West Bromwich Microsimulation Model Validation Report › download › downloads › id › 3255 › west... · West Bromwich Microsimulation Mott MacDonald Model Validation Report

West Bromwich Microsimulation Mott MacDonald Model Validation Report Sandwell Metropolitan Borough Council

S-1 225649/01/B - March 2008/S-1 of 2 H:\S_Shared\WB Microsimulation Modelling report.doc/AC

Sandwell Metropolitan Borough Council PO Box 42 Development House West Bromwich

West Bromwich Microsimulation

Model Validation Report

March 2008 Mott MacDonald Canterbury House 85 Newhall Street Birmingham B3 1LZ UK



Tel: 44 (0)121 2374000 Fax: 44 (0)121 2374001



West Bromwich Microsimulation

Model Validation Report

Issue and Revision Record Rev Date Originator

Checker

Approver

Description

a December 2007

Ashish Chandra

Sonal Ahuja

Tom Van Vuren Unchecked Draft

01 March 2008

Ashish Chandra

Sonal Ahuja

Tom Van Vuren Unapproved Draft

This document has been prepared for the titled project or named part thereof and should not be relied upon or used for any other project without an independent check being carried out as to its suitability and prior written authority of Mott MacDonald being obtained. Mott MacDonald accepts no responsibility or liability for the consequence of this document being used for a purpose other than the purposes for which it was commissioned. Any person using or relying on the document for such other purpose agrees, and will by such use or reliance be taken to confirm his agreement to indemnify Mott MacDonald for all loss or damage resulting therefrom. Mott MacDonald accepts no responsibility or liability for this document to any party other than the person by whom it was commissioned. To the extent that this report is based on information supplied by other parties, Mott MacDonald accepts no liability for any loss or damage suffered by the client, whether contractual or tortious, stemming from any conclusions based on data supplied by parties other than Mott MacDonald and used by Mott MacDonald in preparing this report.


i 225649/01/B - March 2008/i of vii H:\S_Shared\WB Microsimulation Modelling report.doc/AC

List of Contents Page

Summary S-1

Chapters and Appendices

1 Introduction 1-1

2 Scope of the Study 2-1 2.1 Study Area 2-1 2.2 Time periods 2-1 2.3 Simulation Software 2-2

3 Pedestrian and Traffic Surveys 3-1 3.1 Survey Schedules and Dates: 3-1 3.2 Survey Details 3-1

3.2.1 Journey Time Surveys 3-1 3.2.2 Video Surveys 3-1 3.2.3 Parking Demand Surveys 3-2 3.2.4 Parking Choice Surveys 3-3 3.2.5 Classified Automatic Traffic Counts with Speed discrimination. 3-4 3.2.6 Pedestrian Surveys 3-4 3.2.7 CCTV camera recording and Analysis: 3-5

3.3 Data Analysis 3-5

4 Modelling Methodology 4-1 4.1 Initial Model Development using the PRISM Strategic Model 4-2 4.2 Development of West Bromwich VISSIM Network 4-3

4.2.1 Network Area 4-4 4.2.2 Model Zoning Plan 4-4 4.2.3 Traffic Composition 4-4 4.2.4 Public Transport 4-5 4.2.5 Link Types 4-5 4.2.6 Pedestrian Network and Pedestrian Route Choice 4-6

4.3 Development of Demand Matrices using West Bromwich Strategic VISUM Interface 4-6

4.3.1 VISUM Network Development 4-7 4.3.2 Development of Prior Matrices 4-8 4.3.3 Matrix Estimation 4-9 4.3.4 Calibration of VISUM Interface 4-10

5 Calibration and Validation of Base Year VISSIM Models 5-1 5.1 VISSIM Model - AM Peak 5-1

5.1.1 Traffic Flows - AM Peak 5-1 5.1.2 Modelled Journey Time on Routes - AM Peak 5-2


ii 225649/01/B - March 2008/ii of vii H:\S_Shared\WB Microsimulation Modelling report.doc/AC

5.1.3 Overall Network Performance - AM Peak 5-7 5.2 VISSIM Model - PM Peak 5-7

5.2.1 Traffic Flows - PM Peak 5-7 5.2.2 Modelled Journey Time on Routes - PM Peak 5-8 5.2.3 Overall Network Performance - PM Peak 5-12

5.3 Overall Network comparison between AM and PM peak base year models 5-13

6 Conclusions 6-1

Appendix A Survey Results and Data Analysis A-1 A.1 Peak hour Analysis (Appendix A1) A-1

A.1.1 Traffic flows (Appendix A1.1) A-1 A.1.2 AM Peak (Appendix A1.2) A-3 A.1.3 PM Peak (Appendix A1.3) A-8 A.1.4 Inter-peak (Appendix A1.4) A-13

A.2 Traffic Zones (Appendix A2) A-14 A.3 Speed Flow Distributions (Appendix A3) A-16 A.4 Travel Time Analysis (Appendix A4) A-26 A.5 Traffic Composition (Appendix 5) A-44 A.6 Parking Demand (Appendix A6) A-45 A.7 Parking Choice Surveys (Appendix A7) A-49 A.8 Pedestrian Perception Surveys (Appendix A8) A-81

A.8.1 Observation sample A-82 A.8.2 Stated responses A-86

A.9 Pedestrian Demand - Link Counts (Appendix A9) A-93 A.9.1 Pedestrian counts A-93 A.9.2 OD Movements survey A-95

A.10 Crime data A-103

Appendix B Performance of base Year Models B-105 B.1 Calibration and Validation Criteria for Link Flows (Appendix B1) B-105 B.2 AM Peak Base Year Models (Appendix B2) B-1

B.2.1 AM Peak Base Year Link Count Validation (Appendix B2.1) B-1 B.2.2 AM Peak Modelled Journey Time Validation Results (Appendix B.2.2) B-1 B.2.3 AM Peak Modelled Queue Lengths (Appendix B.2.3) B-5

B.3 PM Peak Base Year Models (Appendix B.3) B-1 B.3.1 PM Peak Base Year Link Count Validation (Appendix B.1.1) B-1 B.3.2 PM Peak Modelled Journey Time Confidence Checks (Appendix B.3.2) B-1 B.3.3 PM Peak Modelled Queue Lengths (Appendix B.3.3) B-2

Appendix C Pedestrian Simulation and Route Choice C-1


iii 225649/01/B - March 2008/iii of vii H:\S_Shared\WB Microsimulation Modelling report.doc/AC

