Design intersection geometry including lane use arrangements taking advantage of the unique lane-by-lane analysis method of SIDRA intersection; Design short lane lengths (turn bays, lanes with parking upstream, and loss of a lane at the exit side); Analyse effects of heavy vehicles on intersection performance; Analyse complicated cases of shared lanes and opposed turns (e.g. permissive and protected phases, slip lanes, turns on red); Analyse oversaturated conditions making use of the time-dependent delay, queue length and stop rate models used in SIDRA intersection; Carry out sensitivity analyses to evaluate the impact of changes on parameters representing intersection geometry and driver behaviour.

2.12 Model Calibration in SIDRA intersection Important model parameters need to be identified for calibrating SIDRA intersection to reflect local road and driver characteristics and particular intersection conditions (Akcelik & Associates Pty Ltd, 2012). Capacity and performance characteristics (delay, queue length, stops, etc.) of a traffic facility are influenced by both the intersection geometry and driver behaviour. To a great extent, all input parameters (and other parameters that are not available as input parameters but are accessible as default parameters) related to intersection geometry and driver behaviour are therefore important for calibrating the SIDRA intersection traffic model to represent particular intersection conditions. As described by Akcelik and Associates Pty Ltd (2012) in SIDRA intersection user guide, for practical purposes, the most important parameters for calibrating SIDRA intersection capacity and performance models are: i. ii.

saturation flow rate for signalised intersections, and gap-acceptance parameters (especially follow-up headway and critical gap) for roundabouts and other Unsignalized intersections.

SIDRA intersection provides various tools to help the user in model calibration effort (Akcelik & Associates Pty Ltd, 2012). These include: • • • •

the sensitivity analysis facility for all intersection (Site) types, specific roundabout calibration parameters, lane utilisation factor, and various other facilities including the heavy vehicle equivalent for gap acceptance parameter for all intersection (Site) types.

15

2.13 Review of relevant previous theses and study reports on intersection evaluation. 2.13.1 Comparison of Intersection Capacity with Traffic flow in Kabul Metropolitan Area for 2008, 2014, and 2025 Quadratullah & Maruyama (2015) conducted a study of a congested intersection located in the central business district of Kabul, which was unequipped with traffic signals and controlled by traffic police officer standing at the middle of the intersection by using traffic stop rod. The purpose of the study was to estimate the capacity of the intersection and compare it with the traffic flow (demand) at the peak hour in order to find out the congestion rates in 2008, 2014, and 2025. In this study, data collected during person trip (PT) survey by Japan International Cooperation Agency (JICA) in 2008 had been used for the estimation of traffic volume on the intersection in 2008 and forecasted to 2025. Meanwhile, traffic counts (TC) and geometric surveys were conducted by the researcher in the study intersection in 2014 and the observed data had been used to estimate the capacity and the existing traffic demand volume on the intersection in 2014. "Four steps modelling of travel demand" and intersection capacity analysis" were used to estimate the traffic demand volume and capacity of the intersection respectively. The data collected during traffic count (TC) and geometric surveys had been used to analyse the saturation flow, capacity, degree of saturation, and signals timing of intersection. The method used in this study followed the design and planning procedure presented in chapter 16 of the Highway Capacity Manual (HCM 2000). The saturation flow rates were estimated for each of four legs of the intersection with the following equation by using the base condition and traffic data:

The capacity of the intersection was estimated for each approach using the signalized intersection influence factors: . Since the green and red phases for the intersection were not constants due to the intersection controlled by traffic police, the average of the green phase length and total cycle length were used to analyse the capacity. 2.13.2 Detailed traffic study and design for grade separated intersections at five major junctions in Kathmandu In 2011, DoR/GoN had awarded a contract for consulting services to the joint venture of Soil Test (P) Ltd. and AVIYAAN Consulting (P) Ltd., Kathmandu to conduct a detailed traffic study, engineering survey, soil exploration, and design for grade separated intersections at five major junctions in Kathmandu in order to improve level of service (LOS) for both vehicular and pedestrian traffic in the entire network of intersections along the arterial axes in Kathmandu valley and ultimately to contribute significantly towards mitigating traffic congestion and traffic safety in the entire city. The proposed junctions to be designed as grade separated intersection were: • •

Old BaneshworChowk New BaneshworChowk 16

• • •

Thapathali Chowk Tripureshwor Chowk, and Kalimati Chowk

The main scope of the survey and design for each of the selected junctions were a) Detailed traffic survey and analyses for intersection capacity, peak traffic volume, traffic pattern and peak hour average speed, congestion, non-traffic encroachments and viability of grade separation b) Detailed engineering surveys (topographical, hydrological, seismological and geotechnical) of the selected intersections for subsequent design works of grade separated intersections c) Preparation of basic design and configuration of grade separation (Conceptual Design) d) Detailed engineering design and drawings for the grade separated intersection at the selected junctions including grade separated vehicle crossings, traffic safety system, pedestrian crossings, public transport/pedestrian facilities, etc. A v/c ratio of 0.95 had been used for the peak hour operation analysis of the design year (2031, 20 years design period). On the basis of the base year traffic counting survey and well founded traffic forecasting for the design year, the intersection performance had been analysed for the following criteria to come up primarily with basic configuration and support detailed engineering design for a grade separated intersection for each junction. The intersection performance had been analysed with at least the following criteria, including the pedestrian and non-motorized traffic: Peak hourly volume (veh/h), Intersection LoS (A to F), Average intersection delay (sec), Degree of saturation (Highest), Theoretical capacity of roads, Total vehicle delay (veh-h/h), Queue length (m or veh). The steps followed for traffic projection were: • • • •

The base year peak directional traffic volumes taken were the seven days’ averages for the year 2011, which were determined with the analysis of long traffic video data acquired for the project. The assumed growth rates were dynamic and averaged for five years’ blocks. Total projected traffic was further treated with exclusive diversion assumptions made for each direction. Traffic at- and separated- grades were quantified as different entities but possible relationships between them were also inserted.

The methodology for evaluating the threshold at grade followed a three step process: • • •

Estimate peak hour movement volumes on the major and minor legs for the design year Estimate maximum average daily intersection entering volumes for intersection types Evaluate intersection LOS and iterate for thresholds for different options

17

(Soil Test-AVIYAAN Consulting (P) Ltd. JV, 2011) Figure 2.5: Maximum daily entering volume thresholds for primary and secondary roads at-grade In this study, primarily SIDRA Intersection Version 5.1 had been used for alternative intersection designs in terms of capacity, level of service and a wide range of performance measures including delay, queue length, and stops for vehicles and pedestrians. The iterative process of detailed engineering design for selected solution of grade separation for each proposed option had been carried out in conjunction with micro simulation of traffic for various criteria as listed above. Traffic models for different configurations had been analysed for the various intersection options in the Concept Design for each intersection. The analyses primarily focused on the signalized traffic, which was mostly at grade. In the traffic study of the Old Baneshwor Intersection, the traffic problems were identified with a new traffic study more reliably and comprehensively with seven days long traffic video. The second-by-second data captured had been processed for traffic movements (12 movement directions, 13 vehicle types including non-motorized vehicles and 5 directions of pedestrian movements). The analysis of traffic situation had been carried out in terms of traffic movement, intersection performance, and driving behaviour/enforcement issues. The traffic study conducted for the Old Baneshwor intersection in this project on August, 2011 showed that about 58,000 vehicles crossed the intersection per day. In daily traffic flow, about 70% of vehicles were motorized two wheelers (motorcycles and scooters), 25% light vehicles ( cars, four wheel drives, utility vehicles, micro buses and three wheelers), 1% heavy vehicles (buses and trucks) and about 4% cycles. This showed that the traffic crossing the intersection was mostly private passenger vehicles. In Old Baneshwor intersection, high traffic flow was observed continuously for ten hours in a weekday between 9 AM and 7 PM, averaging at about 3800 veh/h. The peak flow was 3007 PCU/h (4552 veh/h) which was about 8% of the daily traffic flow. By vehicle group, the peak hour flow was different from the daily flow. Shares were higher for motorized two wheelers in the peak hour and shares of heavy vehicles and cycles were less (in PCU). 18

This was particularly due to high flow of utility vehicles, trucks and cycles during early morning that enter the intersection. 2.13.3 Kathmandu Sustainable Urban Transport Project (KSUTP) Since 2012, the Ministry of Physical Infrastructure and Transport (MoPIT) under GoN, have been executing the Kathmandu Sustainable Urban Transport Project (KSUTP) with the loan assistance of Asian Development Bank (ADB) in order to improve traffic circulation within the central area through intersection improvement including signalization at strategic locations, access improvement and improved urban transport system. The consultant in this project were SMEC International Pty, Ltd. Australia in Association with Brisbane City Enterprise Pty., Australia Transportation Planning (International) Ltd., UK, GEOCE Consultants (P) Ltd., Nepal. This project’s area is mainly focused in the central area of Kathmandu bounded within Bishnumati River to the west, Bagmati River to the south, Dhobi Khola River to the east and north boundary of the Narayanhiti Royal Palace to the north. The main four corridors on which traffic analysis was conducted are: Pushpalal Path, Kanti Path, Durbar Marg, and Ramshah Path. A total of 36 intersections are included under KSTUP along these four road corridors. However, detailed traffic analysis was carried out only for 22 numbers of key intersections. Old Baneshwor intersection did not lie in the corridors studied by the project. The project has acknowledged that full operational analysis involves assessment of capacity both along the midblock segments (link) and at intersections (nodes). Analysis of intersections was undertaken in greater detail than of the mid-block sections because capacities of the project road corridors were largely determined by the capacities of intersections. The study under the project found out that mid-blocks generally had spare capacity compared to intersections. The approach taken for operational analysis was a five stage process as follows: Stage 1: Issue Identification; Stage 2: Identification of Potential Solutions; Stage 3: Assessment of Potential Solutions; Stage 4: Development of Preferred Solutions; and Stage 5: Analysis of Preferred/Recommended Solution Stage 5 was about the capacity analysis of the preferred/developed solution. The intention was to demonstrate that the proposed solutions/configurations of intersection would work better than the existing configuration (although not necessarily resulting in desired level of service). As explained in the study, traffic growth rate averaged over the network is not useful for the purpose of estimating future traffic for intersections. Growth rate for each link and movement can be different and may vary over time and space. Information on growth rate at this level of disaggregation is not available for roads in Kathmandu. In such situations, scenario analysis with varying growth rate will be useful to see what would happen in each case of traffic growth for policy analysis. The study in KSUTP found that figures on traffic growth rate for Kathmandu are found to vary across the studies. A vehicular traffic growth rate of up to 13% had been reported. Growth rate for motorcycle was reported to be even much higher. Clearly, the growth rate 19

similar to those mentioned above was not sustainable and could not continue since the growth in vehicular traffic needed to be controlled as one of the KSUTP’s key objectives. A more realistic growth rate of 2% was adopted as a matter of policy, which can be achieved by implementing walking, cycling and public transport favoured land use and urban transport policy. SIDRA intersection version 5.1 was used initially but later version 6.0 PLUS was used to carry out analysis of both signalized and un-signalized intersections in KSUTP. The results of the analysis were then used to gain an understanding on how the intersection would perform after the improvements. 2.13.4 Development of Traffic diversion algorithm for the possible reduction of Traffic demand at intersection: a case study of Thapathali intersection Acharya (2015) conducted a case study of Thapathali intersection in Kathmandu in as her M.Sc. thesis to reduce the traffic demand at Thapathali intersection by diverting certain traffic to alternate routes so that the traffic management at that intersection would be better than now. For that purpose, the traffic condition at Thapathali intersection in existing scenario and the effect of traffic diversion at the intersection were determined. Volume count data at Thapathali intersection were taken as secondary data from KSUTP funded by ADB. A base line O-D survey was carried out by using the registration number plate method in order to determine the existing traffic in the alternate diversion route. The maximum traffic flow or capacity of the alternate route was determined by using Green Shield and Greenberg models. The study demonstrated that traffic diversion analysis algorithm can act as means for demand management at neighbouring intersection that is oversaturated. SIDRA intersection version 5.1 was used for the analysis of the intersection before and after the traffic diversion at the intersection. 2.13.5 Comparison of Probable Congestion Reduction Approaches at New Baneshwor Intersection in Kathmandu Sharma (2016) in his Master’s thesis conducted a study of the travel time and delay at New Baneshwor intersection in Kathmandu to provide clear view of best approaches by comparing the present condition with the different probable approaches which can help to improve the intersection performance. This study focused on four techniques that included application of pedestrian bridge (Alternative first), application of indirect U-turn through the median (Alternative second), application of U-turn from below the existing bridges at Bagmati river and Dhobi Khola (Alternative third) and application of flyover (Alternative fourth). VISSIM simulation software was used for the modelling of heterogeneous traffic condition prevailing in Kathmandu. Model was calibrated for local conditions using traffic volume and vehicle speed. Validation of the model was done by comparing the output of travel time and number of vehicles in the queue with the field data. After validation of the model, the travel time and vehicle delay were calculated for all alternatives.

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Chapter 3 RESEARCH METHODOLOGY 3.1

Research Design

This researchisa fieldresearch for the analysis of an isolated intersection to evaluate the operational performance for vehicular traffic under the existing conditions and under the future conditions. Field measurements, observations, and videography method of survey were used to collect the primary data. Past survey data, literatures, codes, reports, and manuals were used for secondary data. Traffic characteristics data such as traffic volume, prevailing saturation flow, phasing and signal timing (operated by traffic police) for the analysis under existing conditions were collected during the morning and evening peak periods for three days (Tuesday, Wednesday, and Thursday), when the traffic is maximum. 'Traffic Count' software developed by Softwel P. ltd. was used for playing the video footage and recording the volume count data. The data was organized in MS Excel. SIDRA intersection version 5.1 was used in evaluation of the intersection performance. Problem Identification Setting Research Questions and Objectives Selection of Study Area

Data Collection& Organization

Step-II Primary Data

Secondary Data

Geometric Design Traffic Characteristics Signal Control data based on Observation of Police Control

Default values Past traffic survey data & reports

Data Analysis Step-

Interpretation of Results and Discussion Conclusion and Recommendation

Figure 3.1: Flow Chart of Research Methodology

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Supervisor Consultation

Literature Review

Identification of required data

3.2

Research Approach Field Measurement

Video Recording

Secondary data

Preparation of Input Parameters for calibration of SIDRA model

Development of Intersection Traffic model in SIDRA and Run the analysis No

Calibration of saturation flow rate • Compare field measured & SIDRA estimated saturation flow rates • Calculate calibration factor • Adjust the default basic saturation flow

SIDRA estimated saturation flow matched with the observed saturation flow?

Re-run SIDRA intersection

Validation of SIDRA model Comparison of control delay & back of queue estimates from SIDRA intersection with those observed in the field

Yes

Yes

No

Is model satisfactory?

Modify appropriate parameters in SIDRA

Evaluate Capacity & Performance statistics in existing conditions under police control

Evaluate Capacity & Performance statistics in existingconditions with optimum signal timings disregarding the police control

Traffic flow projection for future year

Development of SIDRA model for various intersection improvement configurations

Evaluate Capacity & Performance Statistics under future traffic for various intersection improvement configurations

Conclusion and Recommendation

Comparison of Capacities & Performance statistics of various improved configurations

Figure 3.2: Flow chart of research approach

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3.3

Study Area

Old Baneshwor intersection in Kathmandu valley is taken as the study area in this research study. The intersection is one of the main intersections, which is located in the eastern part of the area surrounded by the existing ring road in the valley. The two main roads namely Gaushala – Old Baneshwor – New Baneshwor road and Sinamangal – Old Baneshwor – Ratna park road cross at this intersection. These urban roads, which are under the jurisdiction of Department of Roads, are among the major arterial roads linking the exiting ring road to the city core. The two intersecting roads have been recently widened under the Kathmandu Valley Road Improvement Project, Department of Roads. Currently, the four legged Old Baneshwor intersection is without traffic signals but the traffic police officer has been controlling the intersection during peak hours. The intersection legs formed are: 1. 2. 3. 4.

North-road towards Gaushala (Gausala Leg) South-road towards New Baneshwor (New Baneshwor Leg) East-road towards Sinamangal (Sinamangal Leg) West-road towards Maitidevi (Maitidevi Leg)

The east, west, and south legs have adequate width for three lanes. The width of north leg is adequatefor four lanes. The north and south legs are staggered by approximately 18 meter. During peak hours of weekdays, traffic at the intersection is observed to be congested with long queues of vehicles standing still for considerable period of time while the vehicles speed up on segments between intersections. The Location of the study intersection is shown in figure 3.3. 3.4

Sample Size and Sample Selection

As described in HCM-2010, capacity and other traffic analyses typically focus on the peak-hour traffic volume because it represents the most critical period for operations and has the highest capacity requirements. As shown by a previous study (Soil Test/AVIYAAN P. ltd, 2011), the peak traffic flow occurred during 8 am to 11 am in the morning and from 4 pm to 7 pm in the evening at old Baneshwor. So, traffic volume counts were conducted from 8:00 to 11:00 in the morning and 4:00 to 7:00 in the afternoon for three typical weekdays:Monday, May 7, Tuesday, May 8, and Wednesday, May 9, 2018 at the study intersection. The peak hour and peak 15m within the peak hour were identified after organizing the data in order to determine the actual peak hour factor. 3.5

Data Collection

There are three categories of data required for analysis: (a) geometric characteristics (b) traffic characteristics and (c) signal control characteristics. For this thesis, most of the data required is primary data. Values of some parameters like critical gap, follow up headway, growth rate, passenger car units (PCU’s), vehicle dimensions and queue space were adopted as secondary data.

23

(Internet/Google Earth, 2017) Figure 3.3: Location of Old Baneshwor Intersection

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Various relevant data required for developing SIDRA intersection model and methods employed to collect these data are explained in the following sub-headings. 3.5.1 Intersection Geometry The geometric data includes lane configuration data including lane discipline width of median, numberof approach and exit lanes in each intersection leg, width and grade of each lane, turn bay length (if any) for each movement, presence of on-street parking lane (if any), lane type of each lane such as slip lane (give way/yield, stop controlled, or continuous). These data were observed and measured in the field for developing the traffic model of the existing intersection. The grade of approach and exit lanes were found out from the detail topographic survey of the study area conducted by Soil Test/AVIYAAN J/V (2011) for the study of the grade separated intersection design. For developing the future intersection models of various improvement options, the various parameters of geometric data were proposed as per viability. 3.5.2 Traffic Volume and Pedestrian Volume Traffic volume includes turning movement counts at the stop line of the intersection and the vehicle arrival counts, i.e.,demand flow rates, at upstream of the queue at each intersection leg.Separate traffic count was taken at the upstream of the queue at each leg because the stop line count of vehicles departing from a congested approach (degree of saturation > 1) will produce a demand flow rate that is lower than the true rate. Videography survey was used to collect the volumes of traffic and pedestrians. Five CC cameras as shown in figure 3.5 were set up to capture the turningmovementsat the intersection and arrival of vehiclesupstream of the queues in individual legs. Cameera-1 was set up at a vantage point on the terrace of a building at the intersection corner to record all the turning movements at the intersection. Other four CC cameras (Camera-2 to camera-5) were set up at vantage points on the terrace of road side buildings at the four legs upstream of the queues associated with the legs in order to determine the demand flow rate of vehicles arriving at the intersection.All the five cameras recorded videos continuously in the five digital video recorders (DVR) as shown in figure 3.6for three typical days from Monday, May 7 to Wednesday, May 9, 2018. Later in office, the video footages were extracted from the each digital video recorder only for the study periods of 8:00 am – 11:00 am and 4:00 pm – 7:00 pm in each day. Total five persons were used to count the turning movements at the stop line and the arrival of vehicles upstream of the queues in each leg by playing the recorded video footages. The video footage was played with the “Traffic Count” Computer software, developed by Softwel (P) Ltd, which had the features of entering the type of vehicles manually by viewing in the video and records the video time of each vehicle to the nearest second as the vehicle type is entered in the software. Figure 3.7 shows the screen shot of the user interface of the “Traffic Count” Software. The recording of classified counts for each direction of movement was done by playing the video footage once for each direction of movement. Thus, the classified vehicle counts in the existing twelve movement directions from the four legs of the intersection and classified vehicle arrival counts upstream of the queues at the four legs were conducted for the study periods of 8:00 am – 11:00 am and 4:00 pm – 7:00 pm for three days. Vehicle movement data was exported from the “Traffic Count” software in the form shown in table 3.1. The recorded second by second data of classified counts of vehicle turning movements in each direction was reduced from this 25

table for 15 minute intervalsby using a macro program in MS-Excel developed by Softwel (P) Ltd.

Figure 3.4: A plan showing the locations of CC Camera set up

(Softwel P. Ltd., 2017) Figure 3.5: Infrared CC Camera to capture the video

26

(Softwel P. Ltd., 2017) Figure 3.6: Digital Video Recorder to record the video captured by CC Camera 

(Softwel P. Ltd., 2017) Figure 3.7: Screen shot of the user interface of “Traffic Count” Software The pedestrian volume crossing each leg was counted for only the analysis peak hour for two days after determining the peak traffic hour in order to have average estimate of pedestrian volume crossing the road at the intersection during the analysis hour. Station

Table 3.1: Form of Movement Data exported from Traffic Count Software Turning Video File Start Start Vehicle Movement Name Date Time Type

Time

(Softwel P. Ltd., 2017) 3.5.3 Passenger Car Unit (PCU) The traffic in our country is heterogeneous with many different types of vehicles taking up different amounts of road space and having different operating capabilities. It is, therefore, necessary to adopt a standard traffic unit to which other types of vehicles may be related. For geometric design of roads this standard is the Passenger Car Unit (PCU), which is that of a normal car (passenger car), light van, or pick-up. Table 3.2 shows the PCU factors used for the observed vehicles in this research.

27

Table 3.2: Passenger Car Unit (PCU) of various types of Vehicles S.N. 1 2 3 4 5 6 7 8 9

Vehicle Type Cycle Motor Cycle Car, Light van, Four Wheel Drive, SUV, Utility Vehicle, Three Wheeler Micro Bus Mini Bus Standard Bus Tractor, Light Truck Heavy Truck Multi Axle Truck

PCU Factor 0.25 0.25 1.00

Remarks As per KSUTP, 2012 As per KSUTP, 2012 As per NRS, 2013

1.25 As per KSUTP, 2012 2.50 As per KSUTP, 2012 3.00 As per NRS, 2013 1.50 As per NRS, 2013 2.50 As per KSUTP, 2012 3.50 As per KSUTP, 2012 (NRS/DoR, 2013 &KSUTP/DoR, 2012)

3.5.4 Prevailing saturation flow rate The prevailing saturation flow rate is the rate measured in the field (veh/h) for specific lane. This saturation flow will have effects of all road and traffic factors (heavy vehicles, turning vehicles, lane width, grade, local driver behaviour and so on). This field measured prevailing saturation flow rate is used for the local calibration of the default basic saturation flow in SIDRA. Any potential bias in the estimate of saturation flow rate is minimized by local calibration of the basic saturation flow rate. The saturated period count methodas described in the Overseas Road Note 11 (ORN 11) published by Transport Research Laboratory of UK was used to find out the field measured saturation flow rate of individual lanes. This method consists of measuring the flow of traffic, during the entire period of saturation. It is determined as simply the average flow for all saturated intervals (i.e. once the initial start-up period has been completed, and while the flow is still being supplied from a queue). To allow saturation to develop, a lag of ten seconds was allowed to pass between the start of go signal by police and the first vehicle to be counted. The classified counts of vehicles that departed from the stop line of the lane during saturated period were recorded for 25 cycles and the saturated time period for each cycle was noted by observing in the recorded video footage of the intersection. The observations were made during the morning and evening peak hours for individual lanes. Motorcycles were converted into PCU and added to the number of other light vehicles and heavy vehicles giving total number of vehicles that departed during saturation. The saturation flow (veh/h) was calculated by dividing the total number of vehicles in each saturated green period by its duration in hours. The average of all the saturation flow rates observed was used for calibration of basic saturation flow of individual lanes. 3.5.5 95 percentile back of queue The 95 percentile back of queue was measured in the field for the validation of the SIDRA model of the existing intersection under exiting geometric and traffic characteristics. The 95 percentile means that the queues are shorter than or equal to that value 95 % of the time 28

during a given time period. The classified counts of vehicles that stopped during the red signal by the police including those that stopped after the green time started wererecorded in the field for every cycle during the analysis hourfor individual lanes.Videography survey was not used for this survey due to additional cameras required to cover the views of the queues in all the lanes. Instead, four persons were used for the four approaches of the intersection for this survey of back of queue during the morningand evening periods for three days. The 95 percentile back of queue was calculated with the MS-Excel built in worksheet function. 3.5.6 Vehicle Composition There were various types of vehicles using the study intersection. Vehicles were defined as per identification made during the classified traffic volume survey. Various types of vehiclesidentified and their vehicle class input used for SIDRA intersection model are shown in table 3.3. Table 3.3: Vehicle Types Vehicle Class input in SIDRA Intersection 5.1

S.N.

Vehicle Type

1

Car, Four Wheel Drive, Utility Vehicle, Three Wheeler, Micro Bus

Light vehicle (LV)

2

Cycle, Motorcycle

Light vehicle (LV)

3

Mini Bus, Standard Bus, Tractor, Light Truck, Heavy Truck, Multi Axle Truck

Heavy vehicle (HV)

Motorcycles and cycle volumes cannot be input as separate class in SIDRA Intersection v 5.1. Therefore, volume of motor cycles and cycles were converted into equivalent number of passenger cars by multiplying with their PCU factor of 0.25 and then specified as normal light vehicles in the SIDRA model of the intersection. 3.5.7 Size of Vehicle and Queue Space There are so many types of vehicle size which influence traffic characteristics due to their various sizes and operating capability. Different types of vehicles used this intersection, which makes traffic heterogeneous in nature. Size of vehicle was obtained from respective website of the vehicle manufacturer. Table 3.4 shows the dimensions of various vehicles plying in Kathmandu. Table 3.4: Vehicle Dimensions S.N 1 2 3 4 5 6

Vehicle Type Bike 1 Car Cycle Tempo Pick up Micro Bus

Nos. of Model 1 5 1 1 1 1

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Length (m) 2.01 3.325-4.70 1.775 3.561 4.975 5.38

Width (m) 0.747 1.5-2.069 0.628 1.88 1.76 1.88

S.N 7 8 9 10 11 12 13 14 15

Vehicle Type Jeep Bus Mini Bus Truck Mini Truck M. A Truck Van Tanker Maruti Van

Nos. of Model 2 1 1 1 1 1 1 1 1

Length (m) 4.72-4.4891 10.854 6.615 7 6.26 8.5 5.415 6.5 3.37

Width (m) 2.075-2.13 2.60 2.20 2.351 2.115 2.612 1.975 2.084 1.41

(TATA Motors,2016 as cited in Sharma, 2016) Queue space (also called jam spacing) is the vehicle spacing i.e. the distance between the front ends of two successive vehicles in the traffic stream while queued at the intersection. Table 3.5 shows the values of average vehicle length and queue space parameters adopted for the light and heavy vehicle classes in developing the traffic model in this research. Table 3.5: Values of vehicle length and Queue space parameters adopted S.N.

