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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