LIST OF FIGURES

Figure 2.1: Extent of study area – West Bromwich VISSIM Microsimulation model 2-1 Figure 3.1: Location of Video Surveys 3-2 Figure 3.2: Location of Car Parking Surveys 3-3 Figure 3.3: Location of classified ATC Speed Surveys 3-4 Figure 4.1: Modelling Methodology 4-1 Figure 4.1: West Bromwich Town Centre Study Area in PRISM base year model 4-3 Figure 4.1: West Bromwich Town Centre Study Area in PRISM base year model 4-5 Figure 4.2: West Bromwich Strategic VISUM Model 4-7 Figure 4.3: Process for development of base year trip matrices 4-8 Figure 5.1: Journey time comparison for Route 1 in AM Peak Period 5-2 Figure 5.2: Journey time comparison for Route 2 NB in AM Peak Period 5-3 Figure 5.3: Journey time comparison for Route 2 SB in AM Peak Period 5-3 Figure 5.4: Journey time comparison for Route 3 NB in AM Peak Period 5-4 Figure 5.5: Journey time comparison for Route 3 SB in AM Peak Period 5-4 Figure 5.6: Journey time comparison for Route 4 NB in AM Peak Period 5-5 Figure 5.7: Journey time comparison for Route 4 SB in AM Peak Period 5-5 Figure 5.8: Journey time comparison for Route 5 NB in AM Peak Period 5-6 Figure 5.9: Journey time comparison for Route 5 SB in AM Peak Period 5-6 Figure 5.10: Journey time comparison for Route 1 in PM Peak Period 5-8 Figure 5.11: Journey time comparison for Route 2 NB in PM Peak Period 5-9 Figure 5.12: Journey time comparison for Route 2 SB in PM Peak Period 5-9 Figure 5.13: Journey time comparison for Route 3 NB in PM Peak Period 5-10 Figure 5.14: Journey time comparison for Route 3 SB in PM Peak Period 5-10 Figure 5.15: Journey time comparison for Route 4 NB in PM Peak Period 5-11 Figure 5.16: Journey time comparison for Route 4 SB in PM Peak Period 5-11 Figure 5.17: Journey time comparison for Route 5 NB in PM Peak Period 5-12 Figure 5.18: Journey time comparison for Route 5 SB in PM Peak Period 5-12 Figure 5.19: Overall Comparison of AM and PM peak base Year VISSIM Models 5-13 Figure A.1: 24 hours total weekday traffic variation - 2006 A-1 Figure A.2: 24 hours total weekday traffic variation - 2005 A-2 Figure A.3: 24 hours total weekday traffic variation - 2004 A-2 Figure A.4: Sites total average weekday traffic flow in 1 hour interval – AM (2005) A-3 Figure A.5: Sites total average weekday traffic flow in 1 hour interval – AM (2004) A-4 Figure A.6: Daily variation of peak hour traffic flow – AM (2005) A-4 Figure A.7: Daily variation of peak hour traffic flow – AM (2004) A-5 Figure A.8: Average AM Peak hour flow (0800-0900) – 2005 A-5 Figure A.9: Average AM Peak hour flow (0800-0900) – 2004 Part 1 A-6 Figure A.10: Average AM Peak hour flow (0800-0900) – 2004 Part 2 A-6 Figure A.11: Individual AM Peak flow variation - 2005 A-7 Figure A.12: Individual AM Peak flow variation - 2004 A-7 Figure A.13: Sites total average weekday traffic flow in 1 hour interval – PM (2005) A-8 Figure A.14: Sites total average weekday traffic flow in 1 hour interval – PM (2004) A-9 Figure A.15: Daily variation of peak hour traffic flow – PM (2005) A-9 Figure A.16: Daily variation of peak hour traffic flow – PM (2004) A-10 Figure A.17: Average PM Peak hour flow (1630-1730) – 2005 A-10 Figure A.18: Average PM Peak hour flow (1630-1730) – 2004 Part 1 A-11 Figure A.19: Average PM Peak hour flow (1630-1730) – 2004 Part 2 A-11 Figure A.20: Individual PM Peak flow variation - 2005 A-12


iv 225649/01/B - March 2008/iv of vii H:\S_Shared\WB Microsimulation Modelling report.doc/AC

Figure A.21: Individual PM Peak flow variation - 2004 A-12 Figure A.22: Pedestrian flow peak hour – Video surveys 2006 A-13 Figure A.23: 2006 Speed distributions at various sites A-16 Figure A.24: 2005 Speed distributions at various sites A-16 Figure A.25: 2004 Speed distributions A-17 Figure A.26: A41 EXPRESSWAY North of Trinity Way - SB A-17 Figure A.27: A41 BLACK COUNTRY NEW ROAD West of Bilhay Lane - WB A-18 Figure A.28: A41 EXPRESSWAY North of Trinity Way - NB A-18 Figure A.29: A41 BLACK COUNTRY NEW ROAD West of Bilhay Lane - EB A-19 Figure A.30: B 4252 KENRICK WAY South of Doranda Way - SB A-19 Figure A.31: B 4149 PHOENIX STREET North of Charles Street - SB A-20 Figure A.32: B 4149 PHOENIX STREET North of Charles Street - NB A-20 Figure A.33: SAMS LANE East of Morris Street - EB A-21 Figure A.34: GREAT BRIDGE North of Slater Street - SB A-21 Figure A.35: GREAT BRIDGE North of Slater Street - NB A-22 Figure A.36: CLAYPIT LANE South of Dudley Street - NB A-22 Figure A.37: CLAYPIT LANE South of Dudley Street - SB A-23 Figure A.38: SAMS LANE East of Morris Street - WB A-23 Figure A.39: GLOVER STREET West of Maria Street - EB A-24 Figure A.40: GLOVER STREET West of Maria Street - WB A-24 Figure A.41: SAMS LANE East of Morris Street - EB A-25 Figure A.42: Route 1 for journey time observations A-26 Figure A.43: Journey time on route 1 – AM Peak hour observations A-27 Figure A.44: Journey time on route 1 – IP Peak hour observations A-27 Figure A.45: Journey time on route 1 – PM Peak hour observations A-28 Figure A.46: Route 2 for journey time observations A-28 Figure A.47: Journey time on route 2 NB – AM Peak hour observations A-29 Figure A.48: Journey time on route 2 SB – AM Peak hour observations A-29 Figure A.49: Journey time on route 2 – IP Peak hour observations A-30 Figure A.50: Journey time on route 2 – PM Peak hour observations A-31 Figure A.51: Route 3 for journey time observations A-32 Figure A.52: Journey time on route 3 – AM Peak hour observations A-32 Figure A.53: Journey time on route 3 – IP Peak hour observations A-34 Figure A.54: Journey time on route 3 – PM Peak hour observations A-35 Figure A.55: Route 4 for journey time observations A-36 Figure A.56: Journey time on route 4 – AM Peak hour observations A-37 Figure A.57: Journey time on route 4 – IP Peak hour observations A-38 Figure A.58: Journey time on route 4 – PM Peak hour observations A-39 Figure A.59: Route 5 for journey time observations A-40 Figure A.60: Journey time on route 5 – IP Peak hour observations A-42 Figure A.61: Journey time on route 5 – PM Peak hour observations A-43 Figure A.62: Traffic composition zoning map A-44 Figure A.63: Parking demand at the Queen Street car park A-45 Figure A.64: Parking demand at the Multi Storey car park A-45 Figure A.65: Parking demand at the Spon Lane car park A-46 Figure A.66: Parking demand at the Oak Road car park A-46 Figure A.67: Parking demand at the High Street on-street parking spaces A-47 Figure A.68: Cumulative parking demand at the surveyed parking facilities A-47 Figure A.69: Average duration of stay A-48 Figure A.70: Parking choice questionnaire A-49 Figure A.71: Number of occupants (Queens Street) A-50 Figure A.72: Number of occupants (Multi Storey) A-50 Figure A.73: Number of occupants (Spon Lane) A-51


v 225649/01/B - March 2008/v of vii H:\S_Shared\WB Microsimulation Modelling report.doc/AC

Figure A.74: Number of occupants (Oak Road) A-51 Figure A.75: Number of occupants (High Street) A-52 Figure A.76: Number of occupants A-52 Figure A.77: Legend for pie charts from A.74 to A.79 A-53 Figure A.78: Reason for being at the origin address (Queens Street) A-53 Figure A.79: Reason for being at the origin address (Multi Storey) A-54 Figure A.80: Reason for being at the origin address (Spon Lane) A-54 Figure A.81: Reason for being at the origin address (Oak Road) A-55 Figure A.82: Reason for being at the origin address (High Street) A-55 Figure A.83: Reason for being at the origin address A-56 Figure A.84: How long did it take you reach the parking facility (Queens Street)? A-56 Figure A.85: How long did it take you reach the parking facility (Multi Storey)? A-57 Figure A.86: How long did it take you reach the parking facility (Spon Lane)? A-57 Figure A.87: How long did it take you reach the parking facility (Oak Road)? A-58 Figure A.88: How long did it take you reach the parking facility (High Street)? A-58 Figure A.89: How long did it take you reach the parking facility? A-59 Figure A.90: Legend for pie charts from A.87 to A.92 A-59 Figure A.91: Initial choice of car park (Queens Street) A-60 Figure A.92: Initial choice of car park (Multi Storey) A-60 Figure A.93: Initial choice of car park (Spon lane) A-61 Figure A.94: Initial choice of car park (Oak Road) A-61 Figure A.95: Initial choice of car park (High Street) A-62 Figure A.96: Initial choice of car park A-63 Figure A.97: Was this parking facility your first choice (Oak Road)? A-63 Figure A.98: Was this parking facility your first choice (Queens Street)? A-64 Figure A.99: Was this parking facility your first choice (Multi Storey)? A-64 Figure A.100: Was this parking facility your first choice (Spon Lane)? A-65 Figure A.101: Was this parking facility your first choice (High Street)? A-65 Figure A.102: Was this parking facility your first choice? A-66 Figure A.103: Legend for pie charts from A.100 to A.104 A-66 Figure A.104: Reason for alternative parking facility (Queens Street) A-67 Figure A.105: Reason for alternative parking facility (Spon Lane) A-67 Figure A.106: Reason for alternative parking facility (Oak Road) A-68 Figure A.107: Reason for alternative parking facility (High Street) A-68 Figure A.108: Reason for alternative parking facility A-69 Figure A.109: How much time you spent to find alternative parking? A-69 Figure A.110: How often do you come to West Bromwich? A-70 Figure A.111: Legend for pie charts from A.108 to A.113 A-70 Figure A.112: How often do you park your car in this car park (Queens Street)? A-71 Figure A.113: How often do you park your car in this car park (Multi Storey)? A-71 Figure A.114: How often do you park your car in this car park (Spon Lane)? A-72 Figure A.115: How often do you park your car in this car park (Oak Road)? A-72 Figure A.116: How often do you park your car in this car park (High Street)? A-73 Figure A.117: How often do you park your car in this car park? A-74 Figure A.118: Reason for going to West Bromwich A-75 Figure A.119: How did you complete your journey to final destination? A-76 Figure A.120: How long does it take to walk to final destination (Queens Street)? A-76 Figure A.121: How long does it take to walk to final destination (Multi Storey)? A-77 Figure A.122: How long does it take to walk to final destination (Spon Lane)? A-77 Figure A.123: How long does it take to walk to final destination (Oak Road)? A-77 Figure A.124: How long does it take to walk to final destination (High Street)? A-78 Figure A.125: How long take to final destination? A-78 Figure A.126: Reason for going to the final destination A-79


