Final report of ITS Center project:
Optimization of coordinated actuated signal timing
UVA Center for Transportation Studies
A Research Project Report
For the Center for ITS Implementation Research
A U.S. DOT University Transportation Center
Stochastic Optimization Method for Coordinated Actuated Signal
Systems
Principal Investigators:
Byungkyu (Brian) Park
Ilsoo Yun
Center for Transportation Studies
University of Virginia
Thornton Hall
351 McCormick Road, P.O. Box 400742
Charlottesville, VA 22904-4742
804.924.6362
January 2006
Research Report No. UVACTS-15-0-102
Disclaimer
The contents of this report
reflect the views of the authors, who are responsible for the facts and the
accuracy of the information presented herein.
This document is disseminated under the sponsorship of the Department of
Transportation, University Transportation Centers Program, in the interest of
information exchange. The U.S.
Government assumes
Stochastic Optimization
Method for Coordinated Actuated Signal Systems
By:
Ilsoo Yun
Byungkyu (Brian) Park
A Research
Project Report
For the
Center for ITS Implementation Research
A U.S. DOT
University Transportation Center
Ilsoo Yun
Department of
Civil Engineering
Email:
iy6m@virginia.edu
Dr. Byungkyu (Brian) Park
Department of
Civil Engineering
Email:
bpark@virginia.edu
Center for
Transportation Studies at the University of Virginia produces outstanding
transportation professionals, innovative research results and provides
important public service. The Center
for Transportation Studies is committed to academic excellence,
multi-disciplinary research and to developing state-of-the-art facilities. Through a partnership with the Virginia
Department of Transportation’s (VDOT) Research Council (VTRC), CTS faculty hold
joint appointments, VTRC research scientists teach specialized courses, and
graduate student work is supported through a Graduate Research Assistantship
Program. CTS receives substantial
financial support from two federal University Transportation Center Grants: the
Mid-Atlantic Universities Transportation Center (MAUTC), and through the
National ITS Implementation Research Center (ITS Center).
Other related research activities of the faculty include funding through
FHWA, NSF, US Department of Transportation, VDOT, other governmental agencies
and private companies.
Disclaimer:
The contents
of this report reflect the views of the authors, who are responsible for the
facts and the accuracy of the information presented herein. This document is disseminated under the
sponsorship of the Department of Transportation, University Transportation
Centers Program, in the interest of information exchange.
The U.S. Government assumes no liability for the contents or use
thereof.
1. Report No.
|
2. Government Accession No.
|
3. Recipient’s Catalog No.
|
|
UVACTS-15-0-102
|
|
|
|
4. Title and Subtitle
|
5. Report Date
|
|
Stochastic Optimization Method for Coordinated Actuated Signal
Systems
|
December 2005
|
|
|
6. Performing Organization Code
|
|
|
|
|
7. Author(s)
Ilsoo Yun and Byungkyu (Brian) Park
|
8. Performing Organization Report No.
|
|
|
|
|
9. Performing Organization and Address
|
10. Work Unit No. (TRAIS)
|
|
Center for Transportation Studies
|
|
|
University of Virginia
|
11. Contract or Grant No.
|
|
PO Box 400742
Charlottesville, VA 22904-7472
|
|
|
12. Sponsoring Agencies' Name and Address
|
13. Type of Report and Period Covered
|
|
Office of University Programs, Research and Special Programs
Administration
US Department of Transportation
400 Seventh Street, SW
Washington DC 20590-0001
|
|
Final Report
|
|
|
|
14. Sponsoring Agency Code
|
|
|
|
|
|
15. Supplementary Notes
|
|
|
|
16. Abstract
|
|
This report presents a stochastic traffic signal optimization method
that consists of a heuristic simulation model and the microscopic simulation
model CORSIM. For the heuristic
optimization method, three heuristic methods including a genetic algorithm
(GA), simulated annealing (SA) and OptQuest Engine were investigated and
finally the GA was selected. The main
feature of the GA-based stochastic signal control settings optimization
method is the ability to optimize not only Group 1 settings (i.e., cycle
length, green splits, offsets, and phase sequences) but also Group 2 (i.e.,
controller and detector-related settings) and Group 3 settings (i.e.,
volume-density control related
settings) in the microscopic simulation environment represented by
CORSIM. The performance of the
proposed stochastic optimization method was compared with existing signal
timing optimization programs including TRANSTY-7F and SYNCHRO under a
microscopic simulation environment.