Vehicle Class in SIDRA Intersection v 5.1

1

Light vehicle (LV)

3.0

3.75

2

Heavy vehicle (HV)

7.0

8.0

Average Vehicle Length (m) Average Queue Space (m)

(KSUTP/DoR, 2012) 3.5.8 Approach and Exit Cruise Speeds The cruise speed is the average uninterrupted travel speed, i.e. the speed of a vehicle without the effect of delay at the intersection. If there were posted speed limit, it would be an appropriate value to use. Since, there was not any speed limit posted in the approach roads of the study intersection, a speed survey was carried out on each leg of the intersection during off-peak periods in good weather. Manual short-base method was used for the survey of cruise speeds as described in Overseas Road Note 11: Urban Road Traffic Surveys published by Transport Research Laboratory, UK. As per this method, a short base length was created, over which vehicles could be timed. The length will depend on speeds on the road, with longer bases needed for higher speeds. Table 3.6 relates approximate lengths to average speeds. Table 3.6: Short-base lengths Average speed of traffic (km/h)

Short-base length (m)

Below 40

25

40- 65

50

Above 65

75

(TRL, 1993)

30

Another approximate guide to length is that no vehicle in the traffic stream should take less than 2 seconds to traverse the short-base, in the traffic conditions prevailing during the survey(TRL, 1993). In this study, the ends of the short-base length of 30 meter were marked on the road surface with red spray paint across the approach of each leg. The short-base was marked upstream of the intersection in each approach where there was not any influence of the intersection control. The short-base length was measured accurately with a tape-measure. In addition a “sampling line” was marked upstream of the start line. The sampling line was needed to select the sample vehicle before recording its travel time. With the use of stop watch application in mobile phone, the time taken by sample vehicles to travel from the upstream start line to the downstream end line was recorded along with the type of vehicles.More than 75 observations of speeds in approach were taken for estimating the speed within an acceptable error.The 85th percentile value was used for cruise speed, since this excludes extremely fast drivers (and gross measuring errors) and gives an estimate of what the majority of drivers consider a top limit (TRL, 1993). The exit cruise speed in each leg was assumed to be the same as the approach cruise speed. 3.5.9 Negotiation Speed, Distance, and Radius The intersection negotiation data (negotiation radius, negotiation speed, and negotiation distance) are specified for each O-D movement through the intersection in SIDRA. These parameters are important in determining the geometric delay, average speed, fuel consumption, operating cost, and emission (Akcelik & Associates Pty Ltd, 2012). The intersection negotiation data should be considered as the exit negotiation data, i.e. related to vehicle movement past the stop line as shown in figure 3.8. A number of observations of negotiation speed for different types of vehicles were made by timing the vehicles to travel the measured distance from approach stop line to exit side stop line along the typical vehicle path for each movement by playing the recorded video footage during the off-peak hours. The negotiation distance and negotiation radius were measured along a typical negotiation path of vehicles based on field survey and Ruler dialogue of the Google Earth application. Downstream distance was set to be calculated by the SIDRA program. Approach travel distance and lane lengths for each approach were also measured in Google Earth Image. 3.5.10 Phasing and Timing Data It includes the phase plan, phase times, yellow time, and all red time for each phase. At present the traffic signals are not installed at the study intersection. Traffic police controls the intersection movements during periods of high traffic volume in the morning and evening. So, the phasing and timing data assigned by the traffic police during the peak hour study period were observed for more than 15 cycles for each phase in the recorded video footage of the intersection. Fixed time (pretimed) signal analysis method with an average phase time for each phase was adopted for SIDRA input for the evaluation of performance statistics under the existing operation of the intersection. SIDRA default values of Yellow time (4 sec) and All Red time (2 sec) were adopted.

31

Phasing and timing data for the existing traffic and geometric condition were thenoptimized for signalized option with SIDRA program to find out the optimum performance measures.

(Akcelik & Associates Pty Ltd, 2012) Figure 3.8: Movement Path Data Definitions 3.5.11 Traffic Growth rate Traffic growth rate is required for developing the future model of the intersection. The study in KSUTP found that figures on traffic growth rate for Kathmandu are found to vary across the studies. A vehicular traffic growth rate of up to 13% had been reported. Growth rate for motorcycle was reported to be even much higher. Clearly, the growth rate similar to those mentioned above was not sustainable and could not continue since the growth in vehicular traffic needed to be controlled as one of the KSUTP’s key objectives. A more realistic growth rate of 2% was adopted as a matter of policy, which can be achieved by implementing walking, cycling and public transport favoured land use and urban transport policy. Based on the report on KSUTP, a uniform growth rate of 2 % per year was adopted in this research. 3.6

Processing of Data

In order to perform the analysis for the operational performance of the study intersection, collected data were organized and critically examined in order to attain the objectives.The processing comprised classification, tabulation,and developing of traffic data for the intersection so that it would be easy for input in developing the intersectionmodel. 3.7

Identification of Peak hour and Peak hour factor for the analysis

The fifteen minute interval classified vehicle counts in each movement direction at the interaction were converted to equivalent number of passenger cars by multiplying with the corresponding PCU factors for various types of observed vehicles. The total PCU’s that 32

entered the intersection from all the four legs during each fifteen minute interval in the morning and evening study periods were determined for each of the three days. Then, average of the three days was found for each fifteen minute period. By summing the successive four 15-minute period counts in PCU, three-day average hourly volumes in PCU entering the intersection from all the four legs were determined for the morning and evening periods. Thus, peak hour in each of the morning and evening periods were identified. The higher one among the two peak hours of the morning and evening was selected for the analysis and further study. The peak fifteen minute within the peak hour was identified in order to get the peak hour factor (PHF). The peak hour factor accounts for the peaking of traffic flow during the peak hour so that analysis is based on the peak fifteen minute traffic flow. 3.8

Calibration of SIDRAIntersection Model

Calibration is the process used for getting adequate reliability or validity of the model by assigning suitable values to various model parameters so that the developed model represents particular intersection conditions as closely as possible. For SIDRA models calibration should comprise checks of the input data to ensure that the base case (i.e. the latest valid data representing the existing intersection) data is adequately represented in the model. The calibration process was based on various traffic data, including surveys and site observations. This data, for traffic signals principally included traffic flow, saturation flow, traffic signal phase sequences, phase green times, traffic signal timing settings for yellow, all red and pedestrian phases, and geometric parameters.

An important parameter forcalibrating SIDRA intersection capacity and performance model is the saturation flow rate for signalized intersections (Akcelik & Associates Pty Ltd, 2012). The following recommended method was followed to calibrate the saturation flow in SIDRA intersection v. 5.1 as per the Akcelik & Associates: (i) Field measured lane saturation flow, s' (veh/h) whichhad effects of all prevailing road and traffic factors was determined as explained in aforementioned section 3.5.4 of data collection. (ii) The field measured lane saturation flow, s' was comparedwith the lane saturation flow s (veh/h) estimated by SIDRA intersection, given in Lane Flow and Capacity Information table in the Detailed Output report. If they were significantly different (given that all road and traffic factors had been specified as input to SIDRA intersection correctly), a calibration factor s'/s was calculated. (iii) The basic saturation flow (tcu/h) was then adjustedto s'b = (s'/s).sb where sb is the basic saturation flow (tcu/h) specified as input in the Approach Lane Data of Geometry dialogue, for estimating saturation flow s (veh/h). (iv) SIDRA intersectionwas re-run to estimate saturation flow using the new basic saturation flow (s'b); the process wasrepeatedif necessary. The calibration factor (s'/s) can be used for future design options if it is believed that it adjusts the SIDRA intersection default basic saturation flow for local driver behaviour adequately.

33

The Sensitivity Analysis option in the Demand & Sensitivity input dialog of SIDRA intersectionwas used by choosing the Basic Saturation Flow parameter for calibration purposes. The GEH statistic was used as an acceptance criterion in the calibration of saturation flow rate. The GEH Statistic is a formula used in traffic engineering, traffic forecasting, and traffic modelling to compare two sets of traffic volumes. The GEH formula gets its name from Geoffrey E. Havers, who invented it in the 1970s while working as a transport planner in London, England. Although its mathematical form is similar to a chi-squared test, is not a true statistical test. Rather, it is an empirical formula that has proven useful for a variety of traffic analysis purposes(WIKIPEDIA, n.d.). The formula for the “GEH Statistic” is 2 Where M is the hourly traffic volume estimated by the traffic model (or new count) and C is the real-world hourly traffic count (or the old count). GEH statistics give an indication of a goodness of fit as outlined in table 3.7. Table 3.7: GEH statistic values and its indications GEH Statistic range GEH < 5.0 5.0 ≤ GEH ≤ 10.0 GEH > 10.0

Indication A good match between the modelled and observed hourly volumes. This may warrant investigation. High probability that there is a problem with either the model or the data (this could be something as simple as a data entry error, or as complicated as a serious model calibration problem.

(WIKIPEDIA, n.d.)

3.9

Validation of base model

Validation should provide an additional check, independent of the calibration. Validation should use the model calculated values in the base case model to check that the results are representative of the observed situation. The principal values to be used should be the Degree of Saturation (DoS) and the calculated 95 percentile queue length on the approaches(DPTI, 2017). Both the DoS and 95thpercentile back of queuewere used for validation of the base case model in this study. For this purpose, back of queues (veh) were observed for more than 30 cycles in each shared lane of the four legs during the analysis period (10:00 am – 11:00 am) for two days May 8 to May 9, 2018. Then, the 95th percentile back of queue (veh) was determined for the observedlanesfrom the observed data with the use of MS-Excel worksheet function“percentile.inc(array,k)”and compared with the SIDRA generated 95th percentile back of queue (veh). To check the closeness of match between the observed and model estimated validation parameters, a measure of Root Mean Squared Normalized Error (RMSNE) was used. RMSNE measures the percentage deviation of the estimated data from observed data. A 34

RMSNE of less than 0.15 is considered acceptable for traffic model calibration and validation. Mathematically, it is expressed as: 1

,

, .

3.10 Data Analysis Analyses of survey data such as traffic count and peak hour volume were carried out in MS-excel. The analysis for the evaluation of operational performance was performed with SIDRA Intersection 5.1. The samples speed data, field measured back of queue data, field measured prevailing saturation flow data, and data of phasing and timing assigned by traffic police were analysed in MS-excel for sample mean, maximum, minimum values and percentile values. Standard error of mean of the samples was determined by using the formula recommended by Overseas Road Note 11 (Urban Road Traffic Surveys):

√

Where SEm = standard error of the mean S = standard deviation of the sample values n = number of samples (sample size)

There is a probability of 95.4 per cent that the true (population) value of the statistic lies within confidence limits of plus or minus two standard errors of the sample value (TRL, 1993). Based on this fact, 95 % confidence limits of the sample mean were estimated. The analysis of the collected information such as geometric data, traffic data, and intersection control data were organized into their respective categories as needed for the input toSIDRA intersection. After the calibration and validation of SIDRA model, itwas used to evaluate the various relevant operational performance measures of the intersection for vehicular traffic under existing conditions. Then various alternatives of improved intersection configurations were proposed and evaluated for the performance measures under the future conditions. 3.10.1 Evaluation of Operational Performance Measures After the calibration and validation of the base case model, the various operational performance measures were evaluated for the present base case under existing traffic police control and under the phasing and signal timingsoptimized by SIDRA Intersection program. Then the proposed improvement alternatives were analysed and evaluated in terms of the various performance measures. The performance measures evaluated were as follows: i. Capacity

35

Capacity is the maximum sustainable hourly flow rate that can be achieved during a specified time period under given (prevailing) intersection geometric, traffic, and control conditions. Capacity is the main determinant of performance measure. SIDRA Intersection computes the capacity of each approach lane separately and then adds the lane capacities to obtain movement capacities. ii. Degree of Saturation (DOS, v/c ratio) Degree of saturation is defined as the ratio of demand (arrival) flow to capacity (also known as volume to capacity, v/c ratio). Degrees of saturation above 1.0 represent oversaturated conditions (demand flows exceed capacity), and degrees of saturation below 1.0 represent under-saturated conditions (demand flows are below capacity).

iii. Delay Delay to a vehicle is the difference between interrupted and uninterrupted travel times through the intersection. The average delay predicted by SIDRA Intersection is for all vehicles, queued and unqueued. Based on this definition, the total (aggregate) delay (vehicle-hours per hour) is the product of average delay and the total demand flow rate.The HCM models do not include geometric delays. SIDRA Standard delay model was used to include geometric delays in the average control delay values as used for LoS determination in this research. iv. 95thpercentile Back of Queue (BOQ) The back of queue is the maximum extent of the queue that occurs once each cycle. 95th percentile back of queue (i.e. queue length) is the value below which 95 per cent of all observed cycle queue lengths fall, or 5 per cent of all observed queue lengths exceed. In the Model Settings input dialog of SIDRA Intersection, the Percentile Queue parameter was used for the percentile queue length value to be included in output reports of SIDRA. The back of queue is a more useful performance measure since it is relevant to the design of appropriate queuing space, e.g. for short lane design to avoid queue spillback into adjacent lanes, for phasing design to avoid blockage of upstream signals in paired intersection situations, and for signal coordination offset design to prevent interruption of platoons by downstream queue. v. Level of Service (LOS) Automobile LOS for an intersection is a qualitative measure developed by the transportation professionals to quantify driver perception for such elements as travel time, number of stops, total amount of stopped delay, and impediments caused by other vehicles (Camp Dresser & McKee Inc., 2010). It provides a scale that is intended to match the perception by motorists of the operation of the intersection. As specified by HCM, SIDRA Intersection uses the average control delay as the LoS measure for vehicles at signalized and unsignalized intersection.In this study, LoS Method option used was Delay & v/c (HCM 2010).

vi. Performance Index (PI) 36

Performance Index (PI) is a measure that combines several other performance statistics, and therefore can be used as a basis for choosing between various design options (the best design is the one which gives the smallest value of PI)(Akcelik & Associates Pty Ltd, 2012). The Performance Index is defined as PI = Tu + w1.D + w2.K.H/3600 + w3.N’ Where Tu D H K N’ w1, w2, w3

=total uninterrupted travel time (veh-h/h), Tu= qa.tu where qais the arrival (demand) flow rate and tu is the uninterrupted travel time. = total delay due to traffic interruption (veh-h/h) = total number of effective stops (veh/h) = stop penalty = sum of the queue values (in vehicles) for all lanes, and = delay weight, stop weight, and queue weight values, respectively

A Stop Penalty parameter and various weight factors used for this purpose are included in the defaults system.

37

3.7

Research Matrix

The research matrix for the study is shown in Table 3.8. Table 3.8: Research Matrix Objective 

To evaluate the capacity and various performance measures of the intersection for vehicular traffic under the prevailing conditions 

To evaluate the capacity and performance measures of the intersection for vehicular traffic under the future traffic with various options of improved geometry and signalization of the intersection  To identify the deficiencies of the intersection, various improvement options of enhancing the intersection capacity and performance to an acceptable level, and recommend the best viable option 

Data needed 

i. Existing Geometric data  ii. Existing traffic characteristic data  iii. Existing manual traffic control pattern by traffic police  i. Geometric improvement option and details  ii. Future traffic characteristics at the intersection  iii. Signal timing design for the signalization option (fixed, actuated) 

Capacity and Operational performance measures generated for various improvement options 

38

Method of Data collection 

i. Field observation and past project data  ii. Video graphic survey at peak hours & traffic count   iii. Video observation of the traffic police controlling the intersection i. Various Geometric improvement options will be proposed  ii. Volume to be forecasted based on traffic growth from past secondary data at the site/similar project  iii. Based on signal timing design to be generated from analysis

Output results from the SIDRA intersection analysis run 

Analysis tools 

MS-Excel, Traffic Count Software, and SIDRA intersection v5.1 software 

MS Excel &SIDRA intersection v5.1 software 

Various proposed intersection layouts and signalization options (fixed, actuated) 

Chapter 4 RESULTS AND DISCUSSION 4.1 Intersection Geometry The exiting intersection is a four way intersection with the legs oriented almost in North – South and East – West directions. Two urban roads,one from New Baneshwor to Gaushala and the other from Maitidevi to Sinamangal intersect to form the Old Baneshwor junction. The intersection legs formed are: • North – Road to Gaushala • South – Road to New Baneshwor • East - Road to Sinamangal • West – Road to Maitidevi/Bagbazaar Based on the field measurement, observation, previous topographic survey data, and Google Earth image, Figure 4.1 shows the present intersection geometry with existing number of approach and exit lanes, approach and exit lane widths, lane disciplines, carriageway widths, kerb to kerb road width including side drains, approach grade and foot path widths in all the four legs.

(Source: Field survey, 2018) Figure 4.1: Present Intersection Geometry of Old Baneshwor Intersection 39

4.2 Land use and Building uses The land use pattern is mixed, which is characterized by high concentration of commercial and residentialbuildings. Densely built building structures are very close to the urban roads. Retail shops are thebackbone of business and social activities at the junction area. 4.3 Traffic and Pedestrian Volume Classified traffic studies were conducted from 8:00 to 11:00 in the morning and from 4:00 to 7:00 in the afternoon for three days from Monday, May 7 to Wednesday, May 9, 2018 with the recorded traffic video footage of the intersection. A total of 12 vehicular movement directions with 12 vehicle types including bicycles and 4 directions of pedestrian movements were observedat the intersection. In order to find out the total volume in PCU, the volume of each type of vehicles were multiplied with their respective PCU factors. The AM and PM peak hours were found out to be 10:00 - 11:00 and 5:15 - 6:15 respectively. The three day average of AM peak hour volumes of the intersection was 5387 veh/h (2370 PCU/h). Similarly, the PM peak hour volume of the intersection was 5486 veh/h (2363 PCU/h). Among the AM and PM peak hours, the hour with higher volume in terms of PCU/h was selected for the analysis of the operational performance of the intersection. Table 4.1 shows the hourly intersection total volume for three days. Table 4.1: Hourly Intersection Departure Volume for 3 days Hourly Interval

Hourly Intersection Total Volume for various days Mon, May 7, 2018 Tue, May 8, 2018 Wed, May 9, 2018 Average Veh. PCU Veh. PCU Veh. PCU Veh. PCU

Remarks

AM Hours 8:00 - 9:00

3,405

1,813

4,222

2,191

3,485

1,856

3,704

1,953

8:15 - 9:15

3,859

1,936

4,427

2,262

3,799

1,885

4,028

2,028

8:30 - 9:30

4,230

1,999

4,766

2,359

4,139

1,983

4,378

2,113

8:45 - 9:45

4,478

2,049

5,186

2,446

4,561

2,101

4,742

2,199

9:00 - 10:00

4,736

2,096

5,374

2,446

4,832

2,189

4,981

2,244

9:15 - 10:15

4,982

2,159

5,572

2,452

5,146

2,354

5,233

2,322

9:30 - 10:30

5,182

2,224

5,573

2,453

5,182

2,306

5,312

2,328

9:45 - 10:45

5,260

2,240

5,682

2,490

5,241

2,325

5,394

2,352

10:00 - 11:00

5,289

2,276

5,647

2,513

5,225

2,322

5,387

2,370

AM Peak

PM Hours 16:00 - 17:00

4,514

2,204

5,123

2,464

4,851

2,318

4,829

2,328

16:15 - 17:15

4,586

2,216

5,116

2,446

5,170

2,417

4,957

2,360

16:30 - 17:30

4,865

2,254

5,156

2,370

5,400

2,421

5,140

2,348

16:45 - 17:45

5,034

2,271

5,290

2,395

5,515

2,418

5,280

2,361

17:00 - 18:00

5,241

2,318

5,374

2,360

5,590

2,411

5,402

2,363

17:15 - 18:15

5,368

2,336

5,458

2,357

5,632

2,398

5,486

2,363

17:30 - 18:30

5,188

2,252

5,534

2,355

5,504

2,343

5,409

2,317

17:45 - 18:45

5,063

2,227

5,525

2,336

5,487

2,325

5,358

2,296

18:00 - 19:00

4,726

2,155

5,648

2,394

5,419

2,322

5,264

2,290

PM Peak

(Source: Field survey, May 2018) 40

Table 4.2: Fifteen Minute Interval Intersection Departure Volume for 3 days May 7, 2018

May 8, 2018

May 9, 2018

Monday

Tuesday

Wednesday

Veh.

PCU

Veh.

PCU

Veh.

PCU

Veh.

Peak Hour Volume PHF Remarks PCU (PCU/h)

8:00 - 8:15

654

365

936

503.3

722

419.5

771

429.3

8:15 - 8:30

797

462

1,020

525.3

848

491.8

888

492.8

8:30 - 8:45

920

484

991

538.0

882

454.3

931

492.0

8:45 - 9:00

1,034

502

1,275

624.0

1,033

490.3

1,114

538.8

9:00 - 9:15

1,108

489

1,141

574.5

1,036

449.0

1,095

504.0

9:15 - 9:30

1,168

524

1,359

622.5

1,188

589.0

1,238

578.6

9:30 - 9:45

1,168

534

1,411

624.8

1,304

572.8

1,294

577.3

9:45 - 10:00

1,292

549

1,463

624.0

1,304

578.0

1,353

583.8

10:00 - 10:15

1,354

552

1,339

580.8

1,350

613.8

1,348

582.0

10:15 - 10:30

1,368

589

1,360

623.5

1,224

541.5

1,317

584.8

10:30 - 10:45

1,246

550

1,520

661.5

1,363

591.8

1,376

601.2

10:45 - 11:00

1,321

585

1,428

647.5

1,288

574.8

1,346

602.5

Fifteen minute Intervals

AM Total:

Average

0.98

0.99

PM Peak Hour

13,430 6,185 15,243 7,150 13,542 6,366 14,072 6,567

16:00 - 16:15

1,098

533

1,326

623.3

1,022

508.0

1,149

554.8

16:15 - 16:30

1,096

559

1,266

633.5

1,259

624.5

1,207

605.5

16:30 - 16:45

1,118

546

1,266

609.0

1,227

570.3

1,204

575.2

16:45 - 17:00

1,202

566

1,265

597.8

1,343

614.8

1,270

592.9

17:00 - 17:15

1,170

545

1,319

606.0

1,341

607.3

1,277

586.1

17:15 - 17:30

1,375

596

1,306

556.8

1,489

628.5

1,390

593.8

17:30 - 17:45

1,287

563

1,400

634.0

1,342

567.8

1,343

588.3

17:45 - 18:00

1,409

614

1,349

563.5

1,418

607.5

1,392

595.0

18:00 - 18:15

1,297

562

1,403

603.0

1,383

593.8

1,361

586.3

18:15 - 18:30

1,195

513

1,382

554.0

1,361

574.0

1,313

546.9

18:30 - 18:45

1,162

538

1,391

615.5

1,325

550.0

1,293

567.8

18:45 - 19:00

1,072

542

1,472

621.5

1,350

603.8

1,298

589.2

PM Total:

2,370

AM Peak Hour

2,363

14,481 6,678 16,145 7,218 15,860 7,050 15,495 6,982

(Source: Field survey, May 2018)

41

Hourly Total Intersection Volume (3-day average) 6,000

Vehicles

PCU

Volume (Veh / PCU)

5,000

4,000

3,000

2,000

1,000

-

Morning

Time Interval

Afternoon

 1,298    589  

 1,293    568  

 1,313    547  

 1,361    586  

 1,392    595  

 1,343    588  

 1,390    594  

 1,277    586  

 1,270    593  

 1,207  

 1,204    575  

 555  

 606  

 1,149  

 1,346    603  

 1,376    601  

 1,317    585  

 1,348    582  

 1,353    584  

 1,294    577  

 1,095  

 1,114  

 1,238    579  

 600

 504  

 800

 539  

 1,000

 492  

 1,200

 931  

 1,400  429    771    493    888  

Veh or PCU / 15min.

Figure 4.2: Hourly Intersection Departure Volume (average of three days)

 400  200  ‐

AM

Time Interval

Veh/15 min

PM

PCU/15 min

Figure 4.3 Fifteen Minute Interval Intersection Departure Volumes (Average of 3 days) 4.4

Traffic Volume – Directional Vehicular Flows

Figure 4.4 shows the peak hour volume of each origin – destination (movement) at the study intersection. During the AM peak hour, the highest volume of 1949 veh departed from Gaushala leg at the North with left turn of 509 veh, 1045 through veh, and right turns of 395 veh. During the same hour, the second highest volume of 1404 veh departed from Sinamangal leg at the East with 412 veh left turning, 779 through veh, and 213 veh right 42

turning. Total 1157 vehicles departed from New Baneshwor leg at the South with 196 veh left turning, 903 veh going through, and 58 veh right turning. The total traffic volume that departed from Maitidevi Leg was 880 vehicles with 309 veh left turning, 510 vehgoing through, and 61 vehright turning during the AM peak hour. Thus, the total vehicle that departed from the intersection during the AM peak hour was 5390 vehicles.The volume study showed that North – South road, i.e. Gaushala – New Baneshwor road has the major traffic stream. Data of fifteen minute interval classified counts of turning movements are given in appendix 1.1 to appendix 1.5.

(Source: Field survey, 2018) Figure 4.4:AMPeak Hour Departure Volumes and Turning Movement at Old Baneshwor Intersection (in veh/h) Table 4.3 shows the AM peak hour direction wise departure volumes classified as heavy vehicle (HV) and light vehicle (LV) along with the percentage of heavy vehicles in each movement for input in SIDRA model of the existing intersection.Volume of motor cycles and cycles were converted into equivalent number of passenger cars by multiplying with their PCU factor of 0.25 and then added to other normal light vehicles to determine the total number of light vehicles including motorcycles/cycles for input in the SIDRA model. The overall percentage of HV’s departing from the intersection during the AM peak hour is 1.25%. The pedestrian volumes that crossed the road at the intersection during the AM peak hour were 265 ped/h across the New Baneshwor leg, 163 ped/h across the Maitidevi leg, 143 ped/h across the Gaushala leg, 190 ped/h across the Sinamangal leg and amount to a total volume of 761 ped/h.

43

Table 4.3: AM peak hour Turning Movement Volumes for input in SIDRA Intersection base case model

(PCU)

D1

1

31

164

41

72

73

1.37%

T

D2

8

203

692

173

376

384

2.08%

R

D3

8

50

13

21

21

0.00%

242

906

227

469

478

1.88%

(veh)

New Baneshwor Gausala Approach Sinamangal

(veh)

-

Approach Total:

9

Gausala

L

D6

Sinamangal

T

D5

New Baneshwor

R

D4

2

Approach Total:

Gausala Approach

(veh)

L

To Maitidevi

Maitidevi Approach

Tot. LV Total % of HV MC/ incl. MC LV + HV for Cycle for SIDRA for SIDRA SIDRA in PCU Input Input Input

Movement ID

From

MC/ Cycle

Turn Designation

Movement Description

HV LV for excl. SIDRA MC/ Input Cycle

(veh)

(veh)

(% )

102

207

52

154

154

0.00%

136

372

93

229

231

0.87%

1

7

53

13

20

21

4.71%

3

245

632

158

403

406

0.74% 1.08%

Sinamangal

L

D9

2

75

432

108

183

185

New Baneshwor

T

D8

11

241

793

198

439

450

2.44%

Maitidevi

R

D7

1

128

266

67

195

196

0.51%

14

444

1,491

373

817

831

1.69%

Approach Total: New Baneshwor Sinamangal Maitidevi Approach Gausala

L

D12

1

64

347

87

151

152

0.66%

T

D11

1

216

562

141

357

358

0.28%

R

D10

1

49

163

41

90

91

1.10%

3

329

1,072

268

597

600

0.50%

29

1,260

4,101

1,025

2,285

2,314

1.25%

Approach Total: Intersection Total:

500

Volume (veh/h)

Heavy Vehicle (HV)

 376  

400

Light Vehicle (LV)

 439    357  

300  229    154  

100 -

 195  

 183  

200

 151    90  

 72    8  

 21    ‐    

 2  

 2  

 11  

 1  

 1  

 1  

D1

D2

D3

D6

D5

D4

D9

D8

D7

D12

D11

D10

L

T

R

L

T

R

L

T

R

L

T

To Maitidevi

To Gausala

To Sinamangal

To Gausala

To Sinamangal

 1  

New Baneshwor Leg

 ‐    

 1  

 20  

 1  

To To To To To To Maitidevi Maitidevi New Sinamangal New New Baneshwor Baneshwor Baneshwor

Maitidevi Leg

Gausala Leg

R To Gausala

Sinamangal Leg

Figure 4.5: AM peak hour Turning Movement Volumes classified as HV and LV for input in SIDRA Intersection base case model

44

Volume (veh/h)

800 700 600 500 400 300 200 100 0

793

692

HV

LV

562

MC/Cycle

432

372 207 136 102 50 8 2 0 0

164 203 1 31

8

1 7

53

2

75

128 1

11

347

266

241

216 1

64

1

1

163 49

L

T

R

L

T

R

L

T

R

L

T

R

D1

D2

D3

D6

D5

D4

D9

D8

D7

D12

D11

D10

New Baneshwor leg

Maitidevi Leg

Gaushala Leg

Sinamangal Leg

Figure 4.6: Classified Volume of directional vehicular movements (AM peak hour)

Volume (Veh/h)

1,200

1,045 903

1,000

779

800 510

600 400 200

509

395

309

196

412 213

61

58

0 L

T

R

L

T

R

L

T

R

L

T

R

D1

D2

D3

D6

D5

D4

D9

D8

D7

D12

D11

D10

New Baneshwor leg

Maitidevi Leg

Gaushala Leg

Sinamangal Leg

Vehicles

Figure 4.7: AM Peak hour directional vehicular flows

1,400 1,200 1,000 800 600 400 200 0

1,154

PM Peak Hour Volume of Turning Movements 839

696 285

222

700

588 241

117

98

355 191

L

T

R

L

T

R

L

T

R

L

T

R

D1

D2

D3

D6

D5

D4

D9

D8

D7

D12

D11

D10

New Baneshwor leg

Maitidevi Leg

Gaushala Leg

Figure 4.8: PM Peak hour directional vehicular flows 45

Sinamangal Leg

4.5

Traffic demand (arrival) flow

Based on the traffic volume study at the upstream of the queues in the approach of each leg with the recorded video footage at the upstream of the intersection, table 4.4 shows the AM and PM peak hour demand, i.e. vehicle arrival volumes in each leg in vehicles and PCU. During AM peak hour, approach from Gaushala leg has the highest vehicle arrival rate of 1793 veh/h (803 PCU/h).During the PM peak hour, approach from New Baneshwor leg has the highest vehicle arrival rate in terms of veh/h (1533 veh/h, 643 PCU/h) while Gaushala leg has the highest vehicle arrival rate in terms of PCU/h (1380 veh/h, 662 PCU/h). This was due to higher number of motorcycles and less number of heavy vehicles in the New Baneshwor leg than in the Gaushala leg during the PM peak hour. Total arrival volume from all the legs of the intersection is 5545 veh/h (2419 PCU/h) in average of three days during the AM peak hour and 5410 veh/h (2365 PCU/h) during the PM peak hour. Data of 15 minute interval vehicle arrival counts for each leg are given in appendix 1.6 to appendix 1.10. Table 4.4: Peak hour Demand (Vehicle arrival) Volume for each leg Approach Demand (Vehicle Arrival) Volume

Total Intersection Demand Volume

North South (New East West (Gausala Leg) Baneshwor Leg) (Sinamangal Leg) (Maitidevi Leg)

Time Interval

PCU

Veh.