vi 225649/01/B - March 2008/vi of vii H:\S_Shared\WB Microsimulation Modelling report.doc/AC

Figure A.127: Entitled to a disabled car parking space A-79 Figure A.128: Do you have access to a free parking space? A-80 Figure A.129: Do you hold a season ticket? A-80 Figure A.130: Pedestrian perception questionnaire A-81 Figure A.131: Q1 – Gender distribution A-82 Figure A.132: Q2 – Age distribution A-82 Figure A.133: Q3 – Traffic light while crossing the intersection A-83 Figure A.134: Q4 – Behaviour at intersection A-83 Figure A.135: Q5 – Gap between traffic and pedestrian A-84 Figure A.136: Q6 – Pedestrian movement at intersection A-84 Figure A.137: Pedestrian characteristics A-85 Figure A.138: Crossing behaviour by gender A-85 Figure A.139: Running behaviour at traffic signals A-86 Figure A.140: Purpose of journey to the main destination A-86 Figure A.141: Are the traffic signals clear to understand A-87 Figure A.142: Do you think there is enough time to cross? A-87 Figure A.143: What traffic signal sequence would you like to see? A-88 Figure A.144: When there is low traffic volume? A-88 Figure A.145: Red for a long time? A-89 Figure A.146: When accompanied by children? A-89 Figure A.147: When carrying luggage? A-90 Figure A.148: 95 percentile responses for long wait duration at pedestrian crossings A-90 Figure A.149: 95 percentile responses for short wait duration at pedestrian crossings A-91 Figure A.150: Suitability of different measures to make pedestrians more patient at traffic signals A-91 Figure A.151: Ranking of what will make pedestrians patient at traffic signals A-92 Figure A.152: Pedestrian flow peak hour – Video surveys 2006 A-93 Figure A.153: Pedestrian counts A-94 Figure A.154: Pedestrian zones A-95 Figure A.155: Pedestrian trip lengths A-96 Figure A.156: Factors which affect pedestrian route choice while walking A-97 Figure A.157: Rankings of individual factors based on survey responses A-98 Figure A.158: Average ratings when final destination is HOME A-98 Figure A.159: Average ratings when final destination is USUAL WORKPLACE A-99 Figure A.160: Average ratings when final destination is EMPLOYER’S BUSINESS A-99 Figure A.161: Average ratings when final destination is EDUCATION A-100 Figure A.162: Average ratings when final destination is SHOPPING A-100 Figure A.163: Average ratings when final destination is PERSONAL BUSINESS A-101 Figure A.164: Average ratings when final destination is LUNCH / MEAL A-101 Figure A.165: Average ratings when final destination is LEISURE / RECREATION A-102 Figure A.166: Average ratings when final destination is MEDICAL APPOINTMENT A-102 Figure A.167: Average ratings when final destination is OTHER A-103 Figure A.168: Crime rates in West Bromwich A-103 Figure A.169: Crime rates at major pedestrian movement locations – Top 10 crime locations in West

Bromwich A-104 Figure B.1: Journey Time Comparison in the AM base year VISSIM model B-1 Figure B.2: Average modelled queue lengths in AM Base year model B-1 Figure B.3.2: Journey Time Comparison in the PM base year VISSIM model B-3 Figure B.4: Average modelled queue lengths in AM Base year model B-1 Figure C.1: Pedestrian Network modelled within West Bromwich Microsimulation Models C-2 Figure C.2: Process Flowchart for developing the pedestrian model C-3 Figure C.3: Pedestrian Link Structure and Zones C-4 Figure C.4: VISUM network for pedestrian simulation C-5


vii 225649/01/B - March 2008/vii of vii H:\S_Shared\WB Microsimulation Modelling report.doc/AC

LIST OF TABLES

Table 3.1: Survey tasks and schedule of surveys 3-1 Table 5.1: AM Peak Base Year VISSIM Model Link Flow Calibration results 5-1 Table 5.2: AM Peak Base Year VISSIM Model Link Flow Validation results 5-1 Table 5.3: Average Network performance of all vehicles in AM peak base year models 5-7 Table 5.4: PM Peak Base Year VISSIM Model Link Flow Calibration results 5-7 Table 5.5: PM Peak Base Year VISSIM Model Link Flow Validation results 5-8 Table 5.6: Average Network performance of all vehicles in PM peak base year models 5-13 Table A.1: Traffic composition table A-44 Table A.2: Factors which can affect pedestrian route choice while walking A-96 Table B.1: AM Peak Base Year Calibration Counts and Modelled Flows B-1 Table B.2: AM Peak Base Year Validation Counts and Modelled Flows B-2 Table B.3: Journey Time Confidence intervals for AM Peak Period Route 1 West Bromwich High

Street B-3 Table B.4: Journey Time Confidence intervals for AM Peak Period Route 2: Kelvin Way to great

Western Way B-4 Table B.5: Journey Time Confidence intervals for AM peak period Route 3: Kenrick Way to Great

Western Way (through Expressway) B-4 Table B.6: Journey Time Confidence intervals for AM Peak period Route 4: Bromford Lane to

Vicarage Road B-4 Table B.7: Journey Time Confidence intervals for AM Peak Period Route 5: Newton Road to

Bromford Lane B-5 Table B.8: PM Peak Base Year Calibration Counts and Modelled Flows B-1 Table B.9: PM Peak Base Year Validation Counts and Modelled Flows B-2 Table B.10: Journey Time Confidence intervals for PM Peak Period Route 1 West Bromwich High

Street B-1 Table B.11: Journey Time Confidence intervals for PM Peak Period Route 2: Kelvin Way to great

Western Way B-1 Table B.12: Journey Time Confidence intervals for PM peak period Route 3: Kenrick Way to Great

Western Way (through Expressway) B-1 Table B.13: Journey Time Confidence intervals for PM Peak period Route 4: Bromford Lane to

Vicarage Road B-2 Table B.14: Journey Time Confidence intervals for PM Peak Period Route 5: Newton Road to

Bromford Lane B-2 Table C.1: AM Peak Calibration of pedestrian link volumes in VISSIM C-6 Table C.2: Travel Time Calibration for pedestrians C-7 Table C.3: PM Peak Calibration of pedestrian link volumes in VISSIM C-8 Table C.4: Travel Time Calibration for pedestrians C-9



Executive Summary

A VISSIM microsimulation traffic model and a VISUM assignment model of West Bromwich town centre and surrounding areas have been developed to represent the present and future traffic situation in the study area. In addition to this base year model, microsimulation and VISUM assignment models of the future scenarios have been developed to test the design options and the effect of proposed land use developments.