The results indicate that the proposed method outperformed existing
programs in the optimization of the basic four parameters, and also showed
that additional controller and detector-related settings can further improve
the operations of coordinated actuated signal control systems.
|
|
17 Key Words
|
18. Distribution Statement
|
|
Signal timing optimization, signal control settings, genetic
algorithm, simulated annealing, OptQuest Engine, coordinated actuated signal
system
|
No restrictions. This
document is available to the public.
|
|
|
|
|
|
Since Webster developed the principle of
traffic signal control optimization theory in late 1950, the field of traffic
signal timing control has advanced dramatically over the past few decades. This includes coordinated actuated control
and adaptive control on the basis of advances in the detection and
communication technologies. However,
existing traffic signal timing optimization programs still focuses on the basic
four parameters (i.e.,
cycle, green split, offset, and phase sequence). In addition, these optimization programs do not consider
stochastic variability in drivers’ behavior, vehicle entry headway, vehicle
mix, and so forth. Even though a few
research efforts focused on the use of stochastic simulation models, little was
done in the optimization of traffic signal controller settings (e.g., minimum
green time, vehicle extension time, minimum vs. maximum recalls) and detector
settings (e.g., location).
This report presents a stochastic traffic
signal optimization method that consists of a heuristic simulation model and
the microscopic simulation model CORSIM.
For the heuristic optimization method, three heuristic methods including
a genetic algorithm (GA), simulated annealing (SA) and OptQuest Engine were
investigated and finally the GA was selected.
The main feature of the GA-based stochastic signal control settings
optimization method is the ability to optimize not only Group 1 settings (i.e.,
cycle length, green splits, offsets, and
phase sequences) but also Group 2 (i.e., controller and detector-related
settings) and Group 3 settings (i.e.,
volume-density control related settings) in
the microscopic simulation environment represented by CORSIM. The performance of the proposed stochastic
optimization method was compared with existing signal timing optimization
programs including TRANSTY-7F and SYNCHRO under a microscopic simulation
environment. The results indicate that
the proposed method outperformed existing programs in the optimization of the
basic four parameters, and also showed that additional controller and
detector-related settings can further improve the operations of coordinated
actuated signal control systems.
ABSTRACT. iv
Table of Contents. vi
List of Figures. x
List of Tables. xii
CHAPTER 1 INTRODUCTION.. 1
1.1
Background. 1
1.2
Problem Statement 2
1.3
Goal and Objectives. 5
1.4
Research Scope. 6
1.5
Organization of the Report 6
CHAPTER 2 STATE OF THE ART. 8
2.1
Coordinated Actuated Signal Control 8
2.1.1
Concept of Coordinated Actuated Signal Control 8
2.1.2
Detectors in Traffic Signal Control System.. 10
2.2
Review on Signal Timing Optimization. 12
2.3 State of the Practice in Signal Timing
Optimization Program.. 18
2.3.1
TRANSYT-7F. 18
2.3.2
SYNCHRO.. 19
2.3.3