PCU

Veh.

PCU

Veh.

PCU

Veh.

PCU

AM Peak Hour: 10:00 ‐ 11:00 Monday, May 7, 2018    1,716 Tuesday, May 8, 2018    1,918 Wednesday, May 9, 2018    1,745 3‐day average    1,793

Veh.

      797       835       777       803

     1,151      1,288      1,205      1,215

        484         574         513         524

     1,421      1,743      1,647      1,604

         577          706          685          656

       896    1,027        877        933

        416         468         426         437

        5,184         5,976         5,474         5,545

        2,274         2,584         2,401         2,419

PM Peak Hour: 17:15 ‐ 18:15 Monday, May 7, 2018    1,290 Tuesday, May 8, 2018    1,473 Wednesday, May 9, 2018    1,378 3 days average in PM:    1,380

      589       731       667       662

     1,642      1,448      1,509      1,533

        682         612         633         643

     1,557      1,309      1,270      1,379

         733          530          544          602

   1,057    1,077    1,219    1,118

        417         446         512         458

        5,546         5,307         5,376         5,410

        2,421         2,319         2,357         2,365

2,000

4,764

5,075

4,969 2,236

2,151

2,328

2,275

5,263

5,438 2,397

2,365

5,410

5,464 2,382

5,308 2,353

5,125 2,312

4,985 2,320

4,825 2,333

4,800

PCU

2,328

4,763 2,360

2,416

5,306

5,430

Vehicles

2,435

5,545 2,419

2,460

5,888 2,503

5,836 2,502

5,512 2,436

2,338

4,727 2,243

4,347 2,165

3,550

3,945 2,038

3,000

1,935

4,000

3,229

5,000

1,814

Vehicles or PCU per hour

6,000

5,148

7,000

5,724

(Source: Field survey, 2018)

1,000

Morning

Time Interval

18:30 - 19:30

18:15 - 19:15

18:00 - 19:00

17:45 - 18:45

17:30 - 18:30

17:15 - 18:15

17:00 - 18:00

16:45 - 17:45

16:30 - 17:30

16:15 - 17:15

16:00 - 17:00

15:45 - 16:45

15:30 - 16:30

10:30 - 11:30

10:15 - 11:15

9:45 - 10:45

10:00 - 11:00

9:30 - 10:30

9:15 - 10:15

9:00 - 10:00

8:45 - 9:45

8:30 - 9:30

8:15 - 9:15

8:00 - 9:00

7:45 - 8:45

7:30 - 8:30

-

Afternoon

(Source: Field survey, 2018) Figure 4.9: Hourly Intersection Total Demand (Vehicle arrival) Volume observed at u/s of the intersection queues 46

. % 2 3

% 7 .3 0

2 % 6 .7

F rD u o lv ic h e V

t,/4 U liy W re h T % 8 .6

% .6 5

% 0 .3 1 ,7

4.6

Peak hour and Peak hour factor

The selection of the Peak hour for analysis was based on the hourly total departure volumes from the intersection in PCU. The peak hour total departure volume from the intersection was 2370 PCU (5387 veh) and the peak 15-minute volume was 602.5 PCU/15 min (1346 veh/15 min). The peak hour factor was found out to be 0.98 based on departure flows in PCU. Figure 4.10 and 4.11 show the variation of departure flow in PCU and vehicles respectively within the AM peak hour. 1,600 

650.0  582.0  584.8 

601.2  602.5 

1,400 

veh/15 min

600.0  PCU/15 min

le y trc o M

550.0  500.0  450.0 

1,200  1,000  800  600 

400.0 

400  10:00 ‐ 10:15 ‐ 10:30 ‐ 10:45 ‐ 10:15 10:30 10:45 11:00

10:00 ‐ 10:15 ‐ 10:30 ‐ 10:45 ‐ 10:15 10:30 10:45 11:00

15 minute Interval

15 minute Intervals

Figure 4.11: Flow variation within th Peak Hour in vehicles/15min

Figure 4.10: Flow variation within th Peak Hour in PCU/15 min

4.7

1,348  1,317  1,376  1,346 

Traffic Composition

Figures 4.12 and 4.13 show the composition of traffic streams departing from the intersection by vehicle types and by vehicle class respectively.

(Source: Field survey, 2018) Figure 4.12: Traffic composition at Old Baneshwor Intersection by vehicle types

47

),25.4% V (L

ot M

rcyle/C o

cle 7w

h3.6% o( w elr),T

aficC rT

e yV b ostin p m

as icleC h

During the classified traffic study, the total 12 types of vehicles were identified. The traffic at the intersection were composed of heavy truck, light truck, tractor, standard bus, minibus, micro bus, four wheel drive, utility vehicles, car, electric three wheeler, motorcycle, and cycle. The motorcycles were found out to have the highest percentage (71.3%) in the traffic mix at the intersection. The second highest composition (13 %) in the traffic volume was cars and taxis.

(Source: Field survey, 2018) Figure 4.13: Traffic composition at Old Baneshwor Intersection by vehicle class 4.8

Cruise Speeds

The approach cruise speed of various vehicles were determined from direct field observations by manual short-base method, which is described in section 3.5.8 in Data Collection Section of this report. The speed survey data are included in the appendix 2.1 to appendix 2.4. The table 4.5 shows the resultsof approach cruise speed study for each leg of the study intersection. Table 4.5: Summary of Approach Cruise Speed Study Name of Intersection Leg

95% th Standard Standard 85 No. of Mean Min. Max. Confidence deviation of error of the Speed Speed percentile Speed Speed Limit of the samples mean Speed Samples mean km/h km/h km/h km/h km/h km/h km/h

Gausala Leg

85

30.6

38.6

18.6

50.5

7.75

0.80

± 1.6

Maitidevi Leg

88

30.9

38.6

15.9

60.0

8.71

0.90

± 1.8

Sinamangal Leg

129

26.9

33.5

13.3

46.8

6.09

0.50

± 1.0

New Baneshwor Leg

108

19.4

24.5

8.9

38.0

5.23

0.50

± 1.0

(Source: Field survey, 2018) The 85th percentile speed was used for cruise speed, since this excludes extremely fast drivers (and gross measuring errors) and gives an estimate of what the majority of drivers consider a top limit (TRL, 1993). The exit cruise speed in each leg was assumed to be the same as the approach cruise speed. 48

The approach cruise speed in New Baneshwor leg was observed to be very low as compared to other legs because the pavement surface in this leg left damaged by the Melamchi Water Supply project during the pipe laying works had not been reconstructed yet. For the analysis of the operational performance of the exiting base case intersection, actual observed 85th percentile speed of 24.5 km/h was used for the New Baneshwor leg. But for the analysis of the improvement options, and assuming that the damaged pavement in New Baneshwor leg will be reconstructed in the near future, the 85th percentile cruise speed in New Baneshwor leg was adoptedequal to that in Sinamangal leg, i.e. 33.5 km/h (which is similar to New Baneshwor leg in road side activities, road width, grade, and parking activities). 4.9

Intersection Path Data Parameters

The table 4.6 shows the results of various path data parameters used for developing SIDRA intersection base case model, based on field survey, videography survey and Google Earth. Table 4.6: Path Data of various Movements at the Intersection in the Existing Base Case Condition New Baneshwor Leg

Approach Name =>

D1 L

Movement ID => Turn designation => Parameter

D2 T

D3 R

Maitidevi Leg D4 R

Gausala Leg

D5 T

D6 L

D7 R

D8 T

Sinamangal Leg D9 L

D10 R

D11 T

Remarks

D12 L

Unit th

km/h

24.5

38.6

38.6

33.5

Approach Travel Distance

m

1000

390

700

580

Negotiation Speed

km/h

10.0

13.0

12.0

12.0

17.4

13.0

12.5

11.5

13.0

16.0

22.0

13.0

Negotiation Distance

m

16.0

29.0

40.5

17.0

43.3

28.5

39.5

31.6

22.5

28.2

43.3

35.3

Downstream Distance

m

Prog

Prog

Prog

Prog

Prog

Prog

Prog

Prog

Prog

Prog

Prog

Prog

Negotiaton Radius

m

3.50

7.38

9.00

9.00

S

7.25

10.00

7.63

5.00

10.00

S

5.25

Turn radius

m

3.50

7.38

9.00

9.00

S

7.25

10.00

7.63

5.00

10.00

S

5.25

Cruise Speed

85 percentile speed Measured in Google Earth Mean Speed adopted Measured in ACAD To be calculated by Program Measured in ACAD Measured in ACAD

Notes: L: Left Turn, T: Through, R: Rigth Turn, S: Straight

4.10 Prevailing (field measured) Saturation Flow Rate observation The observation results of field measured saturation flow for various approach lanes based on saturated period count are given in appendix 4.1 to appendix 4.4. A summaryofthe measured saturation flow ratestudy based on saturated period count for a number of cycles are shown in the table 4.7. Since exclusive left turn lanes in New Baneshwor approach and Maitidevi approach did not have any considerable saturated (queued) discharge of vehicles, their saturation flow rate could not be measured at field. So, their prevailing saturation flow rates were assumed based on the saturation flow rate of exclusive left turn lane of Gausala approach and the other characteristic as turning radii. 49

Table 4.7: Summary of lane saturation flow studyat the existing intersection

Approach Name

New Baneshwor Leg Sinamangal Leg Gausala Leg Maitidevi Leg

Mean 95% No. of Standard Standard Lane Lane Saturation confidence samples Deviation Error No. discipline Flow limit observed (veh/h) (veh/h) (veh/h) (veh/h) 1 2 1 1 2 1 2

L TR LTR L TR L TR

25 21 10 25 25

935 1,309 1,715 1,114 1,395 1,150 1,278

443 180 319 367 130

89 39 121 73

± 178 ± 78 ± 242 ± 146

28

± 56

Remarks

Exclusive lane Shared lane Shared lane Exclusive lane Shared lane Exclusive lane Shared lane

(Source: Field survey, 2018) 4.11 Back of queue observation Field observation results of back of queue (BoQ) in all the cycles during the AM peak hour from 10:00 to 11:00 are given in the appendix 3.1 to appendix 3.4. Table 4.8 shows a summary of the back of queue study for the analysis peak hour. Table 4.8: Summary of back of queue survey during the AM Peak hour

Approach Lane Gausala Leg (shared lane-TR) Maitidevi Leg (Shared lane-TR) Sinamangal Leg (Shared lane-LTR) New Baneshwor Leg (Shared lane-TR)

th Standard Standard 95% 95 No. of Mean deviation of error of the Confidence Limit Speed BoQ percentile samples mean of the mean BoQ Samples veh. veh. veh. veh. veh.

33

20

45

13

2

±4

19

9

18

6

1

±2

30

15

30

8

1

±2

14

7

15

6

2

±4

4.12 Phasing and Signal timing Observed phase timesas assigned by the traffic police in various cycles during the AM peak hour (10:00 – 11:00) at the intersection based on observation of video footage is given in appendix 5.1 and appendix 5.2. There were basically two phases assigned by the traffic police in each cycle. Fixed time signal control was assumed for analysis. Figure 4.14 shows the phasing plan assigned by the traffic police. Table 4.9 shows the summary of traffic police assigned phase time observation. Table 4.10 shows mean phase time and green time for each phase for the observed cycles.SIDRA Default values of Yellow time and All-red time was adopted. The average cycle time was found out to be 179 sec.

50

Figure 4.14: Phasing Plan assigned by Traffic Police in existing condition

Table 4.9: Summary traffic police assigned phase time observations Standard 95% Mean Standard error Phase Observation No. of deviation Confidence Limit Phase time of the mean Designation Hour samples of samples of the mean sec sec sec sec Phase-A

10:00 - 11:00

39

110

48

8

± 16

Phase-B

10:00 - 11:00

36

69

26

4

±9

Mean Cycle Time = 179 sec (sum of all the mean phase times)

Table 4.10: Phase Timing Results based on observation of Traffic Police Control

4.13 Calibration of Basic Saturation Flow Local calibration of the default basic saturation flow (tcu/h) was performed by comparing the field measured saturation flow (veh/h) with the saturation flow (veh/h) estimated by 51

the developed SIDRA model of the intersection. GEH statistic was used as an acceptance criterion during calibration. SIDRA model was rerun until the GEH statistic was less than 5 during each run. Table 4.11 shows the final results of the local calibration of default basic saturation flows for various lanes. Table 4.11: Comparison of Field measured and Model estimated Saturation Flows with GEH statistics Approach Name

Field measured Model Estimated adjusted basic Lane Lane GEH Saturation Flow Saturation Flow Saturation Flow No. discipline Statistic (veh/h) (veh/h) (tcu/h)

1 L 935 959 0.78 1,800 2 TR 1,309 1,296 0.36 1,651 Sinamangal Leg 1 LTR 1,715 1,669 1.12 3,116 1 L 1,114 1,163 1.45 1,656 Gausala Leg 2 TR 1,395 1,346 1.32 1,983 1 L 1,150 1,150 0.00 1,534 Maitidevi Leg 2 TR 1,278 1,252 0.73 1,317 L: Exclusive Left turn, TR: Shared through & right turn, LTR: Shared left turn, through, & right turn New Baneshwor Leg

4.14 Validation of base case model The validation of the base case model of the existing intersection was carried out by using 95th percentile back of queue (BoQ) and Degree of Saturation (DoS). Table 4.12: Comparison of observed and model estimated 95th percentile back of queue th

95 Percentile BoQ Observed Model estimated (veh) (veh) 15 26.2 30 85 45 76.1 18 32.5

Approach Lane New Baneshwor Leg (Shared lane-TR) Sinamangal Leg (Shared lane-LTR) Gausala Leg (shared lane-TR) Maitidevi Leg (Shared lane-TR)

The SIDRA estimated back of queue equals the queue at the end of red plus the vehicles moving or in queue at a given time during the green interval.But, the vehicles arriving and moving at the back of queuewithout coming to a complete stop during the green time were not observed during field survey. That is why the observed 95th percentile BOQ’s are considerably less than those estimated by the SIDRA model. However, when the degree of saturation (DoS)in three approach lanes and for the overall intersection were used for validation as shown in table 4.13, a good match was obtained between the observed and the model estimated DoS as indicated by the RMSNE measure. Based on the fact that the observed departure volume equals the capacity during oversaturated conditions (demand volume > departure volume), the observed departure volumesof Sinamangal, Gausala, Maitidevi legs, and overall intersection, whichhad observed departure volumemore thanobserved demand volume,were taken as the observed capacity for calculation of observed DoS. 52

Table 4.13: Comparison of observed and model estimated degree of saturation (DoS) for the AM peak hour. Approach Name

Lane Lane no. discipline

RMSNE Observed Observed Observed Model for all demand departure DoS estimated obs. & est. (arrival) vol vol (v/c ratio) DoS data sets (veh/h) (veh/h)

Sinamangal Leg

1

Shared LTR

649

600

1.082

1.160

Gausala Leg

2

Shared TR

680

646

1.053

1.004

Maitidevi Leg

2

Shared TR

266

252

1.055

1.088

2461

2314

1.063

1.160

Intersection (Total)

0.116

4.15 Evaluation of Operational Performance of the Intersection at present under traffic police control The present model of the intersection for the AM peak hour was developed with adequate calibration of all the relevant geometric parameters, traffic flow, saturation flow, and traffic police assigned phase sequence and phase green times, and other relevant traffic characteristics based on the collected data. The model was then processed in SIDRA Intersection to determine the various operational performance statistics. Table 4.14 shows the results of operational performance measures foreach lane of the intersection under police control at present.

Approach Name New Baneshwor Leg Sinamangal Leg Gausal Leg Maitidevi Leg Intersection

th Average 95 Demand HV Capacity Delay percentile Lane Lane DoS LOS flow per veh ID use BoQ

veh/h

%

veh/h

(sec)

(veh)

1

L

78

1.4

747

0.104

8.8

0.8

A

2

TR

432

2.0

688

0.628

39.3

26.2

D

1

LTR

649

0.5

559

1.160

169.9

85

F

1

L

195

1.1

1013

0.192

8.4

1.2

A

2

TR

680

1.9

677

1.004

101.5

76.1

F

1

L

162

0.0

1040

0.156

10.1

1

B

2

TR

266

1.2

245

1.088

156.8

32.5

F

2462

1.3

2123

1.160

98.3

85

F

Performance Index

Table 4.14: Summary ofoperational performance of the intersection at present under traffic police control.

258.4

Figure 4.15 shows the lane configuration with LOS for various lanes at present during the peak hour. The total intersection demand flow rate is 2462 veh/h with average 1.3% heavy vehicles. The overall degree of saturation of the intersection is 1.16, which suggest that the

53

intersection is operating in oversaturated condition in overall at present under traffic police control. Average delay per vehicle in the intersection is 98.3 sec. Shared approach lanes of Sinamangal leg, Gausala leg, and Maitidevi leg are all oversatruated during the peak hour with demand flow more than capcity in these lanes. Vehcles in all these three lanes experience LoS F and have high delay. The average control delay in approach lane of Sinamangal leg is the highest with 169.9 sec among these three lanes. Shared lane in New Baneshwor leg operates at LoS D. All exclusive left turn lanes operate under LoS B or better. 95 percentile back of queue is higest in Sinamangal approach lane with 85 veh (320.6 m) and 32.5 veh (123.6 m) in Maitidevi leg.

Figure 4.15: Lane Configuration and LoS of various lanes of the intersection under police control at present in the AM Peak hour Figure 4.16 shows the performance measures for the various movements and lanes of the intersection at present.

54

Demand flow (veh/h), HV%, Pedestrian flows

Capacity (veh/h)

Degree of Saturation

Average Delay per vehicle & pedestrian delay (sec)

95 percentile BoQ (veh)

LOS

Figure 4.16: Peak hour flow, Capacity, Degree of saturation, average control delay, 95 percentile back of queue, and LoS at the present condition under police control 55

4.16 Evaluation ofvarious Lane Configuration Optionswith signalization of the Intersection for the base year 2018 The intersection at present condition with the existing lane configuration and traffic police control suffers from poor performance with lower capacity, oversaturation, higher delay, long queues, and very poor LOS, especially in the shared lanes of Sinamangal, Gausala, and Maitidevi legs. Therefore, six options with threedifferent lane configurationsand traffic signalization of the intersection without any geometric improvement except rearrangement of lane discipline and adjustment of lane widths were proposed for analysis. Lane configuration A: Figure 4.17 shows the lane configuration A with proposed lane disciplines and lane widths of various approach and exit lanes. Two approach lanes, one with exclusive left turn, the other with shared through and right turns, and single exit lane of 3.5 m have been proposed in the Sinamangal.Lane assignments in other legs are the same as in the present condition. No geometric improvement other than rearrangement of lane widths and lane discipline has been done.

Figure 4.17: Lane Configuration A

56

Lane configuration B: Figure 4.18 shows the lane configuration B with proposed lane disciplines and lane widths of various approach and exit lanes. Features of this configuration are: two approach lanes in Sinamangal leg, one with exclusive right turn, the other with shared left turnand through, and a single exit lane of 3.25m width;three approach lanes in Gausala leg, one exclusive right turn, one exclusive through, and one exclusive left turn lanes, and a single exit lane of 3.5m width.Lane assignments in other legs are the same as in the present condition except an adjustment of lane widths.

Figure 4.18: Lane Configuration B

Lane configuration C: Figure 4.19 shows the lane configuration C with proposed lane disciplines and lane widths of various approach and exit lanes. Features of this configuration are: Two approach lanes in Sinamangal leg, one withan exclusive left turn and the other with shared right turn and through and a single exit lane of 3.25 m width;three approach lanes in Gausala leg, one exclusive right turn, one exclusive through, and one exclusive left turn with single exit lane of 3.50 m width. All approacheshave exclusive left turns in this configuration. No geometric improvement other than rearrangement of lane widths and lane discipline has been done. 57

Figure 4.19: Lane Configuration C The following six different options of combination of lane configuration and signal phasing plan were developed for analysis: Option A1: Lane configuration A, 2 signal phasing plan, Default basic saturation flow used Option A2: Lane configuration A, 2 signal phasing plan, Local calibration of default basic saturation flow performed Option B1: Lane configuration B,Onlyprotected right turns from Sinamangal and Gausala legs, 4 signal phasing plan Option B2: Lane configuration B,Protected and permitted right turns from Sinamangal and Gausala legs,Exclusive left turns allowed in all phases, 3 signal phasing plan Option C1: Lane configuration C,Protected and permitted right turns from Sinamangal and Gausala legs,Exclusive left turns allowed in all phases, 3signal phasing plan Option C2: Lane configuration C, Protected and permitted right turns from Sinamangal and Gausala legs,Exclusive left turns allowed in all phases,2signal phasing plan

58

4.16.1 Evaluation of Option A1 (Lane configuration A, 2 signal phasing plan, Default basic saturation flow used)

Signal – Fixed Time Cycle Time = 75 sec (Optimum Cycle Time – Minimum Delay)

Figure 4.20: Phasing Summary for Option A1 Table 4.15: Summary of performance of the intersection for Option A1

59

Figure 4.21: Lane Configuration and LOS summary of Option A1

60

4.16.2 Evaluation of Option A2 (Lane configuration A, 2 signal phasing plan, Local calibration of default basic saturation flow performed)

Signal – Fixed Time Cycle Time = 75 sec (Optimum Cycle Time – Minimum Delay)

Figure 4.22: Phasing summary of Option A2 Table 4.16: Summary of performance of the intersection for Option A2

 

 

61

 

 

Figure 4.23: Lane Configuration and LOS summary of Option A2

62

4.16.3 Evaluation of Option B1 (Lane configuration B, Protected right turns from Sinamangal and Gausala legs, 4 signal phasing plan) Signal – Fixed Time Cycle Time = 120 sec (Optimum Cycle Time – Minimum Delay)

Figure 4.24: Phasing Summary of Option B1 Table 4.17: Summary of performance of the intersection for Option B1

63

Figure 4.25: Lane Configuration and LOS summary of Option B1

64

4.16.4 Evaluation of Option B2 (Lane configuration B, Protected and permitted right turns from Sinamangal and Gausala legs, Exclusive left turns allowed in all phases, 3 signal phasing plan) Signal – Fixed Time Cycle Time = 65 sec (Optimum Cycle Time – Minimum Delay)

Figure 4.26: Phasing Summary of Option B2 Table 4.18: Summary of performance of the intersection for Option B2

65

Figure 4.27: Lane Configuration and LOS summary of Option B2

66

4.16.5 Evaluation of Option C1 (Lane configuration C, Protected and permitted right turns from Sinamangal and Gausala legs, Exclusive left turns allowed in all phases, 3 signal phasing plan) Signal – Fixed Time Cycle Time = 70 sec (Optimum Cycle Time – Minimum Delay)

Figure 4.28: Phasing Summary of Option C1 Table 4.19: Summary of performance of the intersection for Option C1

67

Figure 4.29: Lane Configuration and LOS summary of Option C1

68

4.16.6 Evaluation of Option C2 (Lane configuration C, Protected and permitted right turns from Sinamangal and Gausala legs, Exclusive left turns allowed in all phases,2 signal phasing plan)

Signal – Fixed Time Cycle Time = 60 sec (Optimum Cycle Time – Minimum Delay)

Figure 4.30: Phasing Summary of Option C2 Table 4.20: Summary of performance of the intersection for Option C2

69

Figure 4.31: Lane Configuration and LOS summary of Option C2

70

4.16.7 Comparison of Various Options Table 4.21: Comparison of Overall Performance Measures of the Intersection for various options(For the base year 2018) Description of Performance Measures (PM) Total Itersection Demand Flows (veh/h) Percent of Heavy Vehicles Cycle Time (sec) Effective Intersection Capacity (veh/h) Degree of Saturation (DOS) Control delay per vehicle (Average) (sec) Control delay per vehicle (Worst lane) (sec)

Base Case Option Option with Police A1 A2 control 2462 2462 2462 1.3% 1.3% 1.3% 179 75 75 2123 1852 1700 1.16 1.330 1.449 98.3 102.3 127.6 169.9 201.4 260.2

Option Option Option Option B1 B2 C1 C2 2462 1.3% 120 2194 1.122 88.1 139.2

2462 1.3% 65 2450 1.005 47.5 69.9

2462 1.3% 70 2768 0.889 34.8 46.5

2462 1.3% 60 2649 0.929 32.6 44.2

th

95 percentile Back of Queue - Vehicles (worst lane)

85

64.5

74.8

46.5

25.5

18.6

19.4

320.6

246.9

286.4

175.1

96.1

70.7

73

F

F F B 207.8

F F B 235.7

F F C 221.4

D F B 142.6

C D B 123.3

C D B 116.5

th

95 percentile Back of Queue - Distance, m (worst lane) Intersection LOS for Vehicles (Overall) Worst Lane LOS for Vehicles Pedestrian LOS at the intersection Overall Performance Index (PI)

F D 258.4

Performance index, being a measure that combines several other performance statistics, was used as a basis for choosing between various options (the best option is the one which gives the smallest value of PI). So, option C2 having the smallest PI of 116.5 is considered as the best performing option for the base year 2018. 4.18 Evaluation of Option C2 for the future year 2023 (5 yearsdesign life) The evaluation of various lane configuration options with traffic signalization showed that option C2 performed the best for the base year 2018 with an overall intersection LOS C and none of the lanes operating below LOS D. Then, option C2 was further analysed to check whether the intersection performs with an acceptable LOS for the design life of 5 years up to the year 2023. A traffic growth for the future analysis was assumed to be at a compound rate of 2 % per year. The evaluation results showed that the intersection in this option in overall will perform with LOS E in future year 2023. However, the shared through/right lanes in New Baneshwor, Sinamangal, and Maitidevi approaches, and the right turn lane in the Gausala approach will perform at LOS F. The other remaining lanes will perform at LOS D or better. The average control delay will be 72.4 sec/veh for the intersection and 125.2 sec/veh for the worst lane. The overall degree of saturation for the intersection will be 0.993. The summary of evaluation of the option C2 for the future year 2023 (5 years design period) is shown in Table 4.22. Figure 4.32 shows the summary of the signal phasing applied. Figure 4.33 shows the summary of LOS for the future year 2023. 71

Signal – Fixed Time Cycle Time = 180 sec (Practical Cycle Time)

Figure 4.32: Phasing Summary of Option C2 for the year 2023 Table 4.22: Summary of performance evaluation of Option C2 (Year 2023)

72

Figure 4.33: Lane Configuration and LOS summary of Option C2 (Year 2023) 4.19 Geometric Improvement Proposed for the Intersection As the evaluation of optimum lane configuration and phasing plan of option C2 at the end of five years design life in the year 2023 showed that the intersection performs with an unacceptable LOS F, a geometric improvement of the intersection was proposed with improvement only in the corner kerbturning radii, since the intersection has very sharp kerb turning radii (varying from 1.5m to 5.5m) at the corners at present and the North and South legs are staggered by 18.0 m. So, the minimum corner kerb turning radii based on minimum radius of 9 to 15m for trucks & busses on urban arterial streets as per the IRC SP-41 (Guideline for the design of at-grade intersections in Rural & Urban areas) was proposed in geometric improvement in order to increase the turning and negotiation radii of the turning vehicle path.This should result in higher negotiation speeds and saturation flows of vehicles with more efficient traffic operation at the intersection. Addition of 73

auxiliary lanes and increase in carriageway widthwere not proposed. The details of corner kerb radiiare given in the table 4.23. The lane configuration and geometric improvement proposed are shown in figure 4.34. As the North and South legs are staggered by 18.0 m, the South-East, and North-West corners were proposed with a higher radii of 15.0 m than other corners for an efficient traffic flow in the major North-South Road (Gausala – New Baneshwor). Only four houses at the four corners will be affected by this improvement, and have to be properly compensated for land acquisition for the proposed geometric improvement. Table 4.23: Details of the corner kerb radius Description South-West corner kerb North-Eat corner kerb South-East corner kerb North-West corner kerb

Existing radius (m) 1.50 3.10 3.50 5.50

Proposed radius (m) 10.0 10.0 15.0 15.0

Figure 4.34: Lane configuration-C with improvement of corner kerb radii

74

4.20 Evaluation of Performance of the Intersection with Proposed Geometric Improvement In order to see if the intersection with the proposed geometric improvement with only the corner kerb turning radii improvement will perform at an acceptable LOS, analysis was performed for the base year 2018 and future years 2020, 2023, and 2028. A traffic growth for the future analysis was assumed to be at a compound rate of 2 % per year. 4.20.1 Evaluation of proposed geometric improvement for the base year 2018 Gausala Leg

New Baneshwor Leg

Figure 4.35: LOS summary of geometric improvement for the base year 2018 Table 4.24: Evaluation results of geometric improvement for the base year 2018

75

4.20.2 Evaluation of proposed geometric improvement for the year 2020 Gausala Leg

New Baneshwor Leg

Figure 4.36: LOS summary of geometric improvement for the year 2020 Table 4.25: Evaluation results of geometric improvement for the year 2020

76

4.20.3 Evaluation of proposed geometric improvement for the year 2023 Gausala Leg

New Baneshwor Leg

Figure 4.37: LOS summary of geometric improvement for the year 2023 Table 4.26: Evaluation results of geometric improvement for the year 2023

77

4.20.4 Evaluation of proposed geometric improvement for the year 2028 Gausala Leg

New Baneshwor Leg

Figure 4.38: LOS summary of geometric improvement for the year 2028 Table 4.27: Evaluation results of geometric improvement for the year 2028

   

78

4.20.5 Summary of performance evaluation of the proposed geometric improvement of the Intersection for various years The intersection with the proposed geometric improvement with increase of only corner kerb turning radii performs at LOS B in overall with LOS C in the worst lane in the baseyear 2018. In 5 years (Year 2023), the intersection performs at LOS C in overall with LOS C in the worst lane. In 10 years (Year 2028), the intersection with the proposed geometric improvement performs at LOS C in overall with LOS D in the worst lane. Table 4.28 shows the summary of various performance measures for the intersection with the proposed geometric improvement for the base year and various future years. As per the design life analysis for worst lane level of service target of LOS C, the intersection will perform with no lanes operating below LOS C for 7 years from the base year, i.e. up to the year 2025. After 2025, some lanes will begin to operate at LOS D.The intersection LOS will be C with some lanes performing at LOS D by the year 2028. Table 4.28: Summary of performance evaluation of proposed geometric improvement of the intersection for various years 2018 (Base yr.) 2462 1.3%

2020 (2 yrs.)

