Assessment of Urban Transportation Networks by integrating Transportation Planning and Operational

Methods /Tools

Presentation by:

Sabbir Saiyed, P.Eng.

Program Manager, York Region

& Dr. J. A. Stewart

Dean, Engineering, RMC

12th TRB Transportation Planning Application Conference Houston, Texas

Overview Introduction

Study area

Regional travel demand forecasting model

Transportation networks and data

Types of signal control

Experimental methodology

Experimental results and discussions

Conclusions

Introduction

Transportation – vital service

Traffic congestion

Budget constraints

Performance of traffic systems

Integration – planning/operational analysis

Use of micro-simulation

Study area is part of Greater Toronto Area….

Structure of Regional Model

Trip Generation External Trips Airport Trips

Trip Distribution Apply Growth Factors Apply Growth Factors

Modal Split

Auto Occupancy

Trip Assignment

Transportation Network and Data

Transportation network - auto and transit

Transportation data source:

Cordon count

Turning movement counts

O-D survey

Data issues especially for micro-simulation

Transportation Tomorrow Survey (TTS)

Largest O-D survey in Canada

Partnership between 21 municipalities, transit agencies and Province

Collected every 5-year

Household trip data

Geocoding

Correlation with census data

Types of Signal Control

There are three types of signal control Pre-timed Actuated Adaptive

Performance of traffic signal Cycle length Offsets Phases

Transportation network analysis has been conducted for three types of signal controls

Less traffic on side street yet Main Street traffic facing red signal

Transportation Software Packages

Emme/2 TRANSCAD Synchro Sim-Traffic INTEGRATION

Data Tsunami

INTEGRATION Model

Developed by late Dr. M. Van Aerde

Mesoscopic model

Dynamic traffic assignment

Vehicle probe

Uses O-D data

INTEGRATION model has been used to asses performance of traffic adaptive control

Experimental Design

Real transportation network

Experiment conducted in stages Small network (9 intersections) Medium network (over 125 intersections) Downtown network (medium size city)

Current focus on medium network

Arterial Network

Large arterial network

Over 125 intersections

Several unsignalized intersections

Congested conditions during peak periods

Experimental Methodology

Development of auto and transit network Extraction of O-D matrix from TTS Survey O-D matrix estimation Trip assignments Data validation Generation of turning movements Data files for micro-simulation

Experimental Methodology

Process

Regional Model

Develop sub-area model

Develop transportation

network

Conduct trip assignment

Develop turning

movements

Compare turning

movements

Turning movements

Validated

Retrieve and use turning movements

Intersection turning

movement count data

Cordon count data

Forecast Origin-

Destination Matrix

Origin Destination Survey (TTS

Survey)

Develop O-D matrix

Develop sub-area O-D

matrix

Develop Integration

model

Develop Synchro and

Sim-traffic model

Traffic Adaptive Control

Pre-timed Control

Actuated Control

Simulation

MoE Report

Yes

No

Optimization improves signal delays

Total Signal Delay - Unsaturated Condition

02000400060008000

100001200014000160001800020000

BeforeOptimization

CycleOptimization

OffsetsOptimization

Cycle/OffsetsOptimization

To

tal S

ign

al D

elay

(Ho

urs

)

Pre-timed

Actuated

Adaptive

Total number of stops are lower for traffic adaptive signal control

Total Stops - Unsaturated Condition

0

50000

100000

150000

200000

250000

300000

350000

BeforeOptimization

CycleOptimization

OffsetsOptimization

Cycle/OffsetsOptimization

To

tal S

top

s

Pre-timed

Actuated

Adaptive

Shorter cycle lengths produces better results

Pre-timed Signal

0

50000

100000

150000

200000

250000

300000

50 60 70 80 90 100 110 120 130 140 150

Cycle Lengths (Sec)

To

tal

Sto

ps

76

78

80

82

84

86

88

90

92

94

96

Sig

nal

Del

ay (

Ho

urs

)

Total stops

Fuel

Total delay

Actuated signal shows similar results; however delays are lower than pre-timed signal

Actuated Signal

0

50000

100000

150000

200000

250000

300000

50 60 70 80 90 100 110 120 130 140 150

Cycle Lengths (Sec)

To

tal

Sto

ps

71

76

81

86

91

96

Sig

nal

Del

ay (

Ho

urs

)

Total stops

Fuel

Total delay

Signal delays are lower at lower mid-block traffic

Pre-timed Signal

0

50000

100000

150000

200000

250000

300000

0% 10% 20% 30% 40% 50%

Mid-block Traffic

Tota

l Sto

ps

11200

11400

11600

11800

12000

12200

Sign

al D

elay

(H

ours

)

Total stops

Fuel

Total delay

Actuated signal shows similar results; however delays are lower than pre-timed signal

Actuated Signal

0

50000

100000

150000

200000

250000

300000

0% 10% 20% 30% 40% 50%

Mid-block Traffic

Tota

l Sto

ps

9100

9600

10100

10600

11100

11600

12100

Sign

al D

elay

(H

ours

)

Total stops

Fuel

Total delay

Conclusions

The experiment demonstrates that Synchro, Sim-Traffic and INTEGRATION could be used to analyze three types of traffic signal controls

Optimization improves the performance of the arterial network

Shorter cycle lengths produces better results compared to longer cycle lengths.

Actuated signal addresses demands of mid-block traffic better than pre-timed signal

Thank you