Several option models have been built sequentially, on top of the base model, to represent the different network situations and traffic growth scenarios in the study area. This report however only details the methodology of building the base year models and the validation statistics of the base year models. ( future models????)

All models were developed to represent two peak periods:

• The average weekday AM peak period (0730-0900) (March 2006),

• The average weekday PM peak period (1600-1730) (March 2006).

The model has been based not only on historic traffic data (2000 – 2005) and the PRISM strategic model but also on new data collected between January to March 2006.

In addition to the vehicular assignment model, the model also includes a pedestrian assignment model in which the pedestrians can choose between alternative routes in the town centre based on minimising their travel costs.

The model has been overlaid with a parking choice model, where motorists can choose between alternative car parks as a function of car parking accessibility, capacity, distance to final destination, car parking fees etc.

These new models have been developed on the basis of special car parking and pedestrian surveys conducted during February-March 2007. These sub models only focus on the town centre area. The results from the data collection exercise, the pedestrian models and the parking choice models have been presented in the form of PowerPoint presentations. These presentations which have been presented to and been approved by the client through the progress of the project, have been supplied in the Appendices. In addition two publications on parking choice models and pedestrian route choice models on the basis of data collected from this study were made at the World Congress on Transport Research (WCTR), at University of California, Berkeley, in July 2007. These papers have also been attached in the appendices.

The base models were successfully calibrated against the existing traffic counts and observed journey times according to the DMRB criterion. In addition we have validated the models to observed driving behaviour and queues.

A series of workshops were organised between the modelling team and the client engineers who have good local knowledge of the area, between January 2007 and April 2007 to check the visual calibration of the base models. They concluded….



The base year models correctly replicate queuing on all major intersections, the delays experienced by the traffic, and the congestion problems at the major bottlenecks such as the High Street in the town centre, M5 J1, All Saints Expressway, Horsley Heath Road.

This report supplements the modelling work, reported as above, and gives a very good validation of the base year models. It also provides the model validation report of the base network models, the facts, and assumptions taken into account while building the models and the analysis of the results obtained from the modelling exercise.


1-1 225649/01/B March 2008/1-1 of 2 H:\S_Shared\WB Microsimulation Modelling report.doc/AC

1 Introduction

Sandwell Metropolitan Borough Council (SMBC) commissioned Mott MacDonald in November 2005 to produce a scoping report to investigate the possible approaches to model the impacts of the proposed developments and associated transport infrastructure improvements in and around the West Bromwich town centre area. Following the findings of the scoping study, Sandwell MBC commissioned Mott MacDonald in January 2006 to conduct various traffic and pedestrian surveys and develop detailed VISSIM based Microsimulation models of West Bromwich town centre and the surrounding area.

Further to the commissioning of the tasks, the following traffic and pedestrian surveys were carried out in the Bromwich town centre in February 2006,:

• Pedestrian link flow surveys and perception surveys;

• Parking demand and parking Choice surveys;

• ATC traffic link flow survey;

• Journey time surveys; and

• Video surveys.

The survey data collected from the study area, coupled with data from earlier surveys conducted in 2004 and 2005, was used to develop VISSIM microsimulation and VISUM strategic models for the study area. The following base year models were developed during the course of the study:

• 2006 base year VISUM strategic models for AM, PM and inter-peak periods;

• 2006 base year VISSIM microsimulation models for AM, PM and inter-peak periods.

Only the AM and PM peak models have been calibrated and validated. Although inter-peak models (including networks and matrices) were built based on the data collected, these have not been calibrated and validated and hence have not been used in any future appraisal.

In addition the following options haven developed and tested:

• 2011 Scenario VISUM strategic models for AM, and PM periods of proposed changes without ‘underpass’ at All Saints Way junction;

• 2011 Scenario VISUM strategic models for AM, and PM periods – with the proposed ‘underpass’ at All Saints Way junction;

• 2021 Scenario VISUM strategic models for AM, and PM periods - without…..???

• 2021 Scenario VISUM strategic models for AM, and PM periods – with the proposed ‘underpass’ at All Saints Way junction;


1-2 225649/01/B March 2008/1-2 of 2 H:\S_Shared\WB Microsimulation Modelling report.doc/AC

• 2011 Scenario VISSIM microsimulation models for AM, and PM periods without ‘underpass’ at All Saints Way junction; and

• 2011 Scenario VISSIM microsimulation models for AM, and PM periods – with the proposed ‘Underpass’ at All Saints Way Junction.

In addition to the traffic simulation, the 2001VISSIM microsimulation models include a detailed pedestrian network and simulation, integrated with the main traffic simulation model. The 2011 VISSIM microsimulation option models also include an ‘attraction based parking choice’model for cars going to town centre car parks. The model also includes a pedestrian assignment model in which pedestrians can choose between alternative routes in the town centre based on minimising their travel costs. The model has been overlaid with a parking choice model, where motorists can choose between alternative car parks on minimising the total route cost which is function of car parking accessibility, capacity, distance to final destination, car parking fees etc.

The model has been based not only on historic traffic data (2000 – 2005) and the PRISM strategic model but also on new data collected between January and March 2006.

The PRISM (Policy Responsive Integrated Strategy Model) model has been developed as the multi-modal model of the West Midlands metropolitan authorities (as well as Centro and the Highways Agency). It is a strategic model, based on VISUM macro simulation software, developed by Mott MacDonald. The model consists of detailed networks of the major road and public transport systems.

The West Bromwich models (WB models) have been derived from the PRISM strategic model. The models have been created using cordoned PRISM model which have given the basic network and trip matrices to create the models. The zoning structure of the WB models is 100% identical to the PRISM models which are subsets of the PRISM zones. This has been done to ensure compatibility of the demand matrices between PRISM and WB models. The Strategic VISUM WB models have been given the same speed flow specification and network data specification as the PRISM model. As the network for the WB models have been built using aerial photographs and have each and every road in the study area mapped into the models, the new models are more disaggregated and have a more intensive network coverage compared to PRISM.

In addition to the above, the original RSI and car parking OD data used for building the PRISM matrices has been used to build the trip OD matrices for vehicular and pedestrians for the base year.

Finally the PRISM model has been used to assess the forecast year traffic growth and future year matrices as they not only contain the committed schemes for the West Bromwich but for entire West Midlands region.

The WB models are in complete unity and derivates of the PRISM model.

This report provides details of the modelling work and gives an analysis of the results achieved. It provides a detailed model validation report of the base network models, stating the assumptions made in building the VISSIM microsimulation and VISUM strategic simulation models.


2-1 225649/01/B - March2008/2-1 of 2 H:\S_Shared\WB Microsimulation Modelling report.doc/AC

2 Scope of the Study

2.1 Study Area

In addition to the town centre area, the modelled area for the West Bromwich Town Centre VISSIM model includes all the major entry and exit points from the centre. The study area also includes all the likely diversion routes available to avoid congested areas around the centre including the A41 Expressway, A461 Horsley Heath, and M5 Junction 1. Following discussions with SMBC, the Burnt Tree Island model was included in the West Bromwich model to study the impact of traffic management measures on Horsley Heath like Red Routes. The final extent of the West Bromwich Microsimulation model is shown in Figure 2.1.

Figure 2.1: Extent of study area – West Bromwich VISSIM Microsimulation model

2.2 Time periods

Based on the analysis of traffic data collected through automated traffic counts and video surveys, average weekday AM peak, average weekday PM peak, and Inter Peak period have been modelled to represent the existing traffic scenario. However, the models have been calibrated and validated only from the AM and PM peak periods.

Motorway Pedestrian Links Traffic Links



Each of these time periods were modelled separately, in terms of demand and the network description (including signal timings, bus routes, and frequencies). In addition, a 30-minute build-up (30-minutes before peak hour) was also modelled for all three peak periods. No modelled data has been extracted, or analysed, for the build-up periods.

From the current surveys, following peak hours were identified:

Detailed analysis of the survey data including daily traffic profiles for the surveyed sites is presented in Appendix A

2.3 Simulation Software

The West Bromwich VISSIM models have been developed using the VISSIM microsimulation software, version 4.2-30. In addition, the strategic interface VISUM models were developed using VISUM strategic simulation software, version 940-14.

PRISM 1.0 has been used in developing trip matrices and base year networks.

