Development of ITS Evaluation Test-Bed Using
Microscopic Simulation – City
of Hampton Case Study
By:
Ilsoo Yun
Byungkyu “Brian”
Park
Development of ITS Evaluation Test-Bed Using
Microscopic Simulation – City
of Hampton Case Study
By:
Ilsoo Yun
Byungkyu “Brian”
Park
A Research Project Report
For the Center for ITS Implementation
Research (ITS)
A U.S. DOT University Transportation
Center
Ilsoo Yun
Email: iy6m@virginia.edu
Dr. Byungkyu “Brain” 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. |
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VACTS-15-0-45 |
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4. Title and Subtitle |
5. Report Date |
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Development
of ITS Evaluation Test-Bed Using
Microscopic Simulation - City of
Hampton Case Study |
August, 2003 |
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6. Performing Organization
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7. Author(s) Ilsoo Yun and Byungkyu
“Brian” Park |
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9. Performing Organization
and Address |
10. Work Unit No. (TRAIS) |
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Center for Transportation
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University of Virginia |
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PO Box 400742 Charlottesville, VA
22904-7472 |
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12. Sponsoring Agencies'
Name and Address |
13. Type of Report and
Period Covered |
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Office of University
Programs, Research and Special Programs Administration US Department of
Transportation 400 Seventh Street, SW Washington DC 20590-0001 |
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Final Report |
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15. Supplementary Notes |
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16. Abstract |
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Microscopic traffic
simulation models are very powerful tools as they provide inexpensive, fast,
and risk-free evaluation environment. They not only provide the simulation of
scenarios that cannot be practically tested in real world conditions, but
also allow various network wide performance measures including travel times,
delay and emissions. In addition, traffic simulation models are also being
used extensively in Intelligent Transportation Systems (ITS) researches.
However, there is a need to develop appropriate methodology and gain
experience in practical usage of traffic simulation models to evaluate ITS
deployments. In this study, the
procedure relevant to building a microscopic traffic simulation-based
test-bed for ITS applications is presented. This thesis describes the
entire process for building a microscopic traffic simulation-based test-bed
for ITS using a case study. The process consists of building a basic traffic
simulation network, the API development for coordinated actuated signal
control and, the dynamic O-D matrix estimation for the network. In order to
estimate the dynamic O-D matrix, an approach using GA and QUEENSOD method
coupled with traffic simulation model is introduced. Some findings during
making the simulation test-bed are also presented. The findings are very
useful for developing a simulation-based test-bed because the lessons learned
from this study can reduce trial-and-errors and efforts needed. |
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17 Key Words |
18. Distribution Statement |
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Intelligent Transportation
Systems (ITS), evaluation, microscopic traffic simulation |
No restrictions. This
document is available to the public. |
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Microscopic traffic simulation models are very powerful tools as they provide inexpensive, fast, and risk-free evaluation environment. They not only provide the simulation of scenarios that cannot be practically tested in real world conditions, but also allow various network wide performance measures including travel times, delay and emissions. In addition, traffic simulation models are also being used extensively in Intelligent Transportation Systems (ITS) researches. However, there is a need to develop appropriate methodology and gain experience in practical usage of traffic simulation models to evaluate ITS deployments. In this study, the procedure relevant to building a microscopic traffic simulation-based test-bed for ITS applications is presented.
This thesis describes the entire process for building a microscopic traffic simulation-based test-bed for ITS using a case study. The process consists of building a basic traffic simulation network, the API development for coordinated actuated signal control and, the dynamic O-D matrix estimation for the network. In order to estimate the dynamic O-D matrix, an approach using GA and traffic simulation model is introduced in Chapter 5. Some findings during making the simulation test-bed are presented in Chapter 3, 4 and 5. The findings are very useful for developing a simulation-based test-bed because the lessons learned from this study can reduce trial-and-errors and efforts needed.
1 Introduction 1
1.1
BACKGROUND 1
1.2 PROBLEM STATEMENT
2
1.3
RESEARCH OBJECTIVES AND METHODOLOGY 4
1.4
SCOPE OF STUDY 5
1.5
ORGANIZATION OF THIS THESIS 5
2 Literature Review 6
2.1
INTRODUCTION 6
2.2
DYNAMIC O-D MATRIX ESTIMATION2.2.1 STATIC O-D 6
ESTIMATION
2.2.1 Static O-D
Estimation 6
2.2.2 Dynamic O-D Estimation 8
2.2.3 Estimation of
Temporal Route Choice for Dynamic O-D 16
Estimation
2.3
GENETIC ALGORITHM 17
2.3.1 Introduction 18
2.3.2 Mechanisms of Genetic Algorithm
18
2.3.3 Genetic Algorithms
in O-D Estimation 24
2.4 TRAFFIC SIMULATION MODEL
IN ITS EVALUATION 27
2.5 SUMMARY 28
3 a development of large scale case study 29
simulation network
3.1 INTRODUCTION 29
3.2
MICROSCOPIC SIMULATION MODEL SELECTION 30
3.3
MICROSCOPIC SIMULATION MODEL, PARAMICS 33
3.3.1 User Interface 33
3.3.2 Network Structure 33
3.3.3 Traffic Generation and Assignment
33
3.3.4 Signal Control in PARAMICS
34
3.3.5 API in PARAMICS 34
3.3.6 Model Weaknesses 35
3.4 PARAMICS NETWORK 35
3.4.1 Network Building 35
3.4.2 Signal Control Logics 39
3.4.3 Parameter Setting 39
3.4.4 Simulation 39
3.4.5 Visualization 40
3.5 DEVELOPMENT OF AN API FOR
COORDINATED 42
ACTUATED
SIGNALS
3.5.1 Geometry of
Intersection and Detectors 42
3.5.2 Yellow Change Interval
44
3.5.3 New Phase Sequence for API
45
3.5.4 Input Data Structure 47
3.5.5 Control Logic 49
3.5.6 Fully Actuated Signal Control and T-intersection 50
3.6 SUMMARY 51
4 BUILDING Dynamic O-D Estimation Models