2023 (5 yrs.)

2028 (10 yrs.)

2562 1.3%

2718 1.3%

3001 1.3%

Effective Intersection Capacity (veh/h) Intersection Degree of Saturation (DOS) Control delay per vehicle (Average) (sec) Control delay per vehicle (Worst lane) (sec)

45 2941 0.837 16.7 21.6

50 3083 0.831 18.2 24.2

50 3073 0.885 21.2 28.8

90 3349 0.896 30.4 43.4

95th percentile Back of Queue - Vehicles (worst lane)

12.4

13.8

16.6

30.1

46.8 B C B 89.4

52.3 B C B 97.4

62.7 C C B 110.2

113.4 C D B 155.4

Description of Performance Measures (PM) Intersection Total Demand Flows (veh/h) Per cent Heavy Vehicles Cycle Time determined by SIDRA (sec)

th

95 percentile Back of Queue - Distance, m (worst lane) Intersection LOS for Vehicles (Overall) Worst Lane LOS for Vehicles Pedestrian LOS at the intersection (Overall) Overall Performance Index (PI)

79

Chapter 5 CONCLUSION AND RECOMMENDATIONS 5.1

Conclusion

This research was carried out to investigate the operational performance of the Old Baneshwor intersection at the peak hour in the existing condition and to propose measures of improvement to enhance the capacity and performance of the intersection to an acceptable level of service. Videography survey was conducted for 8:00 – 11:00 in the morning and 4:00 – 7:00 in the evening for 3 typical days from Monday, May 7 to May 9, 2018 at the intersection and upstream of the queues in each approach of the four legs of the intersection. Classified counts of turning movements at the intersection were used to determine the vehicle departure volumefrom the intersection per 15 minutes. Demand (vehicle arrival) volumeat the intersection per 15 minute was also determined with classified counts at the upstream of the intersection queues. The peak hour and peak hour factor were based on the total vehicle departures in PCU from the intersection. The peak hour at this intersection was found out to occur from 10:00 – 11:00 in AM with the peak hour factor of 0.98 based on PCU. The total departure volume from all the approaches of the intersection was 5387 veh/h (2370 PCU/h) and the total demand (arrival) flow was 5545 veh/h (2419 PCU/h) during the peak hour, which indicated that the intersection is oversaturated in overall at the present geometric and traffic condition under traffic police control. The directional volume study showed that North – South road, i.e. Gausala – New Baneshwor road has the major traffic stream. Surveys for cruise speed study, negotiation speed study, prevailing saturation flow study, back of queue (queue length) study, phasing and signal timing assigned by traffic police were conducted for the calibration and validation of the intersection base case model. Local calibration of the default basic saturation flow was conducted and validation of the base case model was done by using 95 percentile back of queue and degree of saturation for the analysis of present condition of the intersection. In the validation using 95 percentile back of queue, considerable variations in the observed and model estimated back of queues were due to not observing the vehicles arriving and moving at the back of queueafter the go signal indication by traffic police during the queue field survey and SIDRA estimated back of queue includes the queue at the end of red plus the vehicles moving or in queue during the green interval. However, in the validation using degree of saturation, a good match was obtained between the observed and the model estimated degrees of saturation for the oversaturated lanes and the intersection as a whole. Thus the base case model of the intersection was concluded to be valid. SIDRA Intersection 5.1 was used for developing and analysing the base case and future intersection models. The default basic saturation flow of 1800 tcu/h for environment class 2, i.e. average to poor (area type 2), as suggested in literatures was used for analysing future models of intersection with improvements in lane configuration and geometry.It was assumed in analysis that there was not be any parking or vehicle stopping within 75 m from the stop linein the approach and exit lanes in each intersection leg. The performance of the intersection wasstudied at the present condition with traffic police control without any improvement and with various six options of combinations of lane configurations 80

andsignal phasing for traffic signalization of the intersection without geometric improvement. Further, analysis of the intersection with geometric improvement in only the corner kerb turning radii was performed for future year performance. The performance measures for evaluation were capacity, degree of saturation (v/c ration), average delay, LOS, back of queue (queue length), and overall performance index. The overall performance level of the intersection at present condition with traffic police control without any improvement was found to be at LOS F and oversaturated (DoS = 1.16) with PI = 258.4, average overall intersection delay of 98.3 sec/veh, and greatest average delay varying from 101 sec/veh to 170 sec/veh in the shared lanes of Gausala, Sinamangal, and Maitidevi approaches during the peak hour. Fixed time signal method was used for traffic signalization and six options of combinations of three lane configurations and various optimum signal phasing were studied to find the operational performance of the intersection at present without any geometric improvement.The analysis of these options showed that inadequate lane assignments in the approaches of the Gausala and Sinamangal legs with higher traffic demand and inefficient phasing of the traffic police during the peak hour were the main causes of poor performance of the intersection at present with traffic police control. Hence, it was concluded that improvement in lane configuration with rearrangement of lane assignments and minor adjustment of lane widths within the existing carriageway and optimum traffic signalization are necessary for improving the operational performance of the intersection. Among the six options A1, A2, B1, B2, C1, and C2 that were evaluated with signalization, options A1, A2, and B1 did not have significant improvements in performance although they are slightly better than base case with police control as indicated by the PI of 207.8, 235.7, and 221.4 respectively. But all these first three options performed at LOS F. There were significant improvements in all the performance measures of the intersectionwith the last three options B2, C1, and C2 with LOS D, LOS C, and LOS C respectively. Among these last three options, option C2 had the least PI = 116.5 and least delay of 32.6/veh. The capacity and degree of saturation for option C2 are 2649 veh/h and 0.929 respectively. The worst lane in this option gets LOS D. Hence, it w concluded this option is best performing in terms of all performance parameters for signalization at the base year. Further evaluation of option C2 at end of the design life of 5 years in the year 2023 at a compound traffic growth rate of 2 % per year revealed that the intersection with this option will perform at an unacceptable LOS E in overall with four approach lanes at LOS F. So, a geometric improvement in the option C2 was proposed by increasing the radii of the existing very sharp corner kerbs to radii of 9 m to 15 m in order to increase the turning and negotiation radii of the turning vehicle paths for more efficient traffic operation. Analysis of this geometric improvement for various future years showed intersection performance is enhanced to an acceptable LOS threshold of LOS C with the worst lane performing at LOS D by the year 2028. Design life analysis for worst lane level of service target of LOS C revealed that the intersection with the proposed geometric improvement in the Option C2 will perform with no lanes operating below LOS C for 7 years up to 2025. After 2025, some lanes will begin to get LOS D with overall intersection having LOS C up to the year 2028, which is an acceptable performance. So grade separation will not be required up to year 2028 provided the traffic growth rate is maintained at 2 % per year, which can be

81

achieved by implementing walking, cycling and public transport favoured land use and urban transport policies. . 5.1 Recommendation It is recommended to improve the intersection with rearrangement of lane assignments and minor adjustment of lane widths as per the lane configuration C with installation of traffic signalization for short term up to the year 2020. Then the proposed geometric improvement by increasing the radii of the corner turning kerbs along with traffic signalization is recommended to be implemented up to year 2028. Proper compensation of affected four houses at the four corners and required land acquisition is proposed for the geometric improvement for the efficient operation of the intersection. Kerb Parking and stopping of public vehicles shall not be allowed within 75 – 100 m from the stop line in the approach and exit lanes of each leg of the intersection for efficient departures of the vehicles from the intersection, since proposed lane configuration has single exit lanes. 5.3

Scope for Future Works

It is very difficult to cover all aspect of topic in a limited time period with limited resources. The recommendations for further studies in similar topic are as follows: •

In areas where there are heavy volumes of motorcycles,this study can be done with an alternative method of allowing for motorcycles as suggested in user guide of the SIDRA Intersection 5.1 by adjusting the basic saturation flow and Light Vehicle queue space parameters to allow for motorcycles. The motorcycle volumes can then be specified as normal Light Vehicle (LV). Or, a new version of SIDRA Intersection which allows inputting motorcycles as a separate class can be used to perform this study and comparison be made.

•

Similar types of study can be done by using traffic simulation tools such as VISSIM, which is effective in evaluating the dynamic evolution of traffic congestion problems on transportation systems and can model the variability in driver/vehicle characteristics and the result compared with that of SIDRA Intersection.

82

REFERENCES Acharya, B., 2015. Development of Traffic diversion algorithm for the possible reduction of Traffic demand at intersection: a case study of Thapathali intersection. MSc. Nepal Engineering College-Centre for Postgraduate Studies (nec-CPS), Pokhara University. Akcelik & Associates Pty Ltd, 2012. SIDRA intersection 5.1 User Guide. [Online] Greythorn, Australia: Akcelik & Associates Pty Ltd Available at: https://sidrasolutions.com [Accessed 15 June 2017]. Anusha, C.S., Verma, A. & Kavitha, G., 2013. Effects of Two-Wheelers on Saturation Flow at Signalized Intersections in Developing Countries. [Online] USA: American Society of Civil Engineers Available at: https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29TE.1943-5436.0000519 [Accessed July 2017]. Bell, M.G.H., 1997. Signal Control at Intersections. In C.A. O'Flaherty, ed. Transport Planning and Traffic Engineering. New Delhi: Reed Elsevier India Private Limited. Ch. 26. Camp Dresser & McKee Inc., 2010. Polson Area Transportation Plan. Existing Intersection Level of Service. Helena, Montana: Montana Department of Transportation. Chand, S., Gupta, N.J. & Kumar, N., n.d. Analysis of Saturation Flow at Signalized Intersections in Urban Area. (Abstract Number: 239), pp.1-10. DoR, 2013. Nepal Road Standard 2070. [Online] Kathmandu, Nepal: Department of Roads (DoR) Available at: http://dor.gov.np/home/publication/general-documents/nepalroad-standard-2-7 [Accessed 1 August 2017]. DPTI, 2017. Traffic Modelling Guidelines - SIDRA INTERSECTION 7. Australia: Department of Planning, Transport and Infrastructure (DPTI), Government of South Australia. Ekman, A.K., 2013. Calibration of traffic models in SIDRA, [online]. Available at: http://liu.diva-portal.org/smash/get/diva2:633004/FULLTEXT01.pdf [Accessed 3 March 2018]. Kadiyali, L.R., 2012. Capacity of Urban Streets. In Kadiyali, L.R. Traffic Engineering and Transport Planning. 7th ed. New Delhi, India: Khanna Publishers. p.531. Mathew, T.V., 2017. Uncontrolled Intersection. [Online] Bombay: Indian Institute of Technology (IIT) Available at: https://www.civil.iitb.ac.in/tvm/nptel/tselnp51.pdf [Accessed 15 February 2018]. Meyer, M.D. et al., 1989. A Toolbox for Alleviating Traffic Congestion. Washington , DC: Institute of Transportation Engineers.

83

O'Flaherty, C.A., 1997. Intersection design and capacity. In C.A. O'Flaherty, ed. Transport Planning and Traffic Engineering. New Delhi: Reed Elsevier India Private Limited. Ch. 20. Quadratullah & Maruyama, T., 2015. Comparison of Intersection Capacity with Traffic Flow in Kabul Metropolitan Area: 2008, 2014 and 2025. International Journal of Architecture, Planning and Building Engineering, [online] 02(02), pp.37-41. Available at: http://basharesearch.com/IJAPBE/7020202.pdf [Accessed 15 July 2017]. Sharma, A.K., 2016. Comparison of Probable Congestion Reduction Approaches at New Baneshwar Intersection in Kathmandu. MSc. nec-CPS, Pokhara University. SMEC International Pty, Ltd. Australia In Association with Brisbane City Enterprise Pty., Australia Transportation Planning (International) Ltd., UK, GEOCE Consultants (P) Ltd., Nepal, 2014. Traffic Management Working Paper 2: Traffic Analysis. Volume 1: Main Text. Detailed Project Report. Kathmandu: GoN, MoPIT, Project Management and Coordination Office. Soil Test-AVIYAAN Consulting (P) Ltd. JV, 2011. Detailed Traffic Study, Engineering Survey, Soil Exploration, Design, Drawing and Preparation of Tender Documents for Grade Separated Intersection. Basic Configuration Design Report. Vol I: Main Report. Detailed Project Report. Kathmandu: GoN/DoR. Tiwari, G. et al., 2011. Modification of Highway Capacity Manual Model for Evaluation of Capacity and Level of Service at a Signalized Intersection in India. Journal of Eastern Asia Society for Transportation Studies, 9, pp.1-14. TRB, 2000. Signalized Intersection. In TRB HCM2000 Highway Capacity Manual. Washington, DC: Transportation Research Board (TRB). Ch. 16. TRB, 2010. Signalized Intersection. In TRB HCM2010 Highway Capacity Manual. Washington DC, USA: Transportation Research Board (TRB). Ch. 18. TRB, 2010. Signalized Intersections: Supplemental. In TRB HCM2010 Highway Capacity Manual. Washington DC: TRB. Ch. 31. TRB, 2010. Traffic Flow and Capacity Concepts. In TRB HCM2010 Highway Capacity Manual. Washington DC, USA: TRB. Ch. 4. TRL, 1993. Overseas Road Note 11, Urban Road Traffic Surveys. Crowthorne, Berkshire, United Kingdom: Transport Research Laboratory (TRL). WIKIPEDIA, n.d. GEH statistic. [Online] Available https://en.wikipedia.org/wiki/GEH_statistic [Accessed 14 June 2018].

84

at:

APPENDICES

85

Appendix-1.1: Fifteen Minute Classified Counts of Turning movements in the AM Peak Hour (Average of 3 days) From

To

Movmnt. Turn ID Desig.

Maitidevi

D1

L

Vehicle Category

10:00 - 10:15 - 10:30 - 10:45 - Total 10:15 10:30 10:45 11:00 (veh)

HV

0

0

1

0

1

LV

10

7

5

9

31

MC/Cycle Total for movement : HV

New Baneshwor Leg

Gausala

D2

T

LV MC/Cycle

Total for movement : HV Sinamangal

D3

R

LV MC/Cycle

D4

R

T

L

T

L

0

2

1

2

3

8

11

12

12

15

50

16.8%

1 7

14

16

13

9

53

15

19

15

11

61

HV

1

0

1

0

2

LV

31

35

40

30

136

92

93

97

90

372

124

128

138

120

510

HV

0

0

0

0

0

LV

23

23

27

28

102

52

50

53

53

207

74

73

80

82

309

5.7%

214

220

233

213

880

16.3%

HV

0

0

0

0

1

LV

27

30

32

39

128

LV

LV

56

44

81

86

266

83

74

113

125

395

3

1

3

3

11 241

64

60

55

62

222

199

197

175

793

289

261

255

240

1,045

1

0

1

0

2

1.1%

9.5%

7.3%

19.4%

17

21

18

19

75

111

110

111

101

432

129

130

129

120

509

9.4%

501

465

497

485

1949

36.2%

HV

0

0

0

0

1

LV

15

6

13

15

49

LV

43

43

37

40

163

58

49

50

55

213

0

0

0

1

1 216

50

60

52

53

137

146

135

144

562

187

207

187

198

779

HV

0

0

0

1

1

LV

16

14

17

17

64

79

81

98

89

347

MC/Cycle

4.0%

14.5%

94

95

115

107

412

7.6%

340

351

353

360

1404

26.0%

1,348

1,317

1,376

1,346

5,390

100.0%

Total for movement : Total for Leg: Total for the Intersection:

0

2

MC/Cycle

D12

0

0

Total for movement : New Baneshwor

0

1

HV T

0

1

MC/Cycle

D11

903

3

Total for movement : Sinamangal Maitidevi Leg

219

0

Total for movement :

R

240

1

Total for Leg: D10

215

0

MC/Cycle

Gausala

692

228

LV

HV L

50 168

HV

MC/Cycle

D9

54 186

1.1%

Total for movement : Sinamangal

56 156

21.5%

HV D8

203

43 182

3.6%

58

MC/Cycle

New Baneshwor

8

1157

Total for movement : Gausala Leg

1

18

Total for Leg: R

1

288

Total for movement :

D7

3

14

MC/Cycle

Maitidevi

196

2

294

MC/Cycle

D6

164

13

Total for movement : Gausala

41 50

281

MC/Cycle

D5

35 40

13

Total for movement : Maitidevi Sinamangal Leg

46 53

293

Total for movement : Total for Leg: New Baneshwor

42 52

% Vol. wrt Tot. Int. Vol.

(Source: Field survey, May 2018)

86

Appendix-1.2: Fifteen Minute Intersection Counts of Turning movements (Average of three days)

Fifteen Minute Intersection Counts of Turning movements Site Name: Old Baneshwor Intersection Average of Three Days

Time Interval

8:00 - 8:15 8:15 - 8:30

From: Maitidevi Leg (West)

From: Gausala Leg (North)

From: Sinamangal Leg (East)

To: Maitidevi (West)

To: Gausala (North)

To: Sinamangal (East)

To: New Baneshwor (South)

To: Sinamangal (East)

To: Gausala (North)

To: Maitidevi (West)

To: New Baneshwor (South)

To: Sinamangal (East)

Left Turn

Through

Right Turn

Right Turn

Through

Left Turn

Right Turn

Through

Left Turn

D1 MC/ Total HV LV HV Cycle (veh) 2 10 14 26 5 2 7 10 18 3

8:30 - 8:45

0

4

26

8:45 - 9:00

0

3

29

9:00 - 9:15

0

4

30

9:15 - 9:30

0

7

Total Total D2 D3 D4 D5 D6 D7 D8 MC/ Total MC/ Total MC/ MC/ Total MC/ Total MC/ Total MC/ LV HV LV HV LV Total HV LV HV LV HV LV HV LV Cycle (veh) Cycle (veh) Cycle Cycle (veh) Cycle (veh) Cycle (veh) Cycle 52 84 1 3 18 0 1 6 0 23 60 3 19 24 2 28 33 2 37 68 22 189 8 84 46 137 63 141 55 106 19 7 1 29 76 24 35 6 47 85 24 207 0 3 10 106 1 27 51 167 0 25 60 164 0 5

To: Gausala (North)

To: New Baneshwor (South)

Total Left Turn (veh) D10 D11 D12 MC/ Total MC/ Total MC/ Total HV LV HV LV HV LV Cycle (veh) Cycle (veh) Cycle (veh) 20 1 38 79 58 221 1 13 34 118 1 13 71 224

Total

D9 Total MC/ Total HV LV (veh) Cycle (veh) 1 10 39 108 50

To: Maitidevi (West)

Right Turn

Through

Total for the Intersection (veh)

Fifteen Minute Intersection Counts of Turning Movements From: New Baneshwor Leg (South)

771

137

0

17

55

72

269

1

13

31

45

3

50

72

124

4

11

61

76

245

186 0 211 0

5

13

17

234

1

2

8

11

2

31

58

91

0

20

26

46

148

2

27

47

76

5

37

102

144

1

12

61

73

293

1

18

29

48

2

43

78

122

3

20

63

86

256

931

2

14

16

259

0

2

10

12

0

33

77

110

0

20

38

57

179

2

23

58

83

2

60

147

209

1

15

57

73

366

0

10

38

48

0

56

107

163

1

21

78

99

310

1,114

229 0 203 0

2

6

8

271

0

1

9

10

1

28

56

85

0

20

30

49

144

1

22

51

74

4

42

153

200

1

17

71

89

363

1

10

36

47

1

34

139

174

1

17

78

96

316

1,095

148

2

18

20

258

0

1

9

10

1

34

78

114

0

21

34

55

179

1

27

47

75

6

57

181

244

1

19

85

106

424

1

16

37

53

0

51

163

214

1

21

88

110

377

1,238 1,294

30 5 32 2

59

123

69

140

34 2 35 2

65

161

28

53

888

9:30 - 9:45

0

5

36

41 2

52

147

201 1

1

14

15

257

0

2

14

16

1

31

101

133

1

25

40

66

215

1

27

46

74

1

58

199

258

1

20

77

98

430

0

15

34

49

0

58

155

213

0

16

116

132

393

9:45 - 10:00

0

7

49

56 1

55

172

228 0

2

16

19

302

0

1

14

15

0

31

106

137

0

23

37

60

212

0

28

41

69

2

53

209

264

0

19

91

110

443

0

15

39

54

0

58

170

229

0

19

94

113

396

1,353

10:00 - 10:15

0

10

42

43

182

2

11

13

293

0

1

14

15

1

31

92

124

0

23

52

74

214

0

27

56

83

3

64

222

289

1

17

111

129

501

0

15

43

58

0

50

137

187

0

16

79

94

340

1,348

10:15 - 10:30

0

7

46

52 2 53 3

56

156

228 0 215 0

1

12

13

281

0

3

16

19

0

35

93

128

0

23

50

73

220

0

30

44

74

1

60

199

261

0

21

110

130

465

0

6

43

49

0

60

146

207

0

14

81

95

351

1,317

10:30 - 10:45

1

5

35

40 1

54

186

240 0

2

12

14

294

1

1

13

15

1

40

97

138

0

27

53

80

233

0

32

81

113

3

55

197

255

1

18

111

129

497

0

13

37

50

0

52

135

187

0

17

98

115

353

1,376

10:45 - 11:00

0

9

41

50 1

50

168

219 0

3

15

18

288

0

2

9

11

0

30

90

120

0

28

53

82

213

0

39

86

125

3

62

175

240

0

19

101

120

485

0

15

40

55

1

53

144

198

1

17

89

107

360

5

78

385

2

29

168

3

18

131

152

8 377 984 1,369

590

9 603 1,525 2,136 11 202 982 1,195 3,922

AM Total:

468 30

663 1,773 2,466

199 3,133

6 275 459

740 2,260

9 335 623

968 40 631 1,937 2,608

9 205 967 1,180 4,756

5 159 426

1,346 14,072

16:00 - 16:15

2

4

40

45

4

49

178

231

0

5

16

22

298

1

5

21

26

2

30

101

133

2

24

40

66

226

0

25

38

62

5

43

109

157

3

23

89

115

335

1

13

30

45

1

46

97

144

2

15

85

101

290

1,149

16:15 - 16:30

1

10

38

49

4

73

184

261

2

5

17

24

334

0

3

20

23

3

39

101

143

1

21

37

59

225

0

26

35

61

3

52

132

187

4

19

90

114

361

3

13

27

42

2

46

113

161

1

16

66

83

287

1,207

16:30 - 16:45

0

8

43

51

1

58

164

223

1

6

23

30

304

0

3

25

29

1

37

104

142

3

22

31

55

226

0

23

44

68

3

53

136

191

1

19

78

98

357

2

12

31

45

2

50

126

178

2

20

72

94

317

1,204

16:45 - 17:00

0

9

42

52

2

68

184

255

1

7

21

29

335

0

1

22

23

1

33

109

143

0

23

44

67

233

1

21

48

70

3

58

145

206

2

21

105

128

404

1

21

27

49

2

46

111

159

0

11

79

90

298

1,270

17:00 - 17:15

1

14

39

53

3

65

191

259

1

5

24

29

341

0

4

30

34

1

31

119

151

0

22

48

70

255

0

23

40

63

3

50

146

198

0

21

99

120

381

2

15

29

46

0

49

113

162

2

17

74

92

300

1,277

17:15 - 17:30

0

10

45

55

2

61

212

275

0

6

18

23

353

0

3

25

28

0

33

132

165

0

14

60

74

268

1

25

38

64

2

51

169

222

1

31

129

160

447

0

12

35

47

0

44

130

174

0

17

84

101

322

1,390

17:30 - 17:45

41

47

1

64

206

271

0

5

19

24

342

0

3

23

26

0

34

150

184

0

20

39

60

270

0

20

36

56

5

46

161

212

407

324

1,343 1,392

1

5

1

24

114

140

0

13

40

53

1

58

127

186

0

16

69

85

17:45 - 18:00

0

10

49

59

6

139

166

310

0

2

19

21

390

0

4

27

31

0

31

136

168

0

16

64

80

279

0

27

42

69

3

51

149

202

0

24

124

148

419

0

16

32

48

0

48

127

175

0

14

65

80

303

18:00 - 18:15

1

9

51

61

6

137

154

297

0

4

26

30

388

0

5

26

31

1

30

148

179

0

22

49

71

282

0

22

30

52

2

57

145

203

0

20

119

140

394

0

11

32

43

1

48

116

165

0

15

74

89

297

1,361

18:15 - 18:30

0

9

54

63

7

125

131

262

0

7

27

34

359

0

7

25

32

1

33

159

192

1

16

52

69

293

1

18

43

62

2

45

143

189

0

18

120

139

390

0

9

32

41

1

36

102

139

0

17

73

90

270

1,313

18:30 - 18:45

0

13

48

61

6

143

138

288

0

5

26

31

379

0

6

20

26

0

43

163

206

0

15

52

68

300

0

23

31

54

0

45

125

170

1

21

117

139

363

0

15

36

51

1

47

74

122

0

19

59

78

251

1,293

18:45 - 19:00

0

11

53

65

5

145

117

267

0

6

33

39

371

0

5

20

25

0

39

129

169

0

19

57

75

269

0

30

41

72

2

47

121

170

1

24

120

145

387

0

13

33

46

1

53

82

136

0

19

71

90

272

1,298

543

660 47 1,128 2,024 3,199

269

335 4,195

284

334 10 413 1,552 1,975

573

815 3,125

466

753 32 596 1,681 2,309 14 266 1,305 1,585 4,647

386

556 11 571 1,319 1,901

PM Total:

5 112

5 61

2 48

8 234

3 284

9 161

8 194

871 1,073 3,529 15,495

(Source: Field survey, May 2018)

87

Appendix-1.3: Fifteen Minute Intersection Counts of Turning movements on Monday, May 7, 2018

Fifteen Minute Intersection Counts of Turning movements Site Name: Old Baneshwor Intersection Date: Monday, May 7, 2018

Time Interval

8:00 - 8:15 8:15 - 8:30 8:30 - 8:45 8:45 - 9:00

To: Sinamangal (East)

To: New Baneshwor (South)

To: Sinamangal (East)

To: Gausala (North)

To: Maitidevi (West)

To: New Baneshwor (South)

To: Sinamangal (East)

To: Gausala (North)

To: Maitidevi (West)

To: New Baneshwor (South)