• AM peak period from 08:00 – 09:00 hours

• PM peak period from 16:30 – 17:30 hours; and

• Inter-peak period from 12:00-13:00 hours. (this period has not been calibrated and validated)

In addition to the above, half hour pre-peak build-up time periods were also modelled in all the base year and option scenario VISSIM models.



3 Pedestrian and Traffic Surveys

3.1 Survey Schedules and Dates:

Table 3.1 gives a summary of the surveys undertaken and the dates on which the surveys were conducted.

Table 3.1: Survey tasks and schedule of surveys

3.2 Survey Details

3.2.1 Journey Time Surveys

The journey time surveys were conducted on 21st February during the following hours:

AM Period: 0700 -1000 Inter Peak Period: 1100 -1400 PM Period: 1500 -1900

A total of 9 teams were deployed in the town centre area. This data has been used to validate the base year models. Details of the journey time routes and a detailed analysis is presented in Appendix A.1.

3.2.2 Video Surveys

Continuous 12 hour video surveys were conducted at four signalised intersections in the study area. The exact location of these surveys is given below in Figure 3.1. These locations have been decided in consultation with Sandwell MBC. In addition to classified traffic data, pedestrian flows at the intersections were analysed using the video surveys. The video surveys were carried out at the same time as the journey time surveys on 21st February for a continuous 12 hour period between 0700 -1900.

In addition to determining the traffic peak periods, the video surveys were used in the analysis of the pedestrian peak hour. The video survey data analysis used for estimation of the peak pedestrian data is presented in Appendix A.1.4.

S No Survey Tasks Survey Date

1 ATC Survey 19th February – 26th February (1 week)2 Journey Time Surveys 21st February (Tuesday)3 Video Surveys 21st February (Tuesday)4 Pedestrian Questionnaire 22nd February (Wednesday)5 Parking Surveys 23rd February (Thursday)6 Pedestrian Tracking Routing and Counts 20th February (Monday)



Figure 3.1: Location of Video Surveys

3.2.3 Parking Demand Surveys

Continuous 12 hour parking occupancy and duration surveys were conducted at five parking sites in the study area between 0700 and 1900 hours.

Continuous 12 hour parking in-out counts surveys were conducted at five parking sites in the study area on 23rd February between 0700 -1900. Registration plate surveys were conducted at the following four sites shown in Figure 3.2:

1. Spon Lane Car Park; 2. Multi Storey Car Park; 3. Queen Street Car Park; and 4. Oak Road Car Park.

In addition, parking beat surveys were conducted on the High Street between the Victoria Street and Sandwell Road junctions. The data collected was utilised to determine car park utilisation, occupancy and duration.

A detailed analysis of the data collected from the parking surveys is presented in Appendix A.6.



Figure 3.2: Location of Car Parking Surveys

Based on client consultations and site visits, it is suggested that a combination of parking beat and registration number plate matching surveys will be conducted on the car parks. This will give an indication of car park utilisation, occupancy and duration. The number of staff required to conduct the surveys is a function of number of entrances and exits at the car park and the size of the car park. An initial and final beat of the cars that are parked in the car park will be done

3.2.4 Parking Choice Surveys

Parking Choice Surveys were conducted at the above mentioned car parks using pre-paid postal questionnaires. The questionnaires were handed out to the people parking at the car parks on the 23rd February. The respondents were given an incentive of one £200 cash prize to complete and return the questionnaire by 3rd March.

In order to get the data on origins, destinations and attractiveness of each car park an addition car park user survey is also proposed to be conducted. A questionnaire, aimed at collecting information about the user, printed on a pre paid postcard will be handed out during the survey in the car parks. The respondents will given an incentive to complete and return the questionnaire in 7 days time and enter into a prise draw for £500. In most cases these questionnaire will be handed out by enumerators at pay and display machines.



A detailed analysis of the data collected from the parking surveys is presented in Appendix A6 and A7.

3.2.5 Classified Automatic Traffic Counts with Speed discrimination.

Automatic Traffic Count Surveys with speed discrimination and classification of vehicles were conducted on two locations on A41 Expressway as shown in Figure 3.3. The surveys were carried out on a 24 hour basis, continuously for a period of two consecutive weeks in February 2006.

Figure 3.3: Location of classified ATC Speed Surveys

3.2.6 Pedestrian Surveys

Three types of pedestrian surveys were conducted in West Bromwich town centre namely:

1. Pedestrian counts;

2. Pedestrian tracking and OD surveys; and

3. Pedestrian perception face to face interviews with observations;



Pedestrian tracking surveys were conducted to identify pedestrian routes to assist in construction of a pedestrian matrix. In total 15 enumerators were used in tracking people between prefixed zones on the defined pedestrian network. In addition 15 minute lateral pedestrian counts were conducted at several locations on foot paths. 10 enumerators were also to count pedestrian on links (footpaths). This gave a total of approximately 90 count locations per peak period. The pedestrian cross flow survey was carried out simultaneously with the pedestrian tracking survey.

The pedestrian counts and tracking (O/D) surveys were conducted during the following hours: AM Period: 0700 -1000 Inter Peak Period: 1100 -1400 PM Period: 1500 -1800 Pedestrian Perception Surveys

In addition specific data to understand the pedestrian behaviour and factors affecting their route choice was also collected. This was conducted during the following hours:

Morning: 0930-1430

Evening: 1500 -1800

The survey was conducted around signalised intersections, pedestrian crossings and interchanges, locations such as the bus terminal and tram station and was coupled with CCTV camera recordings. This data is analysed and presented in Appendix A8

The survey was utilised to build the pedestrian assignment model (Appendix C)

3.2.7 CCTV camera recording and Analysis:

In addition, CCTV recordings from Sandwell MBC CCTV cameras were utilised to collect additional information on traffic and pedestrian behaviour.

3.3 Data Analysis



4 Modelling Methodology

Figure 4.1 below shows the methodology for developing West Bromwich traffic models. The following sections describe the methodology and assumptions used for developing the VISSIM microsimulation and VISUM strategic models using primary and secondary data sources including the PRISM model.

Figure 4.1: Modelling Methodology

Error Checking - Review inputs - Review simulation

14

- Analyse existing traffic data - Establish the need for further traffic data collection

Initial Modeling

Calibration and

Validation

Model Application

Analysis of Survey Data 1 1

Additional Primary Data Analysis - Traffic volumes, speeds, journey time, bus dwell time - Base maps / inventory /field observations - PRISM zones, network, cordon and OD matrices

1 3 Is any

additional traffic data available?

Base Model Development

- Input network data - Input traffic data - Develop the cordon VISUM model from PRISM - -Develop base year matrix using VISUM partial model outputs and observed data

1 2

Working Model before Calibration

Calibration Process Compare model parameters to observed data - Flows match? - Congestion in right places? Queues at right locations

1 5

Acceptable match?

Calibrated and Validated Model

Alternatives Analysis - Forecast demand - Base case - Project alternatives

1 8

Final Report - Key results - Technical documentation - Animations

1 9

Adjust Model Parameters - Modify global parameters - Modify link parameters - Modify route choice parameters

1 7

Client Meeting and Presentation Workshop

Client Meeting and Presentation Workshop

Yes

No

Yes No

Validation Process Compare model outputs to independent observed data - Journey times match?

1 6

Acceptable match?

Yes No



4.1 Initial Model Development using the PRISM Strategic Model

In the PRISM model, the highway network was converted from a GIS database. The geographic representation and geometry is accurate up to the level of unclassified roads. The public transport model was based on CENTRO’s VIPS models and the Strategic Rail Authority’s Planet model.

The model is calibrated and validated to the 2001 base year. In addition, there are future year networks and matrices available for years 2011, 2021, and 2031. Separate models exist for the weekday AM peak (0700-0930), Inter-peak (0930-1530), PM peak (1530-1900) and off-peak (1900-0700) periods. The trip matrices were built using data from the West Midlands Transport Survey 2001 (WMTS2001), 2001 Highway Agency motorway roadside interview surveys, and 2001 West Midlands household interview data (2001 HHI).

The PRISM model has been calibrated and validated to a number of motorway screen lines and town centre cordons. In the study area, the PRISM model is calibrated to M5 screen line counts, and various link flow counts and RSI information for the West Bromwich town centre cordon.