Features and Limitations. 20
2.4
Heuristic Optimization Methods. 21
2.4.1
Genetic Algorithm (GA) 23
2.4.2
Simulated Annealing (SA) 24
2.4.3
OptQuest Engine. 25
2.4.4
Heuristic Optimization Methods in Signal Control Settings Optimization. 26
2.5
Microscopic Traffic Simulation Models. 27
2.5.1
Microscopic Traffic Simulation Models. 27
2.5.2
Microscopic Traffic Simulation Models
in Signal Control Settings Optimization. 30
2.6
Summary. 34
CHAPTER 3 DEVELOPMENT OF STOCHASTIC SIGNAL CONTROL
SETTINGS
OPTIMIZATION
METHODS 36
3.1
Introduction. 36
3.2
Development of Heuristic Optimization Module. 38
3.2.1
Signal Control Settings to be Optimized. 39
3.2.2
Objective Functions. 40
3.2.3
Heuristic Optimization Methods. 42
3.3
Optimization-Simulation Interface Module. 55
3.3.1
Decoding of Signal Control Settings. 55
3.3.2
Decoding Scheme for Group 1 Settings. 57
3.3.3
Decoding Scheme for Group 2 Settings. 64
3.3.4
Decoding Scheme for Group 3 Settings. 66
3.4
Simulation Module. 69
3.4.1
MOE Calculation. 69
3.4.2
Calibration and Validation of CORSIM Field Networks. 71
3.4.3
Findings from the Use of CORSIM simulation model 76
3.5
Summary. 77
CHAPTER 4 CASE STUDY: SIMPLE NETWORKS. 79
4.1
Building Test Networks. 79
4.2
Optimization of Traffic Signal Control Settings. 82
4.2.1
Implementation of Stochastic Optimization Methods. 82
4.2.2
Optimization of Group 1 Settings. 83
4.2.3
Optimization of Group 2 and Group 3 Settings. 91
4.3
Summary. 98
CHAPTER 5 CASE STUDY: TWELVE INTERSECTIONS AT THE LEE
JACKSON MEMORIAL HIGHWAY IN NORTHERN VIRGINIA.. 100
5.1
Introduction. 100
5.2
Test Site Selection. 100
5.3
Data Collection. 102
5.4
Field Network Building. 105
5.5
Calibration and Validation. 108
5.5.1
Identification of Calibration Parameters. 108
5.5.2
Feasibility Test 110
5.5.3
GA-based Calibration. 115
5.5.4 Final Comparison. 119
5.5.5
Validation. 120
5.5.6
Discussions on Calibration and Validation. 121
5.6
Optimization of Signal Control Settings. 122
5.6.1
Introduction of Signal Control Settings Optimization. 123
5.6.2
Evaluation of Current Signal Control Settings. 124
5.6.3
Optimization of Group 1 Settings. 125
5.6.4
Effects of Calibration in Stochastic Optimization. 139
5.7
Summary. 141
CHAPTER 6 TWO INTERSECTIONS AT EMMET ST.
IN CHARLOTTESVILLE 144
6.1
Test Site Selection. 144
6.2
Data Collection. 147
6.3
Field Network Building. 153
6.4
Calibration and Validation. 160
6.4.1 Identification of Calibration Parameters. 160
6.4.2 Feasibility Test 164
6.4.3
Evaluation of Best Sample. 173
6.4.4
Validation. 174
6.5
Optimization of Signal Control Settings. 175
6.5.1
Evaluation of Current Signal Control Settings. 177
6.5.2
Optimization Using SYNCHRO.. 177
6.5.3
Optimization of Group 1 Settings Using GA-based Method. 180
6.5.4
Optimization of Group 2 Settings Using GA-based Method. 183
6.5.5
Optimization of Group 3 Settings Using GA-based Method. 187
6.6
Summary. 192
CHAPTER 7
CONCLUSIONS AND RECOMMENDATIONS. 194
7.1
Conclusions. 194
7.2
Expected Contributions. 197
7.3
Recommendations. 198
References. 202
Appendix A.
Glossary. 210
Appendix B.
Comparison of the Number of Replications. 212
Appendix C.
Sensitivity Analysis for GA Setting. 219
Appendix E.
Comparison among the Five Optimization Methods. 229
Figure 1.
Vehicle Trajectory on a Time-Space Diagram.. 10