Left Turn

Through

Right Turn

Right Turn

Through

Left Turn

Right Turn

Through

Left Turn

Right Turn

Through

Left Turn

0

3

29

9:15 - 9:30

0

6

24

9:30 - 9:45

0

6

32

9:45 - 10:00

0

4

45

10:00 - 10:15

0

10

33

10:15 - 10:30

0

12

32

10:30 - 10:45

0

7

33

10:45 - 11:00

0

6

41

79

16:00 - 16:15

1

4

16:15 - 16:30

1

11

16:30 - 16:45

0

6

16:45 - 17:00

0

8

17:00 - 17:15

0

9

17:15 - 17:30

0

10

17:30 - 17:45

0

5

17:45 - 18:00

1

12

18:00 - 18:15

1

9

18:15 - 18:30

0

9

18:30 - 18:45

0

8

0

9

18:45 - 19:00 PM Total:

From: Sinamangal Leg (East)

To: Gausala (North)

Total D1 D2 D3 D4 D5 MC/ Total MC/ Total MC/ Total MC/ MC/ HV LV HV LV HV LV HV LV Total HV LV Cycle (veh) Cycle (veh) Cycle (veh) Cycle Cycle 2 84 17 0 23 47 19 170 0 8 9 1 7 8 19 3 45 132 2 0 2 97 24 1 27 67 32 204 0 7 11 3 4 7 20 3 52 152 0 8 0 4 44 110 0 11 19 1 1 35 70 30 213 4 21 1 6 8 25 158 0 1 61 132 0 1 17 0 0 27 86 18 248 13 6 30 3 10 36 194

4

From: Gausala Leg (North)

To: Maitidevi (West)

9:00 - 9:15

AM Total:

From: Maitidevi Leg (West)

4 100

340

32 3 30 3

53

158

57

142

38 1 49 1

52

146

43

174

43 3 44 4

39

172

57

167

49

173

35

162

40 3 47 1

214 0 202 0

1

1

2

248

0

1

0

20

20

252

0

0

199 1 218 0

2

15

18

255

0

1

0

19

19

286

0

0

214 0 228 0

1

12

13

270

0

0

0

9

9

281

0

2

225 0 198 0

3

8

11

276

0

1

2

7

9

254

0

1

3

29

168

200 2,957

1

14

423 30 587 1,717 2,334

33 38 40 52

6

42

145

193

0

4

21

25

256

2

7

1

69

126

196

4

2

16

22

270

0

2

36 42 33 41

1

63

138

202

0

7

20

27

271

0

2

2

64

165

231

1

9

18

28

300

0

0

28 37 32 42

3

58

184

245

1

1

20

22

304

0

5

1

55

236

292

0

9

12

21

355

0

1

38 43 45 58

0

58

198

256

0

3

6

9

308

0

3

67

258

328

0

1

9

10

396

0

45 55 41 50

1

64

266

331

0

0

5

5

391

0

9

2

49

206

257

0

3

25

28

335

0

12

46 54 32 41

2

64

207

273

0

2

21

23

350

0

13

0

68

156

224

0

0

9

9

274

0

12

182

229 3,810

449

553 22 721 2,285 3,028

6 41

4 8

2 75

9 10 16 16

1

38

Total D6 D7 Total MC/ Total MC/ HV LV HV LV Cycle (veh) Cycle (veh) 3 18 13 70 34 112 1 24 21 1 27 20 48 150 0 95 27 36 106

0

17

22

39

153

1

113

0

23

35

58

184

0

34

46

85

0

20

23

43

138

3

27

28

1

37

83

121

0

17

20

37

174

0

37

13 14 12 12

1

30

105

136

0

24

37

61

211

1

25

0

37

102

139

0

27

35

62

213

0

27

13 13 18 20

1

33

91

125

0

20

47

67

205

0

28

0

27

82

109

0

22

48

70

199

0

32

11 12 13 14

1

39

98

138

0

24

34

58

208

0

33

0

36

86

122

0

28

43

71

207

0

44

132

48 78 45 82

3

51

2

46

51 77 51 78

0

57

4

61

69 97 58 90

3

57

2

59

67 100 66 110

3

35

6

72

7 389 963 1,359

4 267 377

648 2,154

6 366 599

19 28 12 14

0

26

107

133

3

24

36

63

224

0

22

1

33

106

140

0

21

36

57

211

0

18

21 23 11 11

1

36

105

142

2

21

36

59

224

0

18

1

31

112

144

1

23

45

69

224

0

13

19 24 27 28

1

24

133

158

0

22

26

48

230

0

16

0

36

134

170

0

11

56

67

265

2

25

38 42 39 47

0

24

117

141

0

13

39

52

235

0

15

0

28

111

139

0

25

57

82

268

0

20

36 45 32 44

0

23

154

177

0

24

57

81

303

0

24

1

36

135

172

0

13

44

57

273

1

18

32 45 23 35

0

54

156

210

1

14

61

76

331

0

12

0

38

82

120

0

18

55

73

228

0

22

548

784 3,016

309

147

386

5 389 1,452 1,846

7 229

3 223

Total

D8 D9 Total MC/ Total MC/ Total HV LV HV LV (veh) Cycle (veh) Cycle (veh) 46 2 26 54 82 1 12 40 53 63 5 42 46 93 1 20 50 71 45 74 6 51 82 139 0 14 89 103 42 76 1 49 99 149 1 16 59 76 2

20

1

14

88 110 74 89

211 268 241 306

0

15

55

0

12

277 337 231 292

1

12

0

21

178 216 180 258

2

23

0

20

971 37 606 1,991 2,634

50 72 40 58

7

55

3

47

38 56 48 61

1

52

3

57

34 50 27 54

2

46

3

42

37 52 47 67

5

43

4

43

34 58 35 54

1

48

3

43

29 41 27 49

1 2

446

177 231 215 263

D10 D11 D12 MC/ MC/ Total MC/ Total HV LV HV LV HV LV Cycle (veh) Cycle (veh) Cycle 181 1 10 27 38 0 33 72 105 1 13 34 227 1 11 21 33 5 35 73 113 4 14 52 316 0 22 27 49 0 36 66 102 3 20 64 301 0 11 43 54 0 50 101 151 1 21 74 419 0 0 187 187 1 10 53 1 11 40 52 434

1

18

415

0

14

491

0

14

570

1

12

131 152 111 136

534

0

5

452

0

11

119 139

507

0

7

70

95 107 123 136

9 199 1,034 1,242 4,847

112 174 110 160

6

29

3

17

102 155 124 184

1

17

2

20

108 156 136 181

0

18

0

35

135 183 139 186

1

26

0

24

0

19

1

21

31

94 143 137 183 72 104

0

19

51

72 125

2

34

41 60 25 39

0

28

0

53

29 43 42 55

1

42

0

36

45 50 46 57

0

53

1

46

41 48

1

38

5 146 427

98 133 90 110

379

1

9

328

2

20

87 105 104 126

316

3

16

371

1

20

97 115 168 203

321

3

17

438

0

12

125 152 153 177

387

0

18

430

0

14

136 155 137 159

356

0

11

396

0

6

113 132 161 197

277

0

20

371

0

12

672 35 558 1,341 1,934 16 279 1,469 1,764 4,370 10 175

578

0

24

4

54

29 48 33 54

2

52

1

49

31 51 29 41

1

55

0

61

43 61 30 44

1

73

0

59

29 40 25 31

0

56

0

31

23 43 15 27

2

57

2

66

336

Total (veh) 48 191

654

70

216

87

238

920

96

301

1,034

64

303

1,108

62 82 63 86

308

1,168

287

1,168

71 83 88 96

302

1,292

309

1,354 1,368

797

138 166 109 162

0

20

1

22

133 176 122 158

0

12

0

8

145 198 124 171

0

15 17

91 106 65 82

354

0

310

1,246

167 206

0

18

81 99

353

1,321

8 450 1,437 1,895 11 190 798

20 30 29 51

Total (veh)

Total for the Intersection

Fifteen Minute Intersection Counts of Turning Movements From: New Baneshwor Leg (South)

105 129 107 165

3

10

1

19

114 168 97 147

1

23

1

15

117 173 118 179

2

21

0

20

141 215 130 189

0

15

1

19

75 131 70 101

0

17

0

17

44 103 61 129

0

16

0

12

521 13 637 1,179 1,829

9 204

999 3,472

13,430

67 80 51 71

239

1,098

287

1,096

67 91 90 106

307

1,118

307

1,202

68 91 77 97

315

1,170

317

1,375

66 81 62 82

357

1,287

315

1,409

59 76 42 59

247

1,297

191

1,195

42 58 31 43

204

1,162

199

1,072

722

935 3,285 14,481

(Source: Field survey, May 2018)

88

Appendix-1.4: Fifteen Minute Intersection Counts of Turning movements on Tuesday, May 8, 2018

Fifteen Minute Intersection Counts of Turning movements Site Name: Old Baneshwor Intersection Date: Tuesday, May 8, 2018

Time Interval

To: Maitidevi (West)

To: Gausala (North)

Left Turn

Through

D1 MC/ HV LV Total HV Cycle 8:00 - 8:15 2 5 14 21 6 8:15 - 8:30 2 8 10 20 3 8:30 - 8:45

1

3

27

8:45 - 9:00

0

2

28

9:00 - 9:15

0

7

34

9:15 - 9:30

1

9

21

9:30 - 9:45

0

9:45 - 10:00

0

8

10:00 - 10:15 0 10:15 - 10:30 0 10:30 - 10:45 2

11

41

6

35

6

10:45 - 11:00 0

8

AM Total:

4

8

77

16:00 - 16:15

1

4

16:15 - 16:30

1

8

16:30 - 16:45

0

12

16:45 - 17:00

1

8

17:00 - 17:15

1

11

17:15 - 17:30

0

8

17:30 - 17:45

1

6

17:45 - 18:00

0

9

18:00 - 18:15

0

9

18:15 - 18:30

0

6

18:30 - 18:45

0

24

18:45 - 19:00

0

14

PM Total:

5 119

31 50

31 6 30 4 41 3 31 1 35 2 58 1 52 3

88

168

83

172

51

177

58

150

63

205

39

209

57

186

27

66

232

38

46 3

61

189

441 36

42 47 28 37 43 55 54 63 47 59 53 61 33 40 51 60 38 47 38 44 33 57 40 54 500

To: Sinamangal (East)

260 0 258 0

1

12

13

303

0

1

3

4

7

306

0

1

229 0 210 0

3

13

16

276

0

0

0

9

9

254

0

269 0 251 0

0

8

8

335

0

247 0 298 0

253 0 765 2,054 2,855 0

1 1

1

3

4

307

0

2

1

5

6

294

0

5

1

7

8

341

1

0

2

18

20

319

1

1

18

115

133 3,429

4

20

5

55

267

327

1

4

18

23

397

0

2

9

77

240

326

2

5

22

29

392

0

1

2

61

222

285

1

3

23

27

367

0

2

2

74

218

294

1

3

23

27

384

1

0

2

75

200

277

0

4

21

25

361

0

3

1

66

208

275

0

5

17

22

358

0

1

82

248

3

To: Sinamangal (East)

To: New Baneshwor (South)

From: Gausala Leg (North)

To: Gausala (North)

Right Turn Right Turn Through Left Turn Total D2 D3 D4 D5 D6 MC/ MC/ MC/ MC/ MC/ LV Total HV LV Total HV LV Total HV LV Total HV LV Cycle Cycle Cycle Cycle Cycle 63 92 11 0 25 71 4 18 30 12 194 1 96 2 4 7 161 0 1 53 132 17 2 23 74 1 23 25 20 228 0 99 2 10 12 188 0 3 8 2 27 46 0 23 32 83 142 10 272 1 75 4 12 17 231 0 2

41 4 35 0

356

From: Maitidevi Leg (West)

4

20

12 13 8 8 15 16 17 18 15 17 13 18 12 13 6 132

8

1

36

68

105

17

46

Right Turn

Through

Left Turn

D7 D8 D9 MC/ MC/ MC/ Total HV LV Total HV LV Total HV LV Total Cycle Cycle Cycle 52 155 2 40 62 104 1 44 101 146 2 11 41 54 49 160 1 21 51 73 6 55 139 200 0 19 52 71 55 147 1 27 65 93 9 39 102 150 0 9 41 50 63 177 2 15 103 120 5 73 161 239 1 17 66 84 66 158 1 15 54 70 10 67 128 205 1 19 68 88 65 185 3 1 20 94 115 16 54 73 3 67 179 249

0

22

57

79

0

28

38

1

31

80

112

0

27

38

1

32

86

119

1

24

47

72

0

27

110

137

0

18

35

53

207

1

27

36

64

2

63

208

0

27

31

58

0

55

1

25

107

133

0

20

52

72

222

0

18

34

52

5

74

1

41

112

154

0

24

41

65

237

0

26

22

48

2

64

1

49

96

146

0

28

57

85

244

1

33

0

30

99

129

0

33

66

99

236

0

40

156 10 368 1,006 1,384

15 17 12 13

0

To: Sinamangal (East)

110 144 115 155

5

69

3

50

144 197

6 283 507

796 2,336 12 305 737 1,054 51 720 1,868 2,639

4

33

98

135

2

22

52

76

228

0

33

30

63

4

36

5

39

101

145

1

26

36

63

221

1

34

32

67

2

45

12 14 9 10

0

37

96

133

4

26

22

52

199

0

28

48

76

5

49

2

33

92

127

0

26

42

68

205

1

24

55

80

4

60

21 24 16 17

2

35

113

150

0

25

56

81

255

1

30

46

77

2

46

0

32

133

165

0

12

55

67

249

0

17

41

58

2

52

1

11 12

0

291

4

307

0

2

26

28

395

0

2

36

145

182

0

11

78

89

281

0

33

46

79

3

46

13

323

0

0

36

36

406

0

1

8 10 11 12 25 27

1

295

1

31

138

170

1

16

54

71

253

0

30

24

54

4

60

397

16

277

5

298

0

7

23

30

372

0

2

17

313

5

335

0

4

11

15

407

0

2

14

300

10

324

0

2

41

43

421

0

0

281

329 4,657

624 98 1,966 1,640 3,704

5 43

1 17

12 14 16 16 168

36

163

199

0

25

39

64

275

0

24

38

62

4

50

1

36

186

223

1

14

68

83

333

0

13

48

61

1

27

1

39

169

209

0

16

56

72

295

0

32

26

58

0

59

0

43

174

217

0

26

83

109

342

0

30

63

93

2

29

3 328

497

186 17 430 1,608 2,055

198 263 168 223 180 259 172 238 196 270

12

24

0

9

To: New Baneshwor (South)

15

333

0

8

Total

9 245

641

From: Sinamangal Leg (East)

To: Maitidevi (West)

895 3,136

122 162 127 174 119 173

2

23

0

23

0

16

0

22

0 1

12

0

14

0

8

0 0

8 212 984 1,204 4,897

1

23

3

22

0

25

94 148 122 176

1

33

114 173 130 161

0

445 408 547

2

22

0

21

0

24

0

20

1

28

0

19

81 103 85 110 76 100

Left Turn

14

454

21

Through

0

85 102

20

Right Turn

408

17

1

To: New Baneshwor (South)

451

16

5

To: Maitidevi (West)

74 60

12

0

21

40 61

1

5 165 429 2

18

3

7

349

2

11

0

17

385

1

11

117 151 125 149

357

0

14

0

10

375

0

13

408

0

6

325

0

8

404

0

9

0

14

380

0

80

328

828 33 559 1,447 2,039 14 278 1,292 1,584 4,451

65

0

402

144 173 107 126

1

1

351

387

35 49 46 58 41 55 39 47 37 49

107 132 107 132

113 134 119 143 111 131

Total

D10 D11 D12 MC/ MC/ MC/ HV LV Total HV LV Total HV LV Total Cycle Cycle Cycle 304 1 16 8 25 2 49 100 151 0 16 91 107 344 1 11 36 48 2 64 83 149 3 7 81 91 293 1 17 31 49 2 52 80 134 3 19 74 96 443 0 13 39 52 0 69 115 184 1 26 89 116 363 1 9 37 47 1 56 98 155 0 15 97 112 437 1 18 40 59 1 68 185 254 1 20 127 148

118 134 100 122 116 133

126 190 128 176

113 162 147 211 105 133

99 124 104 127

Total

To: Gausala (North)

8 138

0

15

0

25

184 199 134 159

283

936

288

1,020

279

991

352

1,275

314

1,141

461

1,359

499

1,411

512

0

16

77

93

365

1,339

0

13

85

98

421

1,360

40

0

16

66

144 211

2

20

2

59

1

41

2

48

24 41 30 42

3

39

0

54

39 53 40 50

0

37

27 40 34 40 26 34 43 52 28 42

1

1,463

157 217 195 276 135 175

148 164 125 147

388

1,520

419

1,428

599 11 743 1,698 2,452 10 208 1,312 1,530 4,581

34 54 26 36 34 47

385

185 251 221 295

Total for the Intersection

Fifteen Minute Intersection Counts of Turning Movements From: New Baneshwor Leg (South)

64

0

38

0

46

2

44

0

39

0

45

109 170 115 157 132 182

3

20

1

18

126 149 90 109

373

1,326

302

1,266

95 122 89

351

1,266

274

1,265

2

25

102 144 125 179

0

7

82

1

13

83

145 182 131 196

1

10

96 107 75 95

133 171 151 197 143 189 88 127 99 144

531 11 554 1,473 2,038

0

20

15,243

97

318

1,319

342

1,306

341

1,400

0

8

79

87

298

1,349

0

13

86

99

336

1,403

0

20

1,382

25

109 129 81 106

352

0

285

1,391

0

28

115 143

329

8 207 1,117 1,332 3,901

1,472 16,145

(Source: Field survey, May 2018)

89

Appendix-1.5: Fifteen Minute Intersection Counts of Turning movements on Wednesday, May 9, 2018

Fifteen Minute Intersection Counts of Turning movements Site Name: Old Baneshwor Intersection Date: Wednesday May 9, 2018 Fifteen Minute Intersection Counts of Turning Movements

Time Interval

From: Maitidevi Leg (West)

To: Maitidevi (West)

To: Gausala (North)

To: Sinamangal (East)

Left Turn

Through

Right Turn

To: Sinamangal (East)

To: Gausala (North)

Right Turn

Through

Left Turn

Total

D1 D2 D3 MC/ MC/ MC/ HV LV Total HV LV Total HV LV Total Cycle Cycle Cycle 2 18 75 129 0 9 26 35 8:00 - 8:15 18 38 7 47 2 90 153 0 4 17 21 8:15 - 8:30 5 8 15 3 60 0 1 11 12 8:30 - 8:45 6 29 35 4 50 116 170 0

From: Gausala Leg (North)

To: New Baneshwor (South)

Total

D4 D5 D6 MC/ MC/ MC/ HV LV Total HV LV Total HV LV Total Cycle Cycle Cycle 62 28 0 1 8 1 22 85 3 20 51 202 7 87 124 1 30 26 0 3 11 1 36 57 189 8

From: Sinamangal Leg (East)

To: Maitidevi (West)

To: New Baneshwor (South)

To: Sinamangal (East)

Right Turn

Through

Left Turn

Total

8:45 - 9:00

0

1

29

30

2

57

120

179

0

3

14

17

226

0

1

12

13

0

35

77

112

0

19

32

51

9:00 - 9:15

0

2

28

30

0

60

154

214

1

1

14

16

260

0

0

6

6

2

25

65

92

0

11

28

39

9:15 - 9:30

0

5

39

44

2

52

125

179

1

2

20

23

246

0

2

3

5

1

35

72

108

1

19

44

64

D8 D9 D7 MC/ MC/ MC/ Total HV LV Total HV LV Total HV LV Cycle Cycle Cycle 39 4 41 95 1 44 144 2 21 16 50 8 35 43 6 43 75 192 0 26 17 69 118 0 12 63 143 3 25 60 1 20 67 32 122 143 2 13 52 54 1 58 59 176 4 21 29 181 240 2 12 45 137 0 24 75 0 70 51 9 154 163 1 12 57 70 13 58 177 0 28 42 148 219 2 23 88 113

217

1

2

5

8

2

31

58

91

0

20

24

44

To: Gausala (North)

To: Maitidevi (West)

To: New Baneshwor (South)

Right Turn

Through

Left Turn

Total

D10 D11 D12 MC/ MC/ MC/ HV LV Total HV LV Total HV LV Total Cycle Cycle Cycle 178 40 1 32 99 59 1 14 25 66 1 10 48 236 53 2 50 67 1 17 35 59 111 5 13 49 270 47 3 40 75 1 16 30 87 130 4 20 51 353 37 0 49 106 155 86 0 5 32 0 16 70 308 41 1 46 132 179 1 10 30 1 27 83 111 402 41 0 56 166 222 0 12 29 1 23 76 100

198

Total for the Intersection

From: New Baneshwor Leg (South)

722

231

848

252

882

278

1,033

331

1,036

363

1,188

9:30 - 9:45

0

5

45

50

3

46

144

193

1

0

17

18

261

0

3

15

18

1

31

112

144

1

26

37

64

226

0

30

50

80

1

54

189

244

0

23

76

99

423

1

16

41

58

0

55

170

225

0

11

100

111

394

1,304

9:45 - 10:00

0

10

51

61

0

59

137

196

0

7

22

29

286

0

1

14

15

0

30

105

135

0

23

41

64

214

0

29

41

70

2

43

219

264

0

23

73

96

430

0

19

41

60

0

59

157

216

1

19

78

98

374

1,304

10:00 - 10:15

0

10

51

61

1

52

166

219

0

5

18

23

303

0

2

14

16

0

36

79

115

0

28

56

84

215

0

36

64

100

1

62

208

271

1

24

91

116

487

0

18

47

65

0

55

131

186

0

23

71

94

345

1,350

10:15 - 10:30

0

4

70

74

1

55

115

171

0

2

21

23

268

0

2

18

20

0

37

84

121

0

24

60

84

225

0

33

51

84

0

57

195

252

0

19

98

117

453

1

6

44

51

0

47

99

146

0

15

66

81

278

1,224

10:30 - 10:45

0

1

45

46

0

46

152

198

0

2

20

22

266

2

1

17

20

0

32

97

129

0

30

67

97

246

0

29

65

94

1

60

218

279

0

13

105

118

491

0

17

28

45

0

71

145

216

0

18

81

99

360

1,363

10:45 - 11:00

0

12

45

57

0

53

154

207

0

5

21

26

290

0

3

9

12

0

24

84

108

1

24

51

76

196

1

33

76

110

1

63

200

264

0

21

99

120

494

0

17

39

56

0

55

122

177

0

13

62

75

308

1,288

AM Total:

4

79

458

541 23 637 1,548 2,208

3

41

221

3 21

128

152

7 274

494

775 2,291 10 335

534

6 167

421

594

835 1,056 3,712

13,542

265 3,014

8 374

982 1,364

879 31 568 1,953 2,552

9 203

882 1,094 4,525

7 615 1,440 2,062 13 208

16:00 - 16:15

3

4

44

51

2

51

121

174

0

7

10

17

242

0

5

28

33

2

31

99

132

2

25

33

60

225

0

19

33

52

5

38

93

136

2

20

88

110

298

1

12

37

50

0

54

78

132

0

14

61

75

257

1,022

16:15 - 16:30

1

10

47

58

3

72

186

261

1

7

12

20

339

1

6

36

43

3

44

97

144

1

16

40

57

244

0

26

32

58

5

63

158

226

5

20

96

121

405

3

12

25

40

1

42

118

161

2

10

58

70

271

1,259

16:30 - 16:45

0

6

51

57

0

50

131

181

1

7

27

35

273

0

6

43

49

2

37

112

151

3

18

34

55

255

1

24

47

72

2

57

187

246

2

17

70

89

407

1

8

31

40

3

49

132

184

3

11

54

68

292

16:45 - 17:00

0

11

40

51

2

67

170

239

1

9

21

31

321

0

2

46

48

0

36

122

158

0

19

45

64

270

1

26

41

68

3

58

184

245

0

22

104

126

439

1

25

25

51

1

51

134

186

0

11

65

76

313

1,343

17:00 - 17:15

1

21

41

63

3

62

189

254

1

9

31

41

358

0

3

51

54

0

33

111

144

0

20

61

81

279

0

22

40

62

4

57

202

263

1

19

92

112

437

2

17

26

45

0

37

96

133

2

16

71

89

267

1,341

17:15 - 17:30

0

12

49

61

4

63

192

259

0

3

24

27

347

0

7

32

39

0

31

130

161

1

20

68

89

289

0

34

47

81

0

60

278

338

1

24

102

127

546

0

9

37

46

0

34

127

161

0

21

79

100

307

1,489

17:30 - 17:45

1

5

53

59

0

53

172

225

0

7

31

38

322

0

5

19

24

1

42

169

212

1

22

40

63

299

0

20

34

54

5

44

227

276

0

25

93

118

448

0

11

38

49

0

36

110

146

0

12

66

78

273

1,342

17:45 - 18:00

0

9

51

60

2

58

235

295

0

3

22

25

380

0

3

34

37

0

30

153

183

0

13

56

69

289

0

28

33

61

1

64

194

259

0

28

105

133

453

0

20

40

60

1

48

117

166

0

15

55

70

296

1,418

18:00 - 18:15

1

9

70

80

2

53

183

238

0

13

37

50

368

1

5

31

37

1

37

152

190

0

25

37

62

289

0

12

31

43

0

62

193

255

0

18

103

121

419

0

15

34

49

2

42

122

166

0

15

77

92

307

1,383

18:15 - 18:30

0

12

82

94

2

48

181

231

0

11

33

44

369

0

6

18

24

0

27

155

182

1

22

45

68

274

1

24

46

71

1

64

187

252

0

14

113

127

450

0

13

46

59

1

34

93

128

0

14

67

81

268

1,361

18:30 - 18:45

0

7

64

71

0

53

203

256

0

8

46

54

381

0

3

17

20

0

35

163

198

0

16

39

55

273

0

26

37

63

0

45

189

234

1

16

94

111

408

0

15

42

57

0

45

91

136

0

15

55

70

263

1,325

18:45 - 19:00

0

11

88

99

1

67

185

253

0

16

49

65

417

0

2

21

23

0

37

132

169

0

12

32

44

236

0

39

34

73

3

60

162

225

0

18

93

111

409

0

12

57

69

0

49

87

136

0

16

67

83

288

1,350

PM Total:

7 117

680

4 100

343

2 53

376

431

9 228

530

3 300

455

8 169

438

615

7 170

775

952 3,402

15,860

804 21 697 2,148 2,866

447 4,117

9 420 1,595 2,024

767 3,222

758 29 672 2,254 2,955 12 241 1,153 1,406 5,119

9 521 1,305 1,835

1,227

(Source: Field survey, May 2018)

90

Appendix-1.6: 15 minute Interval Intersection Total Demand (Arrival) Volume at the U/S of intersection Queues Site Name: Old Baneshwor Intersection Intersection Demand Volume per 15 Minute Interval Time Interval

May 7, 2018

May 8, 2018

May 9, 2018

Monday

Tuesday

Wednesday

Veh.

PCU

Veh.

PCU

Veh.

PCU

3 days Average Veh.