The West Bromwich models (WB models) have been derived from the PRISM strategic model. The models have been created using cordoned PRISM model which has given the basic network and trip matrices to create the models. The zoning structure of the WB models is based on the PRISM zones which were further disaggregated to create WB zones. This has been done to ensure compatibility of the demand matrices between PRISM and WB models.

The Strategic VISUM WB models have the same speed flow specification and network data specification as the PRISM model. As the network for the WB models have been built using aerial photographs and have each and every road in the study area mapped into the models, the new models are more disaggregated and have a more intensive network coverage compared to PRISM.

In addition to the above, the original RSI and car parking OD data used for building the PRISM matrices has been used to build the trip OD matrices for vehicular and pedestrians for the base year.

Finally the PRISM model has been used to assess the forecast year traffic growth and future year matrices as they not only contain the committed schemes for the west Bromwich but for entire West Midlands region.

The PRISM model has be utilised in two ways:

1. To create a sub-model –The PRISM model has been used to build a sub-model, by cordoning out the study area, adding further local detail, and refining the zoning system. This has provided the initial demand and network data.

2. To provide demand matrices –The PRISM model has been used to provide future year matrices for 2011 and 2021 scenarios. PRISM reference cases for the future years (2011, 2021) exist. The matrices from these scenarios have been utilised as a starting point to estimate the demand in the future years.



Figure 4.1: West Bromwich Town Centre Study Area in PRISM base year model

4.2 Development of West Bromwich VISSIM Network

The VISSIM microsimulation models for West Bromwich town centre covers the town centre surrounded by the A40 Expressway and M5 motorway as shown in Figure 2.1. External traffic connectors links (show in brown in Figure 2.1) were used to model the traffic coming into the study area from the immediately surrounding region. The following sections describe the scope of the West Bromwich VISSIM models and the assumptions made for modelling purposes.

• In the first instance a cordon network was created from the PRISM model. This provided an initial network and trip matrix.

• As the PRISM network mainly contains strategic routes and large zone representation, additional links and zones were added to the cordoned network. The large PRISM zones were disaggregated on the basis of present and proposed land use changes.

• These details were added in the VISUM software to create a detailed VISUM model of the study area.

Study Area



• Finally the VISUM network was exported to the VISSIM platform. The links and connectors were refined using aerial photographs provided by Sandwell MBC and site survey photographs and data collected from the CCTV cameras.

• The refined network from VISSIM was once again exported back into VISUM to get an identical 100% match between zones, links and nodes of the VISSIM and VISUM networks.

• The cordoned matrix was disaggregated and refined using additional RSI data, land use data, Gravity model distribution and matrix estimation technique in VISUM to get a good base year matrix. This is explained in detail in Section 4.3.2.

• The base year networks obtained from the Burnt Tree models were also added to the cordoned VISSIM models. Please note that the demand from the Burnt Tree models has been assigned using fixed vehicle inputs and turning proportions in the base year model. This was converted to flexible matrix assignment for the future year matrices.

• Finally the calibrated and estimated matrices from the VISUM models were combined assigned in the VISSIM network.

4.2.1 Network Area

4.2.2 Model Zoning Plan

The study area was divided into various traffic zones on the basis of trip production and attractions from various land-use types and relative distance from the town centre. The following zones were created within the town centre:

1. Town Centre Zones

2. Internal Zones - outside town centre

3. Intermediate Zones - outside study area

4. External Zones – far outside study area

Details of the zoning plan are given in Appendix A.2

4.2.3 Traffic Composition

The traffic in the study area was broken down into the following main classes:

• Cars;

• Heavy Goods Vehicles (HGVs) and Light Goods Vehicles (LGVs);

• Public Transport Buses; and

• Pedestrians



These typologies were based on standard VISSIM vehicle specifications reflecting the average European vehicle-performance specifications. Each of the vehicle types was coded to have standard highest and desired acceleration and deceleration rates. All vehicle types except public transport buses were modelled using the VISSIM dynamic route assignment using independent origin-destination matrices.

The assignment is based on the generalised cost coefficients shown in the figure below.

Figure 4.1: West Bromwich Town Centre Study Area in PRISM base year model

Car HGV LGV

The convergence criteria of the VISSIM model were set to a maximum variation of 60% of travel time on all paths and time intervals between different iterations.

The detailed methodology for developing the demand matrices is given in Section 4.3. The detailed methodology for assigning pedestrian demand is given in Appendix C, section 4.2.6.

A detailed analysis of the observed traffic composition and a summary table of the composition in various parts of the study area is provided in Appendix A.5.

4.2.4 Public Transport

Public transport operations in West Bromwich Town centre was modelled on the basis of information available from Travel West Midlands and CENTRO. The public transport bus routes were modelled as static routes on which the public transport buses enter the network at fixed start times.

4.2.5 Link Types

All the links in the models are coded as urban motorised links with same driver behaviour based on the standard UK driving behaviour characteristics. However, as represented in Figure 2.1, based on the density and speed profile of traffic on various links, multiple link types were modelled as follows:

• Standard Urban Motorised Links (Grey) – with default average vehicle density on links;



• Rural/Residential Links (Sky Blue) – with low density slow moving traffic;

• Motorway Links (Navy Blue) – with high speed and large distance between vehicles;

• Low Density Urban Motorised Links (Light green) – with low density traffic especially as a result of on-street parking and frequent pedestrian crossings;

• High Density Urban Motorised Links (Dark green) - with high density traffic especially near signalised junctions on the expressway and highly congested corridors; and

• Feeder Links (Brown) – serving only as feeder links outside evaluation area connecting the intermediate zones outside model boundary with the network.

The driving behaviour on these link types was calibrated on the average standstill distance between vehicles and average safety distance between moving vehicles.

In addition to the above, two more link types were used:

• Pedestrian and cyclist only links (Pink) – for representing pedestrianised area and footpaths; and

• Bus/Taxi Only Links (Florescent Green).

4.2.6 Pedestrian Network and Pedestrian Route Choice

An integrated pedestrian model was developed on top of the traffic model to study the impact of pedestrian movements on the traffic. The pedestrian movement matrices were developed using the following sources:

• OD tracking survey;

• Car parking questionnaire survey; and

• Pedestrian perception survey

The pedestrian matrices were estimated using the raw OD matrices developed from the above and pedestrian link count data. A pedestrian route choice algorithm was developed to model the movement of pedestrians through the town centre links and the car parks as a function of….

A separate pedestrian model was developed to assign pedestrians through dynamic assignment of pedestrian demand matrices and the modelled paths and assignment was taken into the main model as static fixed route inputs. More detail about the pedestrian modelling methodology including calibration and validation of the pedestrian model is given in Appendix C.

4.3 Development of Demand Matrices using West Bromwich Strategic VISUM Interface

VISSIM is a simulation and assignment software and does not have the capability to estimate trip matrices. The trip matrices for VISSIM need to be derived from a larger often strategic model. For this, the PRISM strategic model was used as a starting point.



The traffic demand matrices for were split according to the observed traffic composition consisting of cars, LGV and HGV vehicle types, using a strategic VISUM model for the study area. The following sections describe the methodology for developing the strategic interface and demand matrices.

4.3.1 VISUM Network Development

The strategic VISUM network for the West Bromwich model was developed as a combination of PRISM and VISSIM microsimulation model as explained in Section 4.2. Figure 4.2 shows the extent of the strategic VISUM interface. The node and edge structure was matched with the VISSIM network and feeder links were modelled as links with high capacities. The exported VISUM model was further enhanced to assign and estimate the traffic matrices for cars and goods vehicles.

Figure 4.2: West Bromwich Strategic VISUM Model

(i) Link Hierarchy

The link types defined for the VISSIM model (ref. Section 4.1.5) were copied into the VISUM network. Link capacities and hierarchy was determined by the road categorisation and link geometries. Link speeds were modelled as per the average speed on the links. These were identical to link typology and definitions of the PRISM model.



(ii) Junction Controls

The traffic junctions in the West Bromwich VISUM model were modelled as nodes with a volume delay function based on node capacities and average delays at the junctions. The junctions were not enhanced further as it not considered as cost effective considering the final objective of developing a detailed VISSIM model. Route choice??

(iii) Public Transport

Public transport was not modelled as a separate demand segment while developing the demand matrices. However, public transport link flow volumes in PCUs were extracted from the VISSIM model and imported into the VISUM model as a capacity constraint.

4.3.2 Development of Prior Matrices

The prior matrices for further matrix calibration and estimation were developed from a variety of sources depending on the quality of data available and compatibility with rest of the OD travel information.

Figure 4.3: Process for development of base year trip matrices

RSI Data gives primary trip distributions

Level 1

Apply trip end constraints

Level 2

Car Park Capacity

ConstraintsLevel 3

Prior Matrices for Matrix Estimation

Road Side Interview Survey Data(2001 and 2003 surveys)

Land Use Information(Production and Attraction Constraints)

Car ParkIn-Out Counts)

RSI Data gives primary trip distributions

Level 1RSI Data gives

primary trip distributions

Level 1

Apply trip end constraints

Level 2 Apply trip end constraints

Level 2

Car Park Capacity

ConstraintsLevel 3

Car Park Capacity

ConstraintsLevel 3

Prior Matrices for Matrix Estimation

Road Side Interview Survey Data(2001 and 2003 surveys)

Land Use Information(Production and Attraction Constraints)

Car ParkIn-Out Counts)

PRISM Cordon Model



Figure 4.3 above shows the data sets used and the level at which the information was used while developing the prior matrices.

Initial Cordon Matrix from the PRISM model

The trip matrix obtained from the cordoned PRISM model was used as a starting point for building the OD matrices. This was a coarse matrix and needed to be disaggregated. Consistency was maintained between PRISM and the finer WB model zones.

(i) Roadside Interview Data

Roadside Interview (RSI) surveys were conducted in West Bromwich in 2003 to produce the previous town centre VISSIM model. In addition, 2001 RSI surveys were conducted as part of data gathering and monitoring and were used for the development of PRISM-the West Midlands strategic VISUM model.. Both these RSI datasets were used to incorporate trip distribution information for the development of the prior matrices. The RSI Matrices were used to develop the platform for the prior matrices by adding trip end constraints using detailed land-use information from the study area.

(ii) Land-Use Data – Gravity Model Interface

The RSI matrices developed were used in a Gravity model furnessing based on the anticipated total trip productions and attractions form the various zones. The gravity model was distributed according to the observed trip length frequency distribution rates obtained from the Car Park and RSI surveys.

In addition the average trip rates based on historical data included in the‘GENERATE’ trips database was used to determine the total trip generation and attraction from each zone based on its present land use composition.

Trip length distribution observed in the 2003 RSI surveys and the car parking surveys were used to furness the RSI matrices and the trip end constraints to further enhance the raw matrices. A double constrained gravity model distribution was used to get a prior raw matrix.

(iii) Car parking surveys 2006

Finally, the car parking in-out surveys were used to determine the total productions and attractions from all the local car parks and on-street parking spaces inside the town centre zones. This information was used to monitor the total number of trip entering and leaving West Bromwich town.

4.3.3 Matrix Estimation

The final prior matrices developed as above were assigned in VISUM and calibrated against the observed link flow volumes and turning movements using the strategic VISUM interface. The matrices were then balanced to produce observed counts using the T-Flow Fuzzy matrix estimation technique available within the VISUM software. Care was taken not to distort any observed individual OD pairs and the target in and out that were accurately measured in the surveys.



4.3.4 Calibration of VISUM Interface

The VISUM models were calibrated to the link flow volumes and travel times on routes and matrices were then taken forward into the VISSIM models for further calibration and validation of the VISSIM base year models. This calibration result has not been included in this report as it mainly focuses on the VISSIM model calibration. The calibration of the Strategic VISUM model is given in attached PowerPoint analysis. The VISUM model was created to feed eventually in the VISSIM model and for generating the base year trip matrices. However these calibrated models have also proved most useful in developing quick option tests with a focus on strategic routing and link capacities rather than the detailed traffic operations that can be simulated in VISSIM.



5 Calibration and Validation of Base Year VISSIM Models

The AM and PM peak base year models were calibrated and validated on link flows as per DMRB model validation criteria for simulation models. The observed traffic volumes were taken from multiple sources including ATC counts, turning counts, link speed counts, video surveys and car park counts from the years 2003 till 2006. In addition, the models were calibrated against journey times observed on 5 different routes spread across the West Bromwich town centre and the Expressway. The models were also checked for consistency through visual validation of queues against average observed queues on a number of traffic links in West Bromwich. The following sections describe the key validation results from the AM and PM peak base year models. Further model results from the base year models are presented in Appendix B.

5.1 VISSIM Model - AM Peak

5.1.1 Traffic Flows - AM Peak

The AM Peak VISSIM models were calibrated to 48 link flow counts (2006) for Cars, HGVs and LGVs separately.. Following the calibration process, the base year model was validated on 58 link flow counts carried out between the years 2003 and 2005. Tables 5.1 and 5.2 show a summary of the calibration and validation (percentage of counts within GEH 5) of link flow counts for the base year models. In the AM peak models, 89% of the calibration counts were modelled within GEH 5 limits including all vehicle types. In addition, 96% of validation counts were modelled with GEH 5 limit for cars. Detailed link flow comparison between the observed and modelled link flow counts for calibration and validation is presented in Appendix B.1

Table 5.1: AM Peak Base Year VISSIM Model Link Flow Calibration results

Table 5.2: AM Peak Base Year VISSIM Model Link Flow Validation results

Cars 96%HGV 84%LGV 89%All Vehicles 89%

AM Peak Validation Results


AM Peak Calibration Results



5.1.2 Modelled Journey Time on Routes - AM Peak

The AM peak base year VISSIM models were validated for journey times observed on 5 key routes through the town centre. The journey time observations including the location of observed routes are given in Appendix A.4. Figures 5.1 to 5.9 show the comparison of AM peak models results against the observed journey time. The model results show that the overall journey time on all the routes is within 1 minute of the observed time on most of the routes. Details of journey time validation are given in Appendix B2.2 which shows a close statistical match between the observed and modelled journey time data.

Figure 5.1: Journey time comparison for Route 1 in AM Peak Period

Journey time on Route 1 - AM Peak hour Observations

0

2

4

6

8

10

12

14

16

0 500 1000 1500 2000 2500Distance (m)

Tim

e (m

in)

Obs 10

Obs 11

Obs 12

Obs 13

Obs 14

Obs 15

Obs 16

Obs 17

Peak AvgObsModelled



Figure 5.2: Journey time comparison for Route 2 NB in AM Peak Period

Journey time on Route 2 NB - AM Peak hour Observations

0

2

4

6

8

10

12

14

16

0 1000 2000 3000 4000 5000 6000Distance (m)

Tim

e (m

in)

Obs 6

Obs 7

Obs 8

Obs 9

Obs 10

Obs 11

Peak AvgObs

Modelled

Figure 5.3: Journey time comparison for Route 2 SB in AM Peak Period

Journey time on Route 2 SB - AM Peak hour Observations

0

2

4

6

8

10

12

14

16

0 1000 2000 3000 4000 5000 6000Distance (m)

Tim

e (m

in)

Obs 7

Obs 8

Obs 9

Obs 10

Obs 11

Peak AvgObsModelled





0

2

4

6

8

10

12

14

16

0 1000 2000 3000 4000 5000 6000 7000Distance (m)

Tim

e (m

in)

Obs 7

Obs 8

Obs 9

Obs 10

Obs 11

Peak AvgObsModelled



0

2

4

6

8

10

12

14

16

0 1000 2000 3000 4000 5000 6000 7000

Distance (m)

Tim

e (m

in)

Obs 7

Obs 8

Obs 9

Obs 10

Obs 11

Peak AvgObsModelled





0

2

4

6

8

10

12

14

16

0 1000 2000 3000 4000 5000 6000

Distance (m)

Tim

e (m

in)

Obs 4

Obs 5

Obs 6

Peak AvgObsModelled



0

2

4

6

8

10

12

14

16

0 1000 2000 3000 4000 5000 6000 7000

Distance (m)

Tim

e (m

in)

Obs 4

Obs 5

Obs 6

Peak AvgObsModelled





0

2

4

6

8

10

12

14

16

0 500 1000 1500 2000 2500 3000 3500 4000 4500

Distance (m)

Tim

e (m

in)

Obs 6

Obs 7

Obs 8

Obs 9

Peak AvgObsModelled



0

2

4

6

8

10

12

14

16

0 500 1000 1500 2000 2500 3000 3500 4000 4500

Distance (m)

Tim

e (m

in)

Obs 6

Obs 7

Obs 8

Obs 9

Peak AvgObsModelled



5.1.3 Overall Network Performance - AM Peak

The overall network performance results for the AM peak base year models are shown below. The table below shows the average speed, delay and number of vehicles entering the network including the pedestrians, public transport vehicles and M5 motorway through trips.