731

424

701

399

738

426.0

723

416.4

7:45 - 8:00

738

411

774

449

761

447.0

758

435.4

8:00 - 8:15

760

425

829

437

802

463.3

797

441.7

8:15 - 8:30

945

517

967

522

940

521.5

951

520.2

8:30 - 8:45

1,046

525

1,074

550

1,015

538.8

1,045

537.7

8:45 - 9:00

1,215

555

1,200

563

1,042

499.3

1,152

538.9

9:00 - 9:15

1,200

579

1,215

574

1,181

552.3

1,199

568.3

9:15 - 9:30

1,332

584

1,338

602

1,323

608.3

1,331

598.3

9:30 - 9:45

1,368

585

1,615

693

1,414

620.0

1,466

632.6

9:45 - 10:00

1,412

596

1,590

674

1,547

641.8

1,516

637.3

10:00 - 10:15

1,397

579

1,663

679

1,508

643.5

1,523

633.9

10:15 - 10:30

1,280

566

1,482

628

1,388

604.8

1,383

599.7

10:30 - 10:45

1,288

588

1,349

617

1,269

562.3

1,302

588.9

10:45 - 11:00

1,219

541

1,482

659

1,309

590.8

1,337

596.9

11:00 - 11:15

1,401

638

1,365

639

1,459

671.8

1,408

649.3

11:15 - 11:30

1,210

564

1,328

601

1,239

579.8

1,259

581.3

18,542

8,675

19,972

9,285

18,935

8,971

19,150

8,977

15:30 - 15:45

1,187

606.0

1,161

587

1,174

618.8

1,174

603.8

15:45 - 16:00

1,263

581.5

1,178

571

1,177

567.5

1,206

573.3

16:00 - 16:15

1,209

602.0

1,178

589

1,214

645.0

1,200

612.1

16:15 - 16:30

1,128

548.3

1,221

595

1,200

569.3

1,183

570.7

16:30 - 16:45

1,189

552.0

1,207

584

1,235

581.5

1,210

572.4

16:45 - 17:00

1,262

599.5

1,173

558

1,259

574.5

1,231

577.3

17:00 - 17:15

1,337

579.5

1,395

618

1,350

601.3

1,361

599.7

17:15 - 17:30

1,293

554.0

1,343

564

1,332

569.5

1,323

562.6

17:30 - 17:45

1,471

639.8

1,321

596

1,388

603.8

1,393

613.0

17:45 - 18:00

1,434

629.0

1,355

593

1,372

599.5

1,387

607.2

18:00 - 18:15

1,348

598.0

1,288

566

1,284

583.8

1,307

582.7

18:15 - 18:30

1,308

568.3

1,435

617

1,311

596.3

1,351

593.7

18:30 - 18:45

1,072

526.3

1,330

561

1,252

547.5

1,218

544.8

18:45 - 19:00

1,068

556.5

1,308

564

1,220

541.5

1,199

553.9

19:00 - 19:15

1,165

567.5

1,194

513

1,244

550.3

1,201

543.4

19:15 - 19:30

1,065

487.3

1,195

529

1,178

510.8

1,146

508.9

19,799

9,195

20,282

9,202

20,190

9,261

20,090

9,219

PM Total:

PHF

         2,503

  0.98

PCU

7:30 - 7:45

AM Total:

Peak Hour Volume in PCU

Source: Field survey, May 2018

91

Appendix-1.7:Hourly Intersection Total Demand (Arrival) Volume at the U/S of intersection Queues

Site Name: Old Baneshwor Intersection Hourly Vehicle Deamand (Arrival) Volume for the Intersection Hour Interval

May 7, 2018

May 8, 2018

May 9, 2018

Monday

Tuesday

Wednesday

Veh.

PCU

Veh.

PCU

Veh.

PCU

3 days Average Veh.

PCU

7:30 - 8:30

3,174

1,777

3,271

1,807

3,241

1,858

3,229

1,814

7:45 - 8:45

3,489

1,877

3,644

1,957

3,518

1,971

3,550

1,935

8:00 - 9:00

3,966

2,021

4,070

2,071

3,799

2,023

3,945

2,038

8:15 - 9:15

4,406

2,176

4,456

2,208

4,178

2,112

4,347

2,165

8:30 - 9:30

4,793

2,243

4,827

2,289

4,561

2,199

4,727

2,243

8:45 - 9:45

5,115

2,303

5,368

2,432

4,960

2,280

5,148

2,338

9:00 - 10:00

5,312

2,344

5,758

2,543

5,465

2,422

5,512

2,436

9:15 - 10:15

5,509

2,344

6,206

2,649

5,792

2,514

5,836

2,502

9:30 - 10:30

5,457

2,326

6,350

2,675

5,857

2,510

5,888

2,503

9:45 - 10:45

5,377

2,329

6,084

2,598

5,712

2,452

5,724

2,460

10:00 - 11:00

5,184

2,274

5,976

2,584

5,474

2,401

5,545

2,419

10:15 - 11:15

5,188

2,332

5,678

2,543

5,425

2,430

5,430

2,435

10:30 - 11:30

5,118

2,330

5,524

2,515

5,276

2,405

5,306

2,416

15:30 - 16:30

4,787

2,338

4,738

2,341

4,765

2,401

4,763

2,360

15:45 - 16:45

4,789

2,284

4,784

2,338

4,826

2,363

4,800

2,328

16:00 - 17:00

4,788

2,302

4,779

2,326

4,908

2,370

4,825

2,333

16:15 - 17:15

4,916

2,279

4,996

2,355

5,044

2,327

4,985

2,320

16:30 - 17:30

5,081

2,285

5,118

2,324

5,176

2,327

5,125

2,312

16:45 - 17:45

5,363

2,373

5,232

2,336

5,329

2,349

5,308

2,353

17:00 - 18:00

5,535

2,402

5,414

2,371

5,442

2,374

5,464

2,382

17:15 - 18:15

5,546

2,421

5,307

2,319

5,376

2,357

5,410

2,365

17:30 - 18:30

5,561

2,435

5,399

2,371

5,355

2,383

5,438

2,397

17:45 - 18:45

5,162

2,322

5,408

2,337

5,219

2,327

5,263

2,328

18:00 - 19:00

4,796

2,249

5,361

2,307

5,067

2,269

5,075

2,275

18:15 - 19:15

4,613

2,219

5,267

2,254

5,027

2,236

4,969

2,236

18:30 - 19:30

4,370

2,138

5,027

2,166

4,894

2,150

4,764

2,151

Source: Field survey, May 2018

92

Appendix-1.8:Fifteen Minute Interval Vehicle Arrival Counts (Counted at upstream of the Intersection queues) on Monday, May 7, 2018 Site Name: Old Baneshwor Intersection Date: Monday, May 7, 2018 North (Gausala Leg) Time Interval LV with Veh. PCU 2-Wheeler in PCU 7:30 - 7:45 252 149.8 121.0 7:45 - 8:00 231 138.5 110.0 8:00 - 8:15 257 143.0 125.0 8:15 - 8:30 312 182.3 152.0 8:30 - 8:45 338 170.3 159.0 8:45 - 9:00 375 177.8 166.0 9:00 - 9:15 385 195.8 169.0 9:15 - 9:30 432 187.3 178.0 9:30 - 9:45 425 193.8 183.0 9:45 - 10:00 431 192.8 187.0 10:00 - 10:15 487 210.3 197.0 10:15 - 10:30 424 200.0 186.0 10:30 - 10:45 420 206.5 185.0 10:45 - 11:00 385 180.3 172.0 11:00 - 11:15 478 216.8 199.0 11:15 - 11:30 369 174.5 170.0 AM Total: 6,001 2,919 2,659 15:30 - 15:45 15:45 - 16:00 16:00 - 16:15 16:15 - 16:30 16:30 - 16:45 16:45 - 17:00 17:00 - 17:15 17:15 - 17:30 17:30 - 17:45 17:45 - 18:00 18:00 - 18:15 18:15 - 18:30 18:30 - 18:45 18:45 - 19:00 19:00 - 19:15 19:15 - 19:30 PM Total:

356 301 343 307 239 317 294 318 374 306 292 379 245 278 274 251 4,874

193.0 141.3 190.8 141.0 109.5 148.8 129.3 140.5 170.0 141.8 136.5 172.3 122.5 149.0 122.8 125.5 2,334

171.0 131.0 166.0 132.0 106.0 137.0 126.0 126.0 149.0 132.0 133.0 161.0 119.0 141.0 119.0 119.0 2,168

12.0 12.0 6.0 13.0 5.0 4.0 11.0 3.0 3.0 1.0 4.0 4.0 8.0 2.0 7.0 1.0 96

Leg Demand Volume South (New Baneshwor Leg) East (Sinamangal Leg) LV with LV with Veh. PCU 2-Wheeler HV Veh. PCU 2-Wheeler in PCU in PCU 177 97.0 86.0 4.0 173 99.0 78 176 86.5 81.0 1.0 198 108.5 91.0 184 102.3 91.0 4.0 206 113.3 95.0 216 117.8 103.0 5.0 248 119.8 104.0 230 116.8 106.0 5.0 319 147.0 137.0 278 126.3 117.0 3.0 361 159.5 151.0 274 130.3 124.0 3.0 374 157.8 152.0 286 122.8 122.0 420 183.8 179.0 298 130.3 120.0 4.0 428 166.5 164.0 313 125.0 120.0 3.0 416 161.8 159.0 285 113.0 110.0 1.0 392 151.3 149.0 296 133.8 127.0 3.0 364 145.0 143.0 301 128.8 123.0 4.0 329 138.5 138.0 139.0 269 108.5 105.0 1.0 336 142.0 331 156.0 146.0 5.0 363 162.3 162.0 297 142.3 132.0 6.0 324 145.0 142.0 4,211 1,937 1,813 52 5,251 2,301 2,183

West (Maitidevi Leg) LV with HV Veh. PCU 2-Wheeler in PCU 9 129 78.3 66 7.0 133 77.0 68.0 8.0 113 66.5 61.0 7.0 169 97.5 86.0 4.0 159 90.5 79.0 4.0 201 91.0 89.0 2.0 167 95.5 89.0 2.0 194 90.5 88.0 94.0 90.0 1.0 217 1.0 252 116.5 114.0 1.0 233 104.5 101.0 1.0 196 87.5 87.0 1.0 238 113.8 110.0 1.0 229 110.0 108.0 229 102.5 101.0 1.0 220 102.0 100.0 50 3,079 1,518 1,437

Total Intersection Demand Volume LV with HV Veh. PCU 2-Wheeler HV in PCU 4 731 424.0 351 29 3.0 738 410.5 350 23 2.0 760 425.0 372 20 4.0 945 517.3 445 29 4.0 1,046 524.5 481 18 1,215 554.5 523 11 2.0 1,200 579.3 534 18 1,332 584.3 567 5 1.0 1,368 584.5 557 9 1,412 596.0 580 5 1.0 1,397 579.0 557 7 1,280 566.3 543 8 1.0 1,288 587.5 556 14 1,219 540.8 524 4 1,401 637.5 608 12 1,210 563.8 544 8 22 18,542 8,675 8,092 220

10.0 5.0 11.0 4.0 1.0 4.0 2.0 7.0 8.0 4.0 2.0 4.0 1.0 3.0 1.0 2.0 69

298 392 306 309 311 355 384 362 424 423 433 402 331 292 345 314 5,681

3.0 3.0 5.0 4.0 8.0 7.0 3.0 4.0 3.0 1.0 1.0 3.0 45

2.0 6.0 5.0 1.0 4.0 2.0 1.0 1.0 1.0 23

HV

146.3 177.5 160.0 157.5 147.5 177.8 161.5 152.3 169.5 174.5 185.8 166.5 142.5 136.8 143.5 148.8 2,548

125.0 167.0 132.0 138.0 139.0 163.0 154.0 149.0 169.0 167.0 181.0 156.0 142.0 136.0 142.0 145.0 2,405

8.0 5.0 12.0 8.0 3.0 6.0 4.0 1.0 3.0 2.0 5.0 1.0 1.0 59

287 326 335 291 399 355 416 356 436 409 356 203 197 232 241 236 5,075

140.8 151.5 142.0 147.3 179.3 168.8 183.3 159.5 210.5 194.8 168.5 93.0 117.3 140.3 164.0 104.0 2,465

93

132.0 143.0 129.0 136.0 159.0 151.0 175.0 159.0 200.0 190.0 167.0 90.0 114.0 135.0 164.0 104.0 2,348

246 244 225 221 240 235 243 257 237 296 267 324 299 266 305 264 4,169

126.0 111.3 109.3 102.5 115.8 104.3 105.5 101.8 89.8 118.0 107.3 136.5 144.0 130.5 137.3 109.0 1,849

120.0 96.0 96.0 98.0 105.0 98.0 102.0 100.0 89.0 118.0 107.0 137.0 141.0 130.0 137.0 109.0 1,783

1,187 1,263 1,209 1,128 1,189 1,262 1,337 1,293 1,471 1,434 1,348 1,308 1,072 1,068 1,165 1,065 19,799

606.0 548 23 581.5 537 19 602.0 523 33 548.3 504 17 552.0 509 16 599.5 549 19 579.5 557 10 554.0 534 9 639.8 607 12 629.0 607 10 598.0 588 4 568.3 544 10 526.3 516 3 556.5 542 6 567.5 562 2 487.3 477 3 9,195 8,704 196 Soruce: Field survey, 2018

Appendix-1.9:Fifteen Minute Interval Vehicle Arrival Counts (Counted at upstream of the Intersection queues) on Tuesday, May 8, 2018 Site Name: Old Baneshwor Intersection Date: Tuesday, May 8, 2018

7:30 - 7:45 7:45 - 8:00 8:00 - 8:15 8:15 - 8:30 8:30 - 8:45 8:45 - 9:00 9:00 - 9:15 9:15 - 9:30 9:30 - 9:45 9:45 - 10:00 10:00 - 10:15 10:15 - 10:30 10:30 - 10:45 10:45 - 11:00 11:00 - 11:15 11:15 - 11:30 AM Total:

238 239 235 307 306 373 360 465 556 566 636 412 417 453 383 402 6,348

North (Gausala) LV with PCU 2-Wheeler HV in PCU 141.5 113 11 154.0 122 14 122.0 108 6 182.5 151 13 172.3 149 9 192.0 167 10 185.5 167 7 217.0 198 8 235.5 223 4 259.5 247 4 254.0 241 5 177.3 167 4 195.5 174 8 208.5 201 2 186.0 173 7 183.3 175 4 3,066 2,776 116

15:30 - 15:45 15:45 - 16:00 16:00 - 16:15 16:15 - 16:30 16:30 - 16:45 16:45 - 17:00 17:00 - 17:15 17:15 - 17:30 17:30 - 17:45 17:45 - 18:00 18:00 - 18:15 18:15 - 18:30 18:30 - 18:45 18:45 - 19:00 19:00 - 19:15 19:15 - 19:30 PM Total:

322 324 338 317 338 315 372 356 393 392 332 373 371 319 314 297 5,473

172.0 169.0 170.5 154.5 172.3 173.8 178.3 164.5 203.8 188.8 174.3 163.5 157.8 144.5 151.0 142.8 2,681

Time Interval Veh.

156 153 142 142 152 150 161 152 191 176 162 157 153 138 146 143 2,474

8 5 10 4 7 10 7 4 4 4 4 2 1 2 2 74

Leg Demand Volume South (New Baneshwor) East (Sinamangal) LV with LV with Veh. PCU 2-Wheeler HV Veh. PCU 2-Wheeler HV in PCU in PCU 179 93.3 91 178 99 80 8 190 97.8 77 8 222 123 100 9 201 110.3 98 4 235 121 110 4 238 123.5 111 5 264 134 109 10 270 127.0 124 1 345 159 148 4 275 131.0 126 1 369 157 146 4 295 136.5 132 1 386 165 162 1 259 112.3 106 2 422 180 172 4 331 151.5 145 2 500 200 198 1 302 124.8 121 1 501 197 193 2 290 124.3 116 3 494 200 193 4 340 150.5 140 4 460 171 167 2 319 142.3 134 3 360 156 146 6 179 173 4 339 157.0 149 3 429 355 162.3 162 395 180 168 5 324 151.5 146 2 367 151 150 4,507 2,096 1,978 40 5,927 2,571 2,415 68

106 123 158 158 153 183 174 192 228 221 243 270 253 261 232 235 3,190

299 282 305 343 327 349 396 362 368 379 339 408 355 375 340 332 5,559

211 242 236 251 230 210 290 258 249 280 290 331 302 312 269 301 4,262

154.3 128.3 142.8 168.5 150.0 151.5 170.5 143.3 154.5 164.3 150.3 172.8 155.8 153.8 139.0 140.5 2,440

129 124 127 148 141 144 166 140 150 151 144 162 149 150 129 138 2,292

10 1 6 8 4 3 2 1 2 5 2 5 3 2 5 1 60

329 330 299 310 312 299 337 367 311 304 327 323 302 302 271 265 4,988

149 148 147 144 140 136 136 149 131 122 128 144 119 126 108 116 2,143

94

136 143 131 127 129 126 129 146 128 122 126 139 119 126 107 116 2,050

5 2 6 7 4 4 3 1 1 1 2 1 37

Veh.

West (Maitidevi) LV with PCU 2-Wheeler in PCU 66.0 60 74.3 61 83.5 73 82.0 73 91.3 84 83.0 79 87.0 84 92.8 88 106.3 101 92.5 91 101.0 98 129.0 125 123.5 118 114.5 111 110.8 109 115.3 108 1,553 1,463 111.5 125.5 128.8 127.8 121.3 97.3 133.3 107.5 106.3 118.0 114.3 136.0 128.0 139.8 114.5 129.5 1,939

107 115 111 113 109 94 128 103 106 112 111 133 122 138 114 127 1,843

Total Intersection Demand Volume LV with HV Veh. PCU 2-Wheeler in PCU 2 701 399.3 344 5 774 448.8 360 4 829 436.8 389 3 967 521.8 444 2 1,074 549.8 505 1 1,200 563.0 518 1,215 573.5 545 1 1,338 602.3 564 2 1,615 693.3 667 1,590 674.0 652 1 1,663 679.3 648 1 1,482 628.0 599 2 1,349 617.0 572 1 1,482 659.3 634 1,365 638.5 612 2 1,328 600.5 579 27 19,972 9,285 8,632 1 3 7 5 4 1 2 2 2 1 1 2 1 32

1,161 1,178 1,178 1,221 1,207 1,173 1,395 1,343 1,321 1,355 1,288 1,435 1,330 1,308 1,194 1,195 20,282

HV 21 36 18 31 16 16 9 15 9 7 13 11 19 10 12 8 251

586.5 528 24 570.8 535 11 589.3 511 29 594.5 530 24 583.8 531 19 558.0 514 18 618.3 584 14 564.3 541 8 595.5 575 7 593.0 561 11 566.3 543 8 616.5 591 10 560.8 543 6 563.8 552 4 512.5 496 8 528.8 524 2 9,202 8,659 203 Soruce: Field survey, 2018

Appendix-1.10:Fifteen Minute Interval Vehicle Arrival Counts (Counted at upstream of the Intersection queues) on Wednesday, May 9, 2018 Site Name: Old Baneshwor Intersection Date: Wednesday, May 9, 2018 Time Interval Veh. 7:30 - 7:45 7:45 - 8:00 8:00 - 8:15 8:15 - 8:30 8:30 - 8:45 8:45 - 9:00 9:00 - 9:15 9:15 - 9:30 9:30 - 9:45 9:45 - 10:00 10:00 - 10:15 10:15 - 10:30 10:30 - 10:45 10:45 - 11:00 11:00 - 11:15 11:15 - 11:30 AM Total:

263 251 244 289 326 323 362 417 470 482 480 452 369 444 469 322 5,963

15:30 - 15:45 15:45 - 16:00 16:00 - 16:15 16:15 - 16:30 16:30 - 16:45 16:45 - 17:00 17:00 - 17:15 17:15 - 17:30 17:30 - 17:45 17:45 - 18:00 18:00 - 18:15 18:15 - 18:30 18:30 - 18:45 18:45 - 19:00 19:00 - 19:15 19:15 - 19:30 PM Total:

346 356 323 352 344 331 342 368 367 326 317 348 285 284 248 302 5,239

Approach Demand Volume North (Gausala) South (New Baneshwor) East (Sinamangal) West (Maitidevi) LV with LV with LV with LV with PCU 2-Wheeler HV Veh. PCU 2-Wheeler HV Veh. PCU 2-Wheeler HV Veh. PCU 2-Wheeler HV in PCU in PCU in PCU in PCU 150.8 127 9 182 105.5 91 6 186 104.5 87 7 107 65.3 63 1 161.8 125 14 196 104.5 99 2 203 111.5 92 8 111 69.3 54 7 152.0 121 11 192 121.5 94 11 217 111.3 94 8 149 78.5 69 4 163.5 143 7 230 123.3 114 4 247 135.0 115 8 174 99.8 90 3 188.3 156 11 236 108.8 102 3 287 140.8 123 7 166 101.0 87 6 168.5 146 8 235 111.8 108 1 325 139.8 131 4 159 79.3 77 175.3 160 5 290 134.3 128 2 386 175.0 162 7 143 67.8 64 1 211.3 189 8 262 117.8 112 2 425 167.3 165 1 219 112.0 100 5 100 1 204.3 197 3 293 126.0 119 3 436 185.5 181 2 215 104.3 196.3 192 1 323 131.0 129 1 540 219.5 214 4 202 95.0 92 1 207.8 201 2 341 145.3 144 470 183.8 184 217 106.8 102 2 196.0 192 1 306 133.0 126 2 423 171.8 170 1 207 104.0 100 2 164.0 158 2 277 112.8 112 388 171.3 159 8 235 114.3 109 2 149 6 218 101.0 94 3 209.3 200 4 281 122.0 120 1 366 158.5 211.0 205 2 322 145.0 142 1 381 163.5 153 6 287 152.3 144 3 155.0 147 4 331 141.5 141 385 177.0 171 3 201 106.3 98 3 2,915 2,659 92 4,297 1,984 1,881 39 5,665 2,516 2,350 80 3,010 1,557 1,443 44 191.8 177.8 178.8 172.8 174.5 160.0 162.8 169.8 171.0 162.3 164.3 183.3 149.3 147.3 122.0 139.8 2,627

180 169 149 160 160 139 151 162 159 152 158 170 139 135 120 137 2,440

4 3 11 5 6 8 4 3 4 3 2 5 4 4 1 1 68

316 309 349 299 325 323 369 364 380 384 381 355 382 362 353 353 5,604

168.8 143.3 187.8 145.3 148.0 146.0 170.8 153.5 161.0 154.0 164.8 150.0 156.3 163.0 156.8 146.5 2,516

141 132 158 131 139 136 162 143 153 151 154 145 156 159 157 144 2,361

12 5 15 5 5 5 4 4 4 1 5 2 2 1 70

291 309 299 319 330 359 390 322 320 335 293 310 296 318 352 251 5,094

153.3 145.0 155.0 136.0 149.8 163.5 165.5 128.3 135.0 156.3 124.8 138.0 126.3 128.3 137.5 117.8 2,260

95

137 136 131 126 129 151 152 124 131 151 112 134 123 128 138 115 2,118

7 4 12 4 10 6 7 2 2 3 7 2 2 1 69

221 203 243 230 236 246 249 278 321 327 293 298 289 256 291 272 4,253

105.0 101.5 123.5 115.3 109.3 105.0 102.3 118.0 136.8 127.0 130.0 125.0 115.8 103.0 134.0 106.8 1,858

94 90 107 102 98 104 101 115 133 124 128 119 115 102 131 106 1,769

4 4 6 5 4 1 1 1 1 2 1 30

Total Intersection Demand Volume LV with Veh. PCU 2-Wheeler in PCU 738 426.0 368.0 761 447.0 370.0 802 463.3 378.0 940 521.5 462.0 1,015 538.8 468.0 1,042 499.3 462.0 1,181 552.3 514.0 1,323 608.3 566.0 1,414 620.0 597.0 1,547 641.8 627.0 1,508 643.5 631.0 1,388 604.8 588.0 1,269 562.3 538.0 1,309 590.8 563.0 1,459 671.8 644.0 1,239 579.8 557.0 18,935 8,971 8,333 1,174 1,177 1,214 1,200 1,235 1,259 1,350 1,332 1,388 1,372 1,284 1,311 1,252 1,220 1,244 1,178 20,190

HV 23.0 31.0 34.0 22.0 27.0 13.0 15.0 16.0 9.0 7.0 4.0 6.0 12.0 14.0 12.0 10.0 255

618.8 552.0 27.0 567.5 527.0 16.0 645.0 545.0 44.0 569.3 519.0 19.0 581.5 526.0 25.0 574.5 530.0 19.0 601.3 566.0 15.0 569.5 544.0 10.0 603.8 576.0 11.0 599.5 578.0 8.0 583.8 552.0 15.0 596.3 568.0 11.0 547.5 533.0 6.0 541.5 524.0 6.0 550.3 546.0 2.0 510.8 502.0 3.0 9,261 8,688 237 Soruce: Field survey, 2018

Appendix-2.1:Approach Cruise Speed Survey of Gausala (North) Approach Baseline length = 30.0 m S.N

Clock Time (hh:mm)

Date: May 16, 2018

Vehicle Type

Time Taken (s)

Speed (kph)

Notes

MC

3.61

29.90

K

1

12:23 PM

2

12:23 PM

C

4.70

23.00

K

3

12:25 PM

MC

3.50

30.90

K

4

12:25 PM

MC

2.80

38.60

K

5

12:25 PM

MC

2.50

43.20

K

6

12:25 PM

U

3.16

34.20

K

7

12:26 PM

C

2.61

41.40

K

8

12:26 PM

3W

4.20

25.70

K

9

12:26 PM

MC

4.40

24.50

K

10

12:27 PM

C

3.30

32.70

11

12:27 PM

MC

3.00

36.00

12

12:28 PM

C

4.70

23.00

13

12:28 PM

MC

2.50

43.20

14

12:29 PM

MC

2.50

43.20

15

12:29 PM

MC

3.01

35.90

16

12:30 PM

MC

2.20

49.10

17

12:30 PM

MC

2.91

37.10

18

12:30 PM

C

3.71

29.10

19

12:31 PM

MC

4.80

22.50

X

20

12:31 PM

MC

5.40

20.00

X

21

12:31 PM

3W

4.70

23.00

22

12:33 PM

3W

4.50

24.00

K

23

12:35 PM

FW

4.20

25.70

K

24

12:35 PM

MC

2.90

37.20

K

25

12:35 PM

U

3.70

29.20

K

26

12:36 PM

FW

3.60

30.00

K

27

12:37 PM

C

4.00

27.00

K

28

12:37 PM

U

5.00

21.60

K

29

12:37 PM

C

2.30

47.00

K

30

12:39 PM

MICRO

3.14

34.40

K

31

12:40 PM

MC

2.70

40.00

K

32

12:40 PM

U

3.40

31.80

K

P

33

12:41 PM

B

5.70

18.90

K

34

12:41 PM

3W

4.30

25.10

K

35

12:42 PM

3W

4.00

27.00

K

36

12:42 PM

MC

4.90

22.00

K

37

12:42 PM

C

3.70

29.20

K

38

12:42 PM

C

5.16

20.90

K

39

12:44 PM

MC

3.14

34.40

K

40

12:45 PM

MC

4.80

22.50

K

96

Appendix-2.1:Approach Cruise Speed Survey of Gausala (North) Approach (contd…) S.N

Clock Time (hh:mm)

Vehicle Type

Time Taken (s)

Speed (kph)

Notes

41

12:46 PM

FW

3.20

33.80

K

42

12:46 PM

C

2.80

38.60

K

43

12:47 PM

MC

2.80

38.60

K

44

12:51 PM

C

4.90

22.00

K

45

12:52 PM

MC

3.60

30.00

K

46

12:52 PM

U

2.20

49.10

K

47

12:53 PM

FW

2.90

37.20

K

48

12:54 PM

C

3.30

32.70

K

49

12:55 PM

MC

3.91

27.60

X

50

12:55 PM

MC

4.80

22.50

X

51

12:56 PM

FW

5.70

18.90

X

52

12:57 PM

C

5.80

18.60

X

53

12:57 PM

C

4.01

26.90

K

54

12:58 PM

3W

5.00

21.60

K

55

1:02 PM

C

3.70

29.20

K

56

1:02 PM

MC

4.30

25.10

K

57

1:03 PM

C

3.60

30.00

K

58

1:04 PM

MC

4.60

23.50

K

59

1:04 PM

3W

3.90

27.70

60

1:04 PM

FW

3.10

34.80

61

1:05 PM

3W

4.60

23.50

62

1:05 PM

C

3.70

29.20

63

1:05 PM

MC

2.14

50.50

64

1:06 PM

C

2.70

40.00

65

1:06 PM

MC

3.20

33.80

66

1:06 PM

C

3.60

30.00

67

1:07 PM

MIB

3.30

32.70

K

68

1:07 PM

3W

4.40

24.50

K

69

1:08 PM

4W

4.40

24.50

K

70

1:09 PM

3W

5.70

18.90

K

71

1:10 PM

MC

2.40

45.00

K

72

1:10 PM

3W

4.80

22.50

K

73

1:11 PM

C

3.70

29.20

K

74

1:11 PM

C

3.80

28.40

K

75

1:11 PM

MC

3.30

32.70

K

76

1:11 PM

MC

3.50

30.90

K

77

1:12 PM

3W

4.00

27.00

K

78

1:12 PM

MC

3.30

32.70

K

79

1:12 PM

C

2.70

40.00

K

80

1:12 PM

C

3.30

32.70

K

97

K

Appendix-2.1:Approach Cruise Speed Survey of Gausala (North) Approach (contd…) S.N

Clock Time (hh:mm)

Vehicle Type

Time Taken (s)

Speed (kph)

Notes

81

1:12 PM

MC

2.80

38.60

K

82

1:13 PM

C

4.00

27.00

K

83

1:13 PM

MC

3.20

33.80

K

84

1:13 PM

FW

3.60

30.00

K

85

1:14 PM

3W

5.20

20.80

Minimum Speed =

18.60

km/h

Maximum Speed =

50.50

km/h

Number of Samples, N =

85.00

Sample Mean speed, xm =

30.60

km/h

Standard deviation of sample values, s =

7.75

km/h

Standard Error of the mean, SEm =

0.80

km/h

Value of two standard errors (2xSEm) =

1.60

km/h

30.60

km/h ± 1.6 km/h with 95 % confidence

44.64

km/h

38.60

km/h

Hence, Mean speed = 95th percentile speed = th

85 percentile speed =

Source: Field survey,2018 Vehicle Types Code

Description B:

MC: C:

Code

Bicycle

Description MIB:

Minibus

Motorcycle

SB:

Std. bus

Car/Vans

LT:

Light truck

FW:

Four Wheel Drive

3W:

Three-wheeler

MAT

Multi Axle Truck

U:

Utility Vehicle

Tract:

Tractor

Micro:

HT:

Microbus Reasons for Delay (with codes where used)

S = Signals J = Other Junction

Accident - Breakdown Floods - Weather extremes

P = Pedestrians

Encroachments

B = Bus or Para transit K = Affected by Parking

Police Intervention Roadworks Diversion

X = Part of platoon - not at free speed

Unknown - other

D = Damaged Pavement surfacing

98

Heavy truck

Appendix-2.2:Approach Cruise Speed Survey of Maitidevi (West) Approach Baseline length = 30.0 m Clock Time S.N (hh:mm) 1 1:44 PM 2 1:44 PM 3 1:45 PM 4 1:45 PM 5 1:46 PM 6 1:46 PM 7 1:46 PM 8 1:46 PM 9 1:46 PM 10 1:47 PM 11 1:47 PM 12 1:47 PM 13 1:48 PM 14 1:48 PM 15 1:48 PM 16 1:48 PM 17 1:49 PM 18 1:49 PM 19 1:49 PM 20 1:49 PM 21 1:50 PM 22 1:50 PM 23 1:50 PM 24 1:51 PM 25 1:51 PM 26 1:51 PM 27 1:52 PM 28 1:52 PM 29 1:52 PM 30 1:53 PM 31 1:53 PM 32 1:54 PM 33 1:54 PM 34 1:54 PM 35 1:54 PM 36 1:55 PM 37 1:55 PM 38 1:56 PM 39 1:56 PM 40 1:56 PM 41 1:56 PM 42 1:57 PM 43 1:58 PM 44 1:58 PM 45 1:59 PM

Date: May 16, 2018 Vehicle Type C MC MC MC MC C MC MC C U MC U MC C MC MC MC FW MC MC FW MC MC C MC C MC 3W C MC 3W MC C C MC MICRO MC 3W 3W MICRO U MC MC MC MC

Time Taken (s) 3.90 2.80 3.30 3.14 2.90 3.90 3.60 2.20 4.71 4.30 4.60 4.40 2.80 5.60 2.60 3.30 2.40 2.91 3.30 2.91 3.11 3.40 3.20 3.11 3.00 3.91 2.90 5.30 2.70 3.00 5.18 2.60 4.00 3.20 1.80 5.30 3.51 5.00 6.51 4.40 3.20 3.50 5.30 2.80 2.50

99

Speed (kph) 27.7 38.6 32.7 34.4 37.2 27.7 30.0 49.1 22.9 25.1 23.5 24.5 38.6 19.3 41.5 32.7 45.0 37.1 32.7 37.1 34.7 31.8 33.8 34.7 36.0 27.6 37.2 20.4 40.0 36.0 20.8 41.5 27.0 33.8 60.0 20.4 30.8 21.6 16.6 24.5 33.8 30.9 20.4 38.6 43.2

Notes

TAXI

X

MC PARKED MC PARKED

Appendix-2.2:Approach Cruise Speed Survey of Maitidevi (West) Approach (contd…) Clock Time (hh:mm) 46 2:00 PM 47 2:00 PM 48 2:01 PM 49 2:01 PM 50 2:07 PM 51 2:08 PM 52 2:08 PM 53 2:08 PM 54 2:09 PM 55 2:09 PM 56 2:10 PM 57 2:10 PM 58 2:10 PM 59 2:10 PM 60 2:10 PM 61 2:11 PM 62 2:11 PM 63 2:12 PM 64 2:12 PM 65 2:12 PM 66 2:13 PM 67 2:13 PM 68 2:14 PM 69 2:14 PM 70 2:14 PM 71 2:14 PM 72 2:15 PM 73 2:16 PM 74 2:16 PM 75 2:18 PM 76 2:18 PM 77 2:18 PM 78 2:19 PM 79 2:19 PM 80 2:19 PM 81 2:20 PM 82 2:20 PM 83 2:21 PM 84 2:21 PM 85 2:21 PM 86 2:21 PM 87 2:21 PM 88 2:22 PM Minimum Speed = Maximum Speed = Number of Samples, N = S.N

Vehicle Type MC MC MC 3W MC 3W C C MC MC LT MC MC MC MC 3W C FW MC MC MC C 3W C C C MC 3W 3W C 3W C MC C MICRO U MC 3W C MC 3W 3W C

Time Taken (s) 2.60 2.40 5.00 6.30 3.41 5.41 4.17 4.80 3.20 3.20 4.60 3.00 2.40 2.70 2.81 5.80 4.00 4.40 2.40 2.80 2.30 4.00 4.00 3.51 3.12 3.20 4.20 6.81 5.60 3.19 5.13 4.30 2.80 3.20 4.40 5.20 3.70 5.60 4.60 3.19 3.30 6.00 4.40

100

Speed (kph) 41.5 45.0 21.6 17.1 31.7 20.0 25.9 22.5 33.8 33.8 23.5 36.0 45.0 40.0 38.4 18.6 27.0 24.5 45.0 38.6 47.0 27.0 27.0 30.8 34.6 33.8 25.7 15.9 19.3 33.9 21.1 25.1 38.6 33.8 24.5 20.8 29.2 19.3 23.5 33.9 32.7 18.0 24.5 15.9 60.0 88.0

Notes

X

TURNING VEHICLE

km/h km/h

Appendix-2.2:Approach Cruise Speed Survey of Maitidevi (West) Approach (contd…) S.N

Clock Time (hh:mm)

Vehicle Type

Time Taken (s)

Speed (kph)

Notes

Sample Mean speed, xm = Standard deviation of sample values, s = Standard Error of the mean, SEm = Value of two standard errors (2xSEm) =

30.9 8.7 0.9 1.8

Hence, Mean speed =

30.9

km/h km/h km/h km/h km/h ± 1.8 km/h with 95 % confidence

95 percentile speed =

45.0

km/h

85th percentile speed =

38.6

km/h

th

Source: Field survey, 2018 Vehicle Types Code

Description B: MC: C: FW:

Bicycle

Code

Description

MIB:

Minibus

Motorcycle

SB:

Std. bus

Car/Vans

LT:

Light truck

Four Wheel Drive

HT:

Heavy truck

3W:

Three-wheeler

MAT

Multi Axle Truck

U:

Utility Vehicle

Tract:

Tractor

Micro:

Microbus Reasons for Delay (with codes where used)

S = Signals

Accident - Breakdown

J = Other Junction

Floods - Weather extremes

P = Pedestrians

Encroachments

B = Bus or Para transit

Police Intervention

K = Affected by Parking

Roadworks - Diversion

X = Part of platoon - not at free speed

Unknown - other

D = Damaged Pavement surfacing

101

Appendix-2.3:Approach Cruise Speed Survey of Sinamangal (East) Approach Baseline length = 30.0 m Clock Time Vehicle S.N (hh:mm) Type 1 8:14 AM MC 2 8:14 AM C 3 8:14 AM C 4 8:14 AM MC 5 8:15 AM 3W 6 8:16 AM MIB 7 8:16 AM MC 8 8:16 AM 3W 9 8:17 AM MC 10 8:17 AM MC 11 8:17 AM C 12 8:18 AM MC 13 8:18 AM C 14 8:19 AM C 15 8:19 AM MICRO 16 8:19 AM MC 17 8:19 AM 3W 18 8:20 AM U 19 8:20 AM MIB 20 8:22 AM MC 21 8:22 AM MC 22 8:23 AM C 23 8:23 AM MC 24 8:24 AM MC 25 8:24 AM MC 26 8:24 AM MC 27 8:25 AM MC 28 8:25 AM C 29 8:25 AM 4W 30 8:25 AM C 31 8:26 AM 3W 32 8:26 AM MC 33 8:27 AM C 34 8:27 AM MC 35 8:27 AM MC 36 8:27 AM MC 37 8:28 AM C 38 8:28 AM MC 39 8:28 AM C 40 8:29 AM MC 41 8:29 AM C 42 8:30 AM MC 43 8:30 AM 4W 44 8:30 AM C 45 8:31 AM 3W

Date: May 20, 2018 Time Taken (s) 2.71 6.40 5.90 3.90 6.30 3.80 3.50 5.00 4.01 2.31 4.00 4.20 4.40 3.00 2.71 4.30 4.01 2.60 4.20 4.70 3.20 4.30 4.60 4.77 2.60 6.30 5.30 4.80 4.30 3.30 6.01 3.70 4.20 4.50 3.90 3.41 3.40 4.00 4.60 4.00 3.50 2.50 4.30 3.11 6.17

102

Speed (kph) 39.9 16.9 18.3 27.7 17.1 28.4 30.9 21.6 26.9 46.8 27.0 25.7 24.5 36.0 39.9 25.1 26.9 41.5 25.7 23.0 33.8 25.1 23.5 22.6 41.5 17.1 20.4 22.5 25.1 32.7 18.0 29.2 25.7 24.0 27.7 31.7 31.8 27.0 23.5 27.0 30.9 43.2 25.1 34.7 17.5

Notes

TAXI

GAS TEMPO

X K

D

SMALL VAN GAS TEMPO K

TAXI

ELECTRIC

Appendix-2.3:Approach Cruise Speed Survey of Sinamangal (East) Approach (contd…) S.N 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93

Clock Time (hh:mm) 8:31 AM 8:32 AM 8:32 AM 8:33 AM 8:33 AM 8:33 AM 8:34 AM 8:34 AM 8:34 AM 8:34 AM 8:35 AM 8:35 AM 8:35 AM 8:36 AM 8:36 AM 8:37 AM 8:37 AM 8:37 AM 8:38 AM 8:38 AM 8:38 AM 8:38 AM 8:39 AM 8:39 AM 8:40 AM 8:40 AM 8:40 AM 8:41 AM 8:41 AM 8:41 AM 8:42 AM 8:42 AM 8:43 AM 8:45 AM 8:47 AM 8:47 AM 8:47 AM 8:48 AM 8:48 AM 8:48 AM 8:49 AM 8:50 AM 8:50 AM 8:51 AM 8:51 AM 8:51 AM 8:52 AM 8:52 AM

Vehicle Type C C MC U MC MC C MC MINI C 3W MC 4W MC C C MC MINI MC MINI C C MC MC C MC C C 4W C C C C MICRO C MC 3W MC MC C C C U C MC MC MC 3W

Time Taken (s) 5.00 4.90 5.00 5.00 4.60 3.50 3.91 3.30 3.91 4.11 5.00 3.90 4.15 3.20 3.18 4.20 3.19 4.80 3.90 5.50 3.41 3.90 3.80 3.80 3.40 3.21 3.77 4.40 3.90 5.00 4.70 4.90 3.70 4.60 5.50 5.90 5.50 3.80 4.00 3.00 4.21 5.40 6.20 5.90 4.60 4.80 4.30 8.13

103

Speed (kph) 21.6 22.0 21.6 21.6 23.5 30.9 27.6 32.7 27.6 26.3 21.6 27.7 26.0 33.8 34.0 25.7 33.9 22.5 27.7 19.6 31.7 27.7 28.4 28.4 31.8 33.6 28.6 24.5 27.7 21.6 23.0 22.0 29.2 23.5 19.6 18.3 19.6 28.4 27.0 36.0 25.7 20.0 17.4 18.3 23.5 22.5 25.1 13.3

Notes

P P

TAXI ELECTRIC

P X P P P X X

VAN

P P X

K P K K K K ELECTRIC & P

Appendix-2.3:Approach Cruise Speed Survey of Sinamangal (East) Approach (contd…) Clock Time Vehicle Time (hh:mm) Type Taken (s) 94 8:53 AM MC 2.90 95 8:53 AM MC 2.77 96 8:54 AM C 3.20 97 8:54 AM 4W 3.71 98 8:54 AM C 3.50 99 8:55 AM MC 3.51 100 8:55 AM C 3.77 101 8:56 AM C 3.91 102 8:56 AM C 3.50 103 8:56 AM 4W 4.40 104 8:57 AM MC 3.60 105 8:57 AM MC 3.00 106 8:57 AM 4W 4.30 107 8:58 AM C 5.80 108 8:58 AM C 4.90 109 8:58 AM 3W 5.40 110 8:59 AM MC 3.60 111 8:59 AM U 5.50 112 8:59 AM MC 3.50 113 9:00 AM 3W 5.00 114 9:01 AM MC 3.90 115 9:01 AM C 3.60 116 9:01 AM C 3.20 117 9:01 AM MINI 4.50 118 9:02 AM C 4.20 119 9:02 AM 3W 5.00 120 9:02 AM MC 4.30 121 9:03 AM 4W 3.60 122 9:03 AM C 5.60 123 9:03 AM 4W 3.90 124 9:04 AM MC 4.50 125 9:05 AM MC 3.50 126 9:05 AM C 3.00 127 9:05 AM C 4.40 128 9:06 AM 3W 4.40 129 9:07 AM C 4.11 Minimum Speed = Maximum Speed = Number of Samples, N = Sample Mean speed, xm = Standard deviation of sample values, s = Standard Error of the mean, SEm = Value of two standard errors (2xSEm) =

Speed (kph) 37.2 39.0 33.8 29.1 30.9 30.8 28.6 27.6 30.9 24.5 30.0 36.0 25.1 18.6 22.0 20.0 30.0 19.6 30.9 21.6 27.7 30.0 33.8 24.0 25.7 21.6 25.1 30.0 19.3 27.7 24.0 30.9 36.0 24.5 24.5 26.3 13.3 46.8 129.0 26.9 6.1 0.5 1.0

Hence, Mean speed =

26.9

S.N

95th percentile speed = th

85 percentile speed =

Notes

X ELECTRIC

GAS TEMPO

ELECTRIC K K K

K ELECTRIC km/h km/h km/h km/h km/h km/h km/h ± 1 km/h with 95 % confidence

38.3

km/h

33.5

km/h Source: Field survey, 2018

104

Appendix-2.4:Approach Cruise Speed Survey of New Baneshwor (South) Approach Baseline length = 30.0 m Clock S.N Time (hh:mm) 1 7:58 AM 2 7:58 AM 3 7:59 AM 4 7:59 AM 5 7:59 AM 6 8:01 AM 7 8:01 AM 8 8:02 AM 9 8:02 AM 10 8:08 AM 11 8:09 AM 12 8:10 AM 13 8:10 AM 14 8:10 AM 15 8:11 AM 16 8:12 AM 17 8:12 AM 18 8:13 AM 19 8:14 AM 20 8:14 AM 21 8:15 AM 22 8:16 AM 23 8:16 AM 24 8:17 AM 25 8:17 AM 26 8:18 AM 27 8:18 AM 28 8:19 AM 29 8:19 AM 30 8:20 AM 31 8:21 AM 32 8:22 AM 33 8:23 AM 34 8:24 AM 35 8:24 AM 36 8:25 AM 37 8:25 AM 38 8:26 AM 39 8:27 AM 40 8:27 AM 41 8:28 AM 42 8:29 AM 43 8:30 AM 44 8:30 AM 45 8:31 AM 46 8:32 AM 47 8:32 AM

Date: May 20, 2018 Vehicle Type

Time Taken (s)

Speed (km/h)

MC MC C C MC C 3W U MC C C 3W MC MC MIB MC MC C 3W C C C C MC 3W 3W C C U MC MC 3W LT C LT MC C U C MC U LT MC 3W C MC MC

5.03 4.73 6.27 6.99 5.58 5.79 7.46 6.54 4.34 5.48 6.08 6.54 3.14 3.85 6.98 4.46 4.38 7.48 7.32 4.72 5.65 5.40 6.71 7.96 6.05 6.53 5.22 6.07 5.53 4.41 4.76 9.13 7.66 5.50 7.98 4.08 7.69 5.53 5.77 6.04 5.19 12.08 4.90 7.71 7.05 6.21 5.35

21.5 22.8 17.2 15.5 19.4 18.7 14.5 16.5 24.9 19.7 17.8 16.5 34.4 28.1 15.5 24.2 24.7 14.4 14.8 22.9 19.1 20.0 16.1 13.6 17.9 16.5 20.7 17.8 19.5 24.5 22.7 11.8 14.1 19.6 13.5 26.5 14.0 19.5 18.7 17.9 20.8 8.9 22.0 14.0 15.3 17.4 20.2

105

Notes

X X

X X

X

Appendix-2.4:Approach Cruise Speed Survey of New Baneshwor (South) Approach (contd..) S.N 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93

Clock Time (hh:mm) 8:33 AM 8:33 AM 8:34 AM 8:34 AM 8:35 AM 8:35 AM 8:36 AM 8:36 AM 8:37 AM 8:38 AM 8:38 AM 8:38 AM 8:39 AM 8:40 AM 8:41 AM 8:43 AM 8:43 AM 8:44 AM 8:46 AM 8:46 AM 8:47 AM 8:47 AM 8:48 AM 8:48 AM 8:50 AM 8:51 AM 8:51 AM 8:52 AM 8:52 AM 8:53 AM 8:54 AM 8:54 AM 8:55 AM 8:55 AM 8:56 AM 8:57 AM 8:58 AM 8:59 AM 9:00 AM 9:01 AM 9:02 AM 9:04 AM 9:06 AM 9:06 AM 9:07 AM 9:07 AM

Vehicle Type

Time Taken (s)

Speed (km/h)

C MC C MC MC MC MC U FW MC FW MC MC C MICRO C MC MC 3W MICRO U MC FW MC 3W MC MC 3W C MC MC C C MC LT C 3W C MC C MC C MC C MC C

5.77 4.47 5.43 6.61 5.21 3.89 5.03 8.48 4.87 4.52 6.02 6.07 5.77 5.45 8.14 5.83 3.73 6.98 7.39 5.12 6.48 6.09 8.96 4.05 8.33 8.93 3.59 5.17 6.06 5.15 2.86 6.15 6.73 3.76 5.85 6.12 6.53 5.30 4.19 5.50 3.89 6.41 6.10 5.64 2.84 6.65

18.7 24.2 19.9 16.3 20.7 27.8 21.5 12.7 22.2 23.9 17.9 17.8 18.7 19.8 13.3 18.5 29.0 15.5 14.6 21.1 16.7 17.7 12.1 26.7 13.0 12.1 30.1 20.9 17.8 21.0 37.8 17.6 16.0 28.7 18.5 17.6 16.5 20.4 25.8 19.6 27.8 16.8 17.7 19.1 38.0 16.2

106

Notes

X

X

X

X

X

Appendix-2.4:Approach Cruise Speed Survey of New Baneshwor (South) Approach (contd..) Clock Vehicle Time Type (hh:mm) 94 9:08 AM MC 95 9:08 AM SB 96 9:09 AM 3W 97 9:09 AM MC 98 9:10 AM C 99 9:10 AM MC 100 9:11 AM C 101 9:11 AM C 102 9:12 AM 3W 103 9:13 AM C 104 9:14 AM MICRO 105 9:14 AM 3W 106 9:15 AM MC 107 9:17 AM MC 108 9:18 AM MC Minimum Speed = Maximum Speed = Number of Samples, N = Sample Mean speed, xm = Standard deviation of sample values, s = Standard Error of the mean, SEm = Value of two standard errors (2xSEm) = S.N

Time Taken (s)

Speed (km/h)

7.34 7.16 6.35 5.19 5.92 3.92 6.83 4.35 7.88 5.67 6.68 9.99 5.77 4.65 6.78

14.7 15.1 17.0 20.8 18.2 27.6 15.8 24.8 13.7 19.0 16.2 10.8 18.7 23.2 15.9 8.9 38.0 108.0 19.4 5.2 0.5 1.0

Notes X

X K

P

km/h km/h km/h km/h km/h km/h km/h ± 1 km/h with 95 % confidence

Hence, Mean speed =

19.4

95th percentile speed =

28.5

km/h

24.5

km/h

th

85 percentile speed =

Source: Field survey, 2018 Vehicle Types Code

Description B:

MC: C: FW:

Bicycle Motorcycle

Code

Description

MIB:

Minibus

SB:

Std. bus

Car/Vans

LT:

Light truck

Four Wheel Drive

HT:

Heavy truck

3W:

Three-wheeler

MAT

Multi Axle Truck

U:

Utility Vehicle

Tract:

Tractor

Micro:

Microbus Reasons for Delay (with codes where used)

S = Signals

Accident - Breakdown

J = Other Junction

Floods - Weather extremes

P = Pedestrians

Encroachments

B = Bus or Para transit

Police Intervention

K = Affected by Parking

Roadworks - Diversion

X = Part of platoon - not at free speed

Unknown - other

D = Damaged Pavement surfacing

107

Appendix-3.1:Field Measured Back of Queue in Sinamangal Approach (AM Peak Hour) Intersection Name: Old Baneshwor Chowk Arm Name: Sinamangal Approach (East) Study Period: AM Peak Hour (10:00 AM- 11:00 AM) Weather: Clear Overflow Queue Back of Queue Cycle start Queue at start of RED (veh) (veh) (veh) time Cycl (start red) No Nb Ni e Total MC no. (with H M S MC LV HV Total MC in LV HV MC LV HV Total MC PCU in PCU) veh PCU veh veh veh Tuesday, May 8, 2018 (AM Peak Hour) 32 1 10 05 00 32 8 23 1 31 2 10 08 00 40 10 21 18 3 10 12 00 20 5 13 17 4 10 15 00 20 5 11 1 6 5 10 19 00 9 2 4 14 6 10 21 00 20 5 9 15 7 10 24 00 15 4 11 10 8 10 26 00 20 5 5 16 9 10 29 00 12 3 13 8 10 10 33 00 5 1 7 17 11 10 37 00 12 3 14 20 12 10 41 00 27 7 13 9 13 10 44 00 7 2 7 8 14 10 46 00 4 1 7 15 15 10 55 00 18 5 10 4 16 10 57 00 5 1 3 Wednesday, May 9, 2018 (AM Peak Hour) 15 17 10 00 00 28 7 8 0 29 18 10 06 00 38 10 19 0 24 19 101 14 00 50 13 11 0 15 20 10 21 00 21 5 10 0 26 21 10 23 00 35 9 16 1 10 22 10 29 00 10 3 7 0 12 23 10 36 00 12 3 9 0 5 24 10 42 00 7 2 3 0 4 25 10 45 00 2 1 3 0 16 26 10 47 00 25 6 10 0 26 27 10 50 00 39 10 16 0 6 28 10 54 00 8 2 4 0 3 29 10 57 00 7 2 1 0 8 30 10 59 00 6 2 6 0 95th percentile back of queue:

30

85th percentile back of queue: Sample Mean: Standard Deviation: Sample Size: Standard Error of mean, SE: 2xSE: Hence, the Mean Back of Queue:

25 15 8 30 1 2 15

108

± 2 veh with 95 % confidence

Appendix-3.2:Field Measured Back of Queue in Gausala Approach (AM Peak Hour) Intersection Name: Old Baneshwor Chowk Arm Name: Gausala Approach (North) Study Period: AM Peak Hour (10:00 AM- 11:00 AM) Cycle Queue at start of RED Back of Queue start time (veh) (veh) (start Ni Nb red) Cycle no. MC H M S MC LV HV Total MC in LV HV PCU veh Tuesday, May 8, 2018 (AM Peak Hour) 1 10 02 00 16 2 10 06 00 41 3 10 10 00 51 4 10 14 00 67 5 10 17 00 34 6 10 19 00 32 7 10 22 00 40 8 10 25 00 17 9 10 28 00 2 2 16 10 10 31 00 17 11 10 34 00 18 12 10 36 00 14 13 10 39 00 31 14 10 42 00 12 15 10 45 00 18 16 10 47 00 13 17 10 45 00 3 18 10 52 00 7 19 10 54 00 8 20 10 56 00 10 Wednesday, May 9, 2018 (AM Peak Hour) 21 10 04 00 17 22 10 05 00 33 23 10 07 00 32 24 10 17 00 45 25 10 21 00 3 5 8 19 26 10 22 00 64 27 10 25 00 8 28 10 28 00 27 5 29 10 30 00 30 10 36 00 10 31 10 46 00 13 32 10 47 00 15 33 10 49 00 50

Weather: Clear Overflow Queue (veh) No

Total (with MC MC in PCU) PCU veh veh veh 4 10 13 17 9 8 10 4 4 4 5 4 8 3 5 3 1 2 2 3

6 31 32 20 5 20 11 17 5 14 2 7 24 7 1 20 3 5 13 6

0 2 0 0 0 1 0 0 0 1 0 1 0 0 0 2 0 0 0 0

10 43 45 37 14 29 21 21 9 19 7 12 32 10 6 25 4 7 15 9

4 8 8 11 5 16 2 7 1 3 3 4 13

11 15 28 34 6 22 8 6 6 4 9 15 33

0 0 1 0 0 1 0 0 0 0 0 0 0

15 23 37 45 11 39 10 13 7 7 12 19 46

95th percentile back of queue:

45

85th percentile back of queue: Sample Mean: Standard Deviation: Sample Size: Standard Error of mean, SE: 2xSE: Hence, the Mean Back of Queue:

37 20 13 33 2 4 20

109

LV

HV

Total

2

2 -

3

5

8 -

± 4 veh with 95 % confidence

Appendix-3.3:Field Measured Back of Queue in Maitidevi Approach (AM Peak Hour) Intersection Name: Old Baneshwor Chowk Arm Name: Maitidevi Approach (West) Study Period: AM Peak Hour (10:00 AM- 11:00 AM) Weather: Clear Cycle Queue at start of RED Back of Queue Overflow Queue start time (veh) (veh) (veh) (start Cycle Nb No Ni red) no. MC Total H M S MC LV HV Total MC in LV HV (with MC MC LV HV Total PCU in PCU) veh PCU veh veh veh Tuesday, May 9, 2018 (AM Peak Hour) 3 1 10 01 00 8 2 1 0 8 2 10 04 00 13 3 5 0 18 3 10 06 00 17 4 14 0 2 4 6 13 4 10 09 00 2 4 6 18 5 8 0 5 5 10 11 00 9 2 3 0 20 6 10 15 00 17 4 16 0 9 7 10 18 00 12 3 6 0 18 8 10 20 00 25 6 12 0 8 0 9 10 23 00 11 3 5 11 10 10 28 00 16 4 7 0 15 11 10 30 00 19 5 10 0 4 12 10 38 00 5 1 3 0 1 13 10 40 00 3 1 0 0 3 14 10 43 00 2 1 2 0 14 15 10 46 00 10 3 10 1 14 16 10 49 00 13 3 11 0 3 17 10 52 00 9 2 1 0 5 18 10 54 00 11 3 2 0 3 19 10 57 00 7 2 1 0 95th percentile back of queue:

18

85th percentile back of queue: Sample Mean: Standard Deviation: Sample Size: Standard Error of mean, SE: 2xSE: Hence, the Mean Back of Queue:

16 9 6 19 1 2 9

110

± 2 veh with 95 % confidence

Appendix-3.4:Field Measured Back of Queue in New Baneshwor Approach (AM Peak Hour) Intersection Name: Old Baneshwor Chowk Arm Name: New Baneshwor Approach (South) Study Period: AM Peak Hour (10:00 AM- 11:00 AM) Weather: Clear Cycle Queue at start of Back of Queue Overflow Queue start time (veh) (veh) (veh) (start Ni Nb No red) Cycle Total no. MC (with H M S MC LV HV Total MC in LV HV MC LV HV Total MC PCU in PCU) veh PCU veh veh veh Wednesday, May 9, 2018 (AM Peak Hour) 5 1 10 02 00 3 1 3 1 6 2 10 04 00 6 2 4 0 2 3 10 06 00 4 1 1 0 2 4 6 10 4 10 07 00 2 4 6 7 2 8 0 4 5 10 11 00 5 1 3 0 3 6 10 12 00 4 1 2 0 3 7 10 15 00 7 2 1 0 25 8 10 17 00 21 5 19 1 2 9 10 19 00 4 1 1 0 10 10 10 20 00 12 3 7 0 7 11 10 23 00 4 1 6 0 4 12 10 35 00 2 1 3 0 3 13 10 44 00 3 1 2 0 7 14 10 48 00 8 2 5 0 95th percentile back of queue:

15

85th percentile back of queue: Sample Mean: Standard Deviation: Sample Size: Standard Error of mean, SE: 2xSE: Hence, the Mean Back of Queue:

10 7 6 14 2 4 7

111

± 4 veh with 95 % confidence

Appendix-4.1: Saturation Flow Study of shared lane (TR) in New Baneshwor approach Prevailing Field Saturation Flow Rate Study