Table 5.3: Average Network performance of all vehicles in AM peak base year models

Modelled ParameterAverage of 5

Random Seed Average speed [mph], All Vehicle Types 18.4 Average delay time per vehicle [s], All Vehicle Types 149.4 Total Path Distance [km], All Vehicle Types 133,835.2 Total travel time [h], All Vehicle Types 4,519.1 Total delay time [h], All Vehicle Types 1,959.9 Average number of stops per vehicles, All Vehicle Types 4.5 Number of Vehicles entering the Network 47,212.2 Number of vehicles that have left the network, All Vehicle Types 42,202.0 Number of vehicles in the network, All Vehicle Types 5,010.2

5.2 VISSIM Model - PM Peak

5.2.1 Traffic Flows - PM Peak

The PM Peak VISSIM models were calibrated to 52 link flow counts (2006) for Cars, HGVs and LGVs. Following the calibration process, the base year model was validated on 56 link flow counts carried out between the years 2003 and 2005. Tables 5.4 and 5.5 show a summary of the calibration and validation (percentage of counts within GEH 5) of link flow counts for the base year models. In the PM peak models, 96% of the calibration counts were modelled within GEH 5 limits including all vehicle types. In addition, 85% of validation counts were modelled with GEH 5 limit for cars. A detailed link flow comparison between the observed and modelled link flow counts for calibration and validation is presented in Appendix 3.

Table 5.4: PM Peak Base Year VISSIM Model Link Flow Calibration results


PM Peak Calibration Results



Table 5.5: PM Peak Base Year VISSIM Model Link Flow Validation results

5.2.2 Modelled Journey Time on Routes - PM Peak

The PM peak base year VISSIM models were validated for journey times observed on 5 key routes through the town centre. The journey time observations including the location of observed routes are given in Appendix A.4. Figures 5.10 to 5.18 show the comparison of PM peak model results against the observed journey times. The model results show that the overall journey time on 7 out of 9 routes is within 1 minute of the observed time. Details of journey time validation are given in Appendix B3.2 which shows a close statistical match between the observed and modelled journey time data.

Figure 5.10: Journey time comparison for Route 1 in PM Peak Period

Journey time on Route 1 - PM Peak hour Observations

02468

10121416182022242628

0 500 1000 1500 2000 2500

Distance (m)

Tim

e (m

in)

Obs 4

Obs 5

Obs 6

Obs 7

Obs 8

Peak AvgObs

Modelled


PM Peak Validation Results



Figure 5.11: Journey time comparison for Route 2 NB in PM Peak Period

Journey time on Route 2 NB - PM Peak hour Observations

02468

10121416182022242628

0 1000 2000 3000 4000 5000 6000

Distance (m)

Tim

e (m

in)

Obs 3

Obs 4

Obs 5

Obs 6

Obs 7

Peak AvgObs

Modelled

Figure 5.12: Journey time comparison for Route 2 SB in PM Peak Period

Journey time on Route 2 SB - PM Peak hour Observations

02468

10121416182022242628

0 1000 2000 3000 4000 5000 6000

Distance (m)

Tim

e (m

in)

Obs 3

Obs 4

Obs 5

Obs 6

Peak AvgObs

Modelled




Journey time on Route 3 NB - PM Peak hour Observations)

02468

10121416182022242628

0 1000 2000 3000 4000 5000 6000 7000

Distance (m)

Tim

e (m

in)

Obs 1

Obs 2

Obs 3

Obs 4

Obs 5

Obs 6

Obs 7Obs 8

Obs 9

Obs 10

Obs 11

Obs 12

Avg Obs

Modelled



02468

10121416182022242628

0 1000 2000 3000 4000 5000 6000 7000

Distance (m)

Tim

e (m

in)

Obs 3

Obs 4

Obs 5

Obs 6

Peak AvgObsModelled





02468

10121416182022242628

0 1000 2000 3000 4000 5000 6000

Distance (m)

Tim

e (m

in)

Obs 1

Obs 2

Obs 3

Obs 4

Obs 5

Obs 6

Obs 7

Obs 8

Obs 9

Obs 10

Avg Obs

Modelled



02468

10121416182022242628

0 1000 2000 3000 4000 5000 6000 7000

Distance (m)

Tim

e (m

in)

Obs 3

Obs 4

Obs 5

Peak AvgObs

Modelled





02468

10121416182022242628

0 500 1000 1500 2000 2500 3000 3500 4000 4500

Distance (m)

Tim

e (m

in)

Obs 3

Obs 4

Obs 5

Obs 6

Peak AvgObs

Modelled



02468

10121416182022242628

0 500 1000 1500 2000 2500 3000 3500 4000 4500

Distance (m)

Tim

e (m

in)

Obs 3

Obs 4

Obs 5

Obs 6

Obs 7

Peak AvgObs

Modelled

5.2.3 Overall Network Performance - PM Peak

The overall network performance results for the PM peak base year models are shown below. The table below shows the average speed, delay and number of vehicles entering the network including the pedestrians, public transport vehicles and M5 motorway through trips.



Table 5.6: Average Network performance of all vehicles in PM peak base year models

Modelled ParameterAverage of 5

Random Seed Average speed [mph], All Vehicle Types 20.2 Average delay time per vehicle [s], All Vehicle Types 122.6 Total Path Distance [km], All Vehicle Types 139,516.2 Total travel time [h], All Vehicle Types 4,284.8 Total delay time [h], All Vehicle Types 1,624.8 Average number of stops per vehicles, All Vehicle Types 4.1 Number of Vehicles entering the Network 47,701.8 Number of vehicles that have left the network, All Vehicle Types 42,800.0 Number of vehicles in the network, All Vehicle Types 4,901.8

5.3 Overall Network comparison between AM and PM peak base year models

Figure 5.19 below shows the overall comparison between the AM and PM peak base year VISSIM models. The modelling results suggest that the morning peak period faces higher congestion and delay for the vehicles, especially those entering the town centre.

Figure 5.19: Overall Comparison of AM and PM peak base Year VISSIM Models

Average Speed of vehicles in the Peak hour

18.420.2

0

5

10

15

20

25

AM Peak PM Peak

Aver

age

Spee

d (m

ph)

Base

Average Journey Delay per vehicle in the Peak Hour

149.4122.6

0

50

100

150

200

AM Peak PM Peak Ave

rage

Jou

rney

Del

ay (s

econ

ds)

Base

Total Travel Time in the Peak Hour

4519.1

4284.8

4100

4200

4300

4400

4500

4600

AM Peak PM Peak

Tota

l Tra

vel T

ime

(h)

Base

Total Delay Time in the Peak Hour

1959.91624.8

0

500

1000

1500

2000

2500

AM Peak PM Peak

Tota

l Del

ay T

ime

(h)

Base



6 Conclusions

The AM and PM peak VISSIM models for West Bromwich town centre were calibrated and validated to DMRB criteria. The models were also calibrated against the observed journey times on 5 different routes spread across the West Bromwich town centre and the Expressway. The models were also checked for consistency through visual validation of modelled queues against average observed queues on various traffic links in West Bromwich. As a consequence, the base year models are considered representative of the existing traffic situation in and around the West Bromwich town centre and are considered fit for testing various land use development scenarios and associated network changes.

The VISUM strategic interface is found to be a very useful tool as it can be used to develop strategic option tests at relatively lower costs as the assignment results can be checked and reviewed quickly. This has helped SMBC to obtain a broader perspective of the evolving traffic situation while using the VISSIM model for detailed junction analysis and operation performance evaluations.

Documents

West Bromwich Microsimulation Model Validation Report › download › downloads › id › 3255 › west... · West Bromwich Microsimulation Mott MacDonald Model Validation Report