Tractor

Heavy Truck

Std Bus

Light Truck

Mini Bus

Micro Bus

Utility  Vechicle

Three  Wheeler

Site Name: Old Baneshwor Intersection Arm Name: New Baneshwor Leg (South) Lane No: 2 (TH+RT) Area Type: CBD Date surveyed: Wednesday, May 8, 2018 Study Time Period: 17:01 to 18:13 PM No. of Vehicles Passing Saturat‐ Total ed Four  LV & HV Motor‐ Vehicle Types => Time wheel  Car (with MC  Cycle (s) Drive in PCU) PCU factors => 0.25 1 1 1 1 1.25 2.5 3 1.5 2.5 1.5 veh Clock Cycle Start Time  No. hh:mm:ss 1 17:01:20          20        3            1           ‐            ‐         1    ‐     ‐          ‐            ‐      ‐                 10            12  2 17:02:43          19        5            1            4           1       1    ‐     ‐          ‐            ‐      ‐                 17            63  3 17:05:25          17        6            3           ‐            ‐        ‐      ‐     ‐          ‐            ‐      ‐                 13            38  4 17:08:16          33        7            2           ‐             2      ‐      ‐     ‐          ‐            ‐      ‐                 19            59  5 17:12:12          23        2            1           ‐             1      ‐       1   ‐          ‐            ‐      ‐                 11            52  6 17:13:34          15        4            1            1          ‐        ‐      ‐     ‐          ‐            ‐      ‐                 10            22  7 17:14:10          11       ‐             ‐              1           1      ‐      ‐     ‐          ‐            ‐      ‐                   5            21 

Lane width: 3m Weather: clear Satura‐ tion Flow (s') veh/h

  3,000        957    1,255    1,175        744    1,595        814 

8

17:16:30          61      13            6            1           ‐         ‐        1    ‐           ‐             ‐       ‐                  36           123     1,061 

9 10

17:20:51          26        3           ‐             ‐            ‐         1    ‐     ‐          ‐            ‐      ‐                 11            32    1,181  17:24:48          12        3            2            1          ‐        ‐      ‐     ‐          ‐            ‐      ‐                   9            24    1,350 

11

17:28:03          47      11            3            2           ‐          1     ‐      ‐           ‐             ‐       ‐                  29           108         958 

12

17:31:52          39        7            1            1           ‐          1     ‐      ‐            1           ‐       ‐                  21             60     1,245 

13

17:33:25          20        4            2            2            1        1     ‐      ‐           ‐             ‐        1                16             48     1,200 

14

17:37:09          44      17            4            2          ‐        ‐      ‐     ‐          ‐            ‐      ‐                 34            65    1,883 

15

17:40:52          47      13            4            2           ‐          1     ‐      ‐           ‐             ‐       ‐                  32             82     1,394 

16

17:45:49          26        3            2           ‐             ‐          1     ‐      ‐            1           ‐       ‐                  14             36     1,350 

17 18 19 20 21 22 23 24 25

17:55:33 17:56:16 17:57:08 18:00:29 18:03:55 18:04:45 18:06:27 18:11:08 18:13:57

         15           12           11           26           29           12           15           13           19 

      4        3        2        5        5        4        3        4        2 

         ‐             ‐              1            1           ‐             ‐             ‐              1            2 

         ‐              1            1           ‐             ‐             ‐             ‐             ‐              1 

        ‐            ‐            ‐            ‐            ‐             2          ‐             1          ‐   

    ‐        ‐        ‐        ‐         1      ‐        ‐         1      ‐   

  ‐       1     2     1    ‐      ‐       1    ‐      ‐   

 ‐     ‐     ‐     ‐     ‐     ‐     ‐     ‐     ‐   

      ‐          ‐          ‐          ‐          ‐          ‐          ‐          ‐          ‐   

        ‐            ‐            ‐            ‐            ‐            ‐            ‐            ‐            ‐   

  ‐      ‐      ‐      ‐      ‐      ‐      ‐      ‐      ‐   

               8                 8                 9               14               13                 9                 8               10               10 

          22            25            27            29            34            33            29            27            22 

Notes & delays

  1,268    1,152    1,167    1,676    1,403        982        962    1,367    1,595 

                                                          ‐                                                              ‐                                                              ‐                                                              ‐                                                              ‐                                                              ‐                                                              ‐     Polic intervention for 12 sec.  & affected by pedestrians                                                            ‐                                                              ‐     Polic intervention for 14 sec.  & affected by pedestrians   Polic intervention for 10 sec &  affected by pedestrians   Polic intervention for 28 sec &  affected by pedestrians   Affected by pedestrian   Polic intervention for 10 sec &  affected by pedestrians   Polic intervention for 14 sec &  affected by pedestrians                                                            ‐                                                              ‐                                                              ‐                                                              ‐                                                              ‐                                                              ‐                                                              ‐                                                              ‐                                                              ‐   

Sample Statistics Minimum Saturation Flow =       744 Maximum Saturation Flow =   3,000 Number of Samples, N =         25 Sample Mean Saturation Flow, x m =   1,309 Standard deviation of sample values, σ =       443 Standard Error of the mean, SEm =         89 Value of two standard errors (2xSEm) =       178 Hence, Mean Saturation Flow (veh/h)=   1,309 Median Saturation Flow =   1,245 th 95 percentile Saturation Flow =   1,842 th 85 percentile Saturation Flow =   1,595

112

 ± 178 veh/h with 95 %  confidence

Appendix-4.2: Saturation Flow Study of left turn lane in Gausala approach

Tractor

Heavy Truck

Light Truck

Std Bus

Mini Bus

Micro Bus

Utility  Vechicle

Three  Wheeler

Motor‐ Cycle

Site Name: Old Baneshwor Intersection Arm Name: Gausala Leg (North) Lane No: 1 (LT) Area Type: CBD Date surveyed: Wednesday, May 8, 2018 Study Time Period: 17:01 to 18:13 PM No. of Vehicles Passing Saturat‐ Total ed Four  LV & HV Vehicle Types => Time wheel  Car (with MC  (s) Drive in PCU) PCU factors => 0.25 1 1 1 1 1.25 2.5 3 1.5 2.5 1.5 veh Clock Cycle Start Time  No. hh:mm:ss 1 9:03:31 5 0 0 0 0 0 0 0 0 0 0                1            11  2 9:04:38 3 2 0 0 0 0 0 0 0 0 0                3            14  3 9:09:01 2 0 0 0 0 0 0 0 1 0 0                2              4  4 9:09:45 7 0 0 0 1 0 0 0 0 0 0                3            28  5 9:13:01 2 2 0 0 0 0 0 0 0 0 0                3              6  6 9:16:34 7 0 0 0 0 0 1 0 0 0 0                3            18  7 9:18:43 4 0 0 1 0 0 0 0 0 0 0                2              5  8 9:19:02 4 0 0 0 0 0 0 0 0 0 0                1              4  9 9:19:32 2 1 0 1 0 0 0 0 0 0 0                3            11  10 9:21:19 4 1 0 0 0 1 0 0 0 0 0                3            10 

Lane width: 2.5m Weather: clear Satura‐ tion Flow (s')

Notes & delays

veh/h

 Discarded         707   queued disharge     1,350   queued disharge   Discarded     1,500   queued disharge   Discarded     1,440   queued disharge         900   queued disharge         818   fairly queued disharge     1,080   fairly queued disharge 

Sample Statistics Minimum Saturation Flow =       707 Maximum Saturation Flow =   1,500 Number of Samples, N =           7 Sample Mean Saturation Flow, xm =   1,114 Standard deviation of sample values, σ =       319 Standard Error of the mean, SEm =       121 Value of two standard errors (2xSEm) =       242 Hence, Mean Saturation Flow (veh/h)=   1,114  ± 242 veh/h with 95 %  Median Saturation Flow =   1,080 th 95 percentile Saturation Flow =   1,482 th 85 percentile Saturation Flow =   1,446

113

Appendix-4.3: Saturation Flow Study of shared lane (TR) in Gausala approach

Tractor

Heavy Truck

Light Truck

Std Bus

Mini Bus

Micro Bus

Utility  Vechicle

Three  Wheeler

Motor‐ Cycle

Site Name: Old Baneshwor Intersection Arm Name: Gausala Leg (North) Lane No: 2 (TR) Area Type: CBD Date surveyed: Wednesday, May 8, 2018 Study Time Period: 9:18 to 10:16 PM No. of Vehicles Passing Saturat‐ Total ed Four  LV & HV Vehicle Types => Time Car wheel  (with MC  (s) Drive in PCU) PCU factors => 0.25 1 1 1 1 1.25 2.5 3 1.5 2.5 1.5 veh Clock Cycle Start Time  No. hh:mm:ss 1 9:18:40       22        5            1           ‐             3       1    ‐         ‐      ‐             1    ‐                 17            57  2 9:20:54         5        4            3            1          ‐        ‐      ‐         ‐       1          ‐      ‐                 10            24  3 9:30:33       14        5            4            1          ‐         1    ‐         ‐      ‐            ‐      ‐                 15            30  4 9:32:56       18        3           ‐             ‐            ‐        ‐      ‐         ‐      ‐            ‐      ‐                   8            13  5 8:45:33         8        4            2           ‐            ‐        ‐      ‐         ‐      ‐            ‐      ‐                   8            15  6 8:46:52       12        8            5            1           1       2    ‐         ‐       2           1    ‐                 23            53  7 8:50:10       23        4            3           ‐             1       1    ‐         ‐      ‐            ‐      ‐                 15            28  8 8:55:00       42        4            4           ‐             1       2    ‐         ‐      ‐            ‐      ‐                 22            53  9 8:57:20       10        5            1           ‐             1       2    ‐         ‐      ‐             1    ‐                 13            35  10 9:03:30       13       ‐              1           ‐            ‐        ‐      ‐         ‐      ‐            ‐      ‐                   4            21  11 9:05:50         8        4            3            1          ‐         3    ‐         ‐       1          ‐      ‐                 14            65  12 9:08:55       25        4            3            2           1       2    ‐         ‐      ‐            ‐      ‐                 18            39  13 9:11:00       16        3            3            3           1       2    ‐         ‐      ‐            ‐      ‐                 16            31  14 9:13:00       19        3            3           ‐             1       2     4       ‐      ‐            ‐      ‐                 18            58  15 9:16:20       14        5            6            1          ‐         1    ‐         ‐      ‐            ‐      ‐                 17            56  16 9:34:38       24        8            3            2           2       2    ‐         ‐       1          ‐      ‐                 24            62  17 9:40:11       88      18          11            2           2       3    ‐         ‐       1          ‐      ‐                 59          197  18 9:46:07       63      10          10            2           1       5    ‐         ‐      ‐            ‐      ‐                 44          151  19 9:50:55       32        9            7            1           2       1    ‐         ‐      ‐            ‐      ‐                 28            92  20 9:54:15       52      14            7            3          ‐         6    ‐         ‐      ‐            ‐      ‐                 43          111  21 10:00:00       91      17            9            5           2       3    ‐          1     2          ‐      ‐                 62          200  22 10:05:43       40        6            3           ‐             1       1    ‐         ‐      ‐            ‐      ‐                 21            49  23 10:09:02       56      16            7            1           2       1    ‐         ‐      ‐             1    ‐                 42          128  24 10:13:15       63        9          11            2           2       7    ‐         ‐      ‐            ‐      ‐                 47          107  25 10:16:35       50        6            6            1           ‐         ‐       ‐          ‐       ‐             ‐        1                27             55 

Lane width: 3.75m Weather: clear Satura‐ tion Flow

Notes & delays

veh/h

      1,042        1,538        1,740        2,077        1,920        1,562        1,896        1,460        1,286            729            775        1,685        1,858        1,102        1,061        1,394        1,078        1,043        1,096        1,395        1,112        1,543        1,181        1,573        1,735 

                                           ‐                                               ‐                                               ‐     Taxi blocked for 15 s                                             ‐                                               ‐                                               ‐                                               ‐                                               ‐                                               ‐     P, K   P, K   P, congestion   P, B   P   P   P   P   P   P   P   P, congestion                                             ‐     P                                             ‐   

Sample Statistics Minimum Saturation Flow =          729 Maximum Saturation Flow =      2,077 Number of Samples, N =            25 Sample Mean Saturation Flow, xm =      1,395 Standard deviation of sample values, σ =          367 Standard Error of the mean, SEm =            73 Value of two standard errors (2xSEm) =          146 Hence, Mean Saturation Flow (veh/h)=      1,395 Median Saturation Flow =      1,395 th 95 percentile Saturation Flow =      1,915 th 85 percentile Saturation Flow =      1,787

114

 ± 146 veh/h with 95 %  confidence

Appendix-4.4: Saturation Flow Study of shared lane (LTR) in Sinamangal approach

Tractor

Light Truck

Heavy Truck

Std Bus

Mini Bus

Micro Bus

Utility  Vechicle

Motor‐ Cycle

Three  Wheeler

Site Name: Old Baneshwor Intersection Arm Name: Sinamangal Arm (East) Lane No: 1 (LTR) Area Type: CBD Date surveyed: Wednesday, May 8, 2018 Study Time Period: 9:21 to 10:49 AM No. of Vehicles Passing Saturat‐ Total ed Four  LV & HV Vehicle Types => Time wheel  Car (with MC  (s) Drive in PCU) PCU factors => 0.25 1 1 1 1 1.25 2.5 3 1.5 2.5 1.5 veh Clock Cycle Start Time  No. hh:mm:ss 1 9:21:40       19        4           ‐             ‐          ‐        ‐      ‐       ‐      ‐      ‐      ‐                   9            17  2 9:24:34       15        4            2           ‐          ‐        ‐      ‐       ‐      ‐      ‐      ‐                 10            20  3 9:29:40       25      10            2            2        ‐        ‐      ‐       ‐      ‐      ‐      ‐                 20            46  4

9:31:22

      13        5            1            1           1       ‐       ‐        ‐       ‐       ‐       ‐                  11             23 

5

9:33:36

      11        3            1           ‐           ‐         ‐       ‐        ‐       ‐       ‐       ‐                    7             17 

6

9:37:47

      70        8           ‐              3           3       ‐       ‐        ‐       ‐       ‐       ‐                  32             55 

7

9:43:55

      93      25            3            5         ‐          1     ‐        ‐       ‐       ‐       ‐                  57           126 

8

9:48:56

      82      15            3            4         ‐         ‐       ‐        ‐       ‐       ‐       ‐                  43             90 

9

9:52:40

      48      16            3            2           1       ‐       ‐        ‐       ‐       ‐       ‐                  34             84 

10 11 12 13 14 15 16 17

9:56:14 9:59:10 10:03:36 10:07:28 10:11:34 10:15:15

      37        21        37        40        40        40 

18

10:23:37       24        7            1            2         ‐         ‐       ‐        ‐       ‐       ‐       ‐                  16             31 

19

10:25:57       32      11            4           ‐          ‐        ‐      ‐       ‐      ‐      ‐      ‐                 23            56 

20

10:28:54       18        5            1           ‐           ‐         ‐       ‐        ‐       ‐       ‐       ‐                  11             18 

21 22 23 24 25

              ‐                  ‐    10:40:45       14        4            2           ‐            1      ‐      ‐       ‐      ‐      ‐      ‐                 11            24                ‐    10:48:31       26        4           ‐             ‐            1       1    ‐       ‐      ‐      ‐      ‐                 13            28 

      5      15      10        6      14      16 

          1           ‐              2            1            1            2 

          1           ‐             ‐             ‐              1            2 

        1        ‐          ‐          ‐            3        ‐   

    ‐        ‐        ‐        ‐        ‐        ‐   

  ‐      ‐      ‐      ‐      ‐      ‐   

   ‐       ‐       ‐       ‐       ‐       ‐   

  ‐      ‐      ‐      ‐      ‐      ‐   

  ‐      ‐      ‐      ‐      ‐      ‐   

  ‐      ‐      ‐      ‐      ‐      ‐   

             17               20               21               17               29               30                ‐    10:20:50       15        2            2            1          1      ‐      ‐       ‐      ‐      ‐      ‐                 10 

          33            40            42            35            64            68            21 

Lane width: 4.5m Weather: clear Satura‐ tion Flow

Notes & delays

veh/h

      1,853                                                      ‐          1,755                                                      ‐          1,585                                                      ‐     Motorcycles from cross         1,761  roads & parking at d/s exit  lane   Parking at d/s exit lane &         1,429  Pedestrians   motorcycles from cross         2,062  roads & parking at d/s exit  lane   Affected by parking at d/s         1,636  exit lane   Slightly affected by         1,700  pedestrians   Affected by parking at d/s         1,457  exit lane        1,882                                                      ‐          1,823                                                      ‐          1,821                                                      ‐          1,749                                                      ‐          1,631                                                      ‐          1,588   Affected by pedestrians   Sample Discarded        1,671                                                      ‐     Affected by parking at d/s         1,858  exit lane & pedestrians        1,479                                                      ‐     Affected by parking at d/s         2,100  exit lane   Sample Discarded   Sample Discarded        1,575                                                      ‐     Sample Discarded        1,607                                                      ‐   

Sample Statistics Minimum Saturation Flow =      1,429 Maximum Saturation Flow =      2,100 Number of Samples, N =            21 Sample Mean Saturation Flow, x m =      1,715 Standard deviation of sample values, σ =          180 Standard Error of the mean, SEm =            39 Value of two standard errors (2xSEm) =            78 Hence, Mean Saturation Flow (veh/h)=      1,715 Median Saturation Flow =      1,700 th 95 percentile Saturation Flow =      2,062 th 85 percentile Saturation Flow =      1,858

115

 ± 78 veh/h with 95 %  confidence

Appendix-4.5: Saturation Flow Study of shared lane (TR) in Maitidevi approach

1

1

1

        6        16          8        10        13          2          7        17 

      2        4        5        4        4        4        4        8 

          1            2            1            1            1            1            1            6 

         ‐              3            2           ‐             ‐              2           ‐              2 

         2          ‐            ‐            ‐             2          ‐            ‐             2 

Heavy Truck

3

1.5 2.5 1.5

    ‐         1       1      ‐        ‐        ‐        ‐        ‐   

  ‐      ‐      ‐      ‐      ‐      ‐      ‐      ‐   

  ‐      ‐      ‐      ‐      ‐      ‐      ‐      ‐   

  ‐      ‐      ‐      ‐      ‐      ‐      ‐      ‐   

  ‐      ‐      ‐      ‐      ‐      ‐      ‐      ‐   

Tractor

1.25 2.5

Micro Bus

Light Truck

1

Std Bus

0.25

Mini Bus

PCU factors => Clock Cycle Start Time  No. hh:mm:ss 1 8:43:15 2 8:48:26 3 8:53:18 4 8:56:33 5 9:07:27 6 9:10:25 7 9:15:12 8 9:37:35

Utility  Vechicle

Motor‐ Cycle

Three  Wheeler

Site Name: Old Baneshwor Intersection Arm Name: Maitidevi Leg Lane No: 2 (TR) Lane width: 3m Area Type: CBD Date surveyed: Wednesday, May 8, 2018 Study Time Period: 8:43 to 10:46 AM & 17:03 to  Weather: clear No. of Vehicles Passing Saturat‐ Satura‐ Total ed Four  tion Notes & delays LV & HV Time Vehicle Types => wheel  Car Flow (with MC  (s) Drive in PCU)

 ‐     ‐     ‐     ‐     ‐     ‐     ‐     ‐   

veh

               7               14               11                 8               10                 8                 7               22 

veh/h

          19            75            71            30            20            23            21            55 

     1,232                                                  ‐     Discarded   Discarded   Discarded   Discarded       1,174                                                  ‐         1,157   P       1,456   B 

9

9:44:03

      34        4            3            1            1       ‐       ‐       ‐       ‐       ‐      ‐                  18             48        1,313   Pol. Intervention for 20 s 

10 11 12 13

9:56:25 9:59:05 10:18:23 10:20:54

      25          4        18        16 

14

10:32:15       28        8            1           ‐              2       ‐       ‐       ‐       ‐       ‐      ‐                  18             44        1,473   Pol. Intervention for 18 s 

15 16 17 18

10:35:16 10:40:31 10:43:58 10:46:18

19

17:03:55       27        5            2           ‐             ‐         ‐       ‐       ‐       ‐       ‐      ‐                  14             37        1,338   Pol. Intervention for 10 s 

20 21

17:06:38       50        7            3            1          ‐        ‐      ‐      ‐      ‐      ‐     ‐                 24            57       1,484                                                  ‐    17:11:07       19        9            2           ‐            ‐        ‐      ‐      ‐      ‐      ‐     ‐                 16            41       1,383                                                  ‐   

22

17:14:38       25        7            1           ‐             ‐         ‐       ‐       ‐       ‐       ‐      ‐                  14             33        1,555   Pol. Intervention for 26 s 

23 24 25

17:19:13       25        5            1           ‐             3      ‐      ‐      ‐      ‐      ‐     ‐                 15            43       1,277                                                  ‐    17:22:46       27        5            1           ‐             1      ‐      ‐      ‐      ‐      ‐     ‐                 14            39       1,269                                                  ‐    17:26:40       14        1            2           ‐             1      ‐      ‐      ‐      ‐      ‐     ‐                   8            22       1,227                                                  ‐   

        9        14        17        13 

      2        1        8        9 

      4        5        8        2 

          3            1           ‐              2 

         ‐              2           ‐              2 

         ‐              1            1           ‐   

         ‐              1            2           ‐   

         1          ‐            ‐            ‐   

        ‐             2          ‐             1 

    ‐        ‐        ‐        ‐   

     1      ‐        ‐        ‐   

  ‐      ‐       1    ‐   

   1    ‐      ‐      ‐   

  ‐      ‐      ‐      ‐   

  ‐      ‐      ‐      ‐   

  ‐      ‐      ‐      ‐   

  ‐      ‐      ‐      ‐   

  ‐      ‐      ‐      ‐   

  ‐      ‐      ‐      ‐   

 ‐     ‐     ‐     ‐   

 ‐     ‐     ‐     ‐   

             12                 4               15               15 

               8               14               14                 8 

          39            11            45            43 

          25            43            42            27 

     1,131       1,309       1,160       1,256 

     1,188       1,130       1,221       1,100 

                                                ‐                                                    ‐                                                    ‐                                                    ‐   

                                                ‐                                                    ‐                                                    ‐                                                    ‐   

Sample Statistics Minimum Saturation Flow =      1,100 Maximum Saturation Flow =      1,555 Number of Samples, N =            21 Sample Mean Saturation Flow, x m =      1,278 Standard deviation of sample values, σ =          130 Standard Error of the mean, SEm =            28 Value of two standard errors (2xSEm) =            56 Hence, Mean Saturation Flow (veh/h)=  1,278.0 Median Saturation Flow =      1,256 th 95 percentile Saturation Flow =      1,484 th 85 percentile Saturation Flow =      1,456

116

 ± 56 veh/h with 95 %  confidence

Appendix-5.1:Observation of Phase-A Timing of Traffic Police Date: May 7, 2018 Phase Start Time

Cycle No.

Observation Period: Peak Hour 10:00 AM - 11:00 pm Phase End Time

Remarks

Phase Duration (hh:mm:ss)

Date: May 7, 2018 1

10:07:23 10:10:18

0:02:55

2 3 4 5 6 7 8 9 10

10:12:07 10:15:41 10:18:13 10:22:33 10:25:14 10:27:58 10:29:36 10:30:52 10:32:31

10:14:38 10:16:57 10:20:52 10:24:01 10:26:59 10:29:16 10:30:14 10:31:46 10:33:50

0:02:31 0:01:16 0:02:39 0:01:28 0:01:45 0:01:18 0:00:38 0:00:54 0:01:19

11

10:35:36 10:39:28

0:03:52

12 13 14 15 16 17

10:41:18 10:44:13 10:48:50 10:51:46 10:56:02 10:58:21

10:42:55 10:47:03 10:50:00 10:54:22 10:57:22 10:59:47

0:01:37 0:02:50 0:01:10 0:02:36 0:01:20 0:01:26

10:03:20 10:07:04 10:11:01 10:15:03 10:17:52 10:20:25 10:23:24 10:25:46 10:28:42 10:32:03 10:35:02 10:36:43 10:40:08 10:43:25 10:46:02 10:47:56 10:49:38 10:53:07

0:03:20 0:02:03 0:02:11 0:01:57 0:01:25 0:01:05 0:01:44 0:01:12 0:01:44 0:01:52 0:01:29 0:00:52 0:03:03 0:02:07 0:01:22 0:01:06 0:00:41 0:03:07

RT from Gausala protected for a while by stopping TH&RT from New Baneshwor

RT from Gausala protected for a while by stopping TH&RT from New Baneshwor

Date: May 8, 2018 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

10:00:00 10:05:01 10:08:50 10:13:06 10:16:27 10:19:20 10:21:40 10:24:34 10:26:58 10:30:11 10:33:33 10:35:51 10:37:05 10:41:18 10:44:40 10:46:50 10:48:57 10:50:00

Date: May 9, 2018 36 10:08:59 10:10:40 37 10:20:14 10:22:44 38 10:23:46 10:26:17 39 10:27:31 10:28:22 Maximum Phase Time: Minimum Phase Time: Mean Phase Time: No. of Samples: Standard Deviation: Standard Error of the mean: Mean Phase Time:

0:01:41 0:02:30 0:02:31 0:00:51 0:03:52 0:00:38 0:01:50 39 0:00:48 0:00:08 0:01:50 ± 0:00:16 with 95 % confidence

117

Appendix-5.2:Observation of Phase-B Timing of Traffic Police Date: May 7, 2018 to May 9, 2018 Cycle No.

Phase Start Time

Phase End Time

Phase Duration (hh:mm:ss)

Remarks

Date: May 7, 2018 1 2

10:00:09 10:06:50

10:00:28 10:07:23

0:00:19 0:00:33

3

10:10:36

10:12:06

0:01:30

4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

10:14:38 10:16:54 10:21:02 10:24:00 10:27:00 10:29:06 10:30:10 10:31:43 10:33:35 10:39:30 10:43:01 10:47:27 10:50:02 10:54:28 10:57:25

10:15:44 10:18:11 10:22:28 10:25:09 10:28:03 10:29:37 10:31:00 10:32:32 10:35:35 10:41:14 10:44:10 10:48:44 10:51:44 10:56:02 10:58:20

0:01:06 0:01:17 0:01:26 0:01:09 0:01:03 0:00:31 0:00:50 0:00:49 0:02:00 0:01:44 0:01:09 0:01:17 0:01:42 0:01:34 0:00:55

TH vehicles from Maitidevi stopped to pass RT vehicles from Sinamangal

Date: May 8, 2018 19 20

10:03:27 10:07:07

10:05:17 10:08:48

0:01:50 0:01:41

21

10:11:11

10:13:03

0:01:52

22 23 24 25 26 27 28 29 30 31 32 33

10:15:08 10:18:14 10:20:33 10:23:32 10:25:53 10:28:44 10:32:07 10:35:00 10:36:46 10:40:09 10:43:26 10:46:04

10:16:25 10:19:16 10:21:36 10:24:37 10:26:56 10:30:11 10:33:30 10:35:51 10:37:05 10:41:12 10:44:39 10:46:45

0:01:17 0:01:02 0:01:03 0:01:05 0:01:03 0:01:27 0:01:23 0:00:51 0:00:19 0:01:03 0:01:13 0:00:41

10:09:00 10:27:27 10:29:12

0:01:15 0:01:10 0:00:31

TH vehicles from Maitidevi stopped to pass RT vehicles from Sinamangal

Date: May 9, 2018 34 35 36

10:07:45 10:26:17 10:28:41

Maximum Phase Time: Minimum Phase Time: Mean Phase Time: No. of Samples: Standard Deviation: Standard Error of the mean: Mean Phase Time:

0:02:00 0:00:19 0:01:09 36 0:00:26 0:00:04 0:01:09 ± 0:00:09 with 95 % confidence

118

Appendix-6.0: Photographs

Typical View of Gausala Approach (AM Peak hour)

Typical View of Sinamangal Approach (AM Peak hour)

Typical View of New Baneshwor Approach (AM Peak hour)

Typical View of Maitidevi Approach (AM Peak hour)

119

Appendix-6.0: Photographs (contd…)

Conducting Speed survey in New Baneshwor Approach

Conducting Speed survey in Gausala Approach

Traffic counting by observing in recorded video footage in AVIYAAN Consultancy P Ltd

CC camera set up at a vantage point at the intersection to record turning movements at the intersection

CC camera set up at a vantage point upstream of queues in the Gausala approach for recording vehicle arrival rate

CC camera set up at a vantage point upstream of queues in the Maitidevi approach for recording vehicle arrival rate

120

Appendix-6.0: Photographs (contd…)

CC camera set up at a vantage point upstream of queues in the New Baneshwor approach for recording vehicle arrival rate

Setting up the CC camera and digital video recorder

A typical view of vehicular traffic at the intersection during peak hours

Conducting back of queue (queue length) survey

121

122