Estimation of a Time Dependent OD Matrix from Traffic Counts
| Project/Area Number |
07650613
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
交通工学・国土計画
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| Research Institution | Univ. of Tokyo |
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Principal Investigator |
KUWAHARA Masao Uiiv. of Tokyo Institute of Industrial Science, Associate Professor, 生産技術研究所, 助教授 (50183322)
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| Co-Investigator(Kenkyū-buntansha) |
YOSHII Toshio Univ. of Tokyo Institute of Industrial Science, Research Associate, 生産技術研究所, 助手 (90262120)
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| Project Period (FY) |
1995 – 1996
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| Project Status |
Completed (Fiscal Year 1996)
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| Budget Amount *help |
¥2,300,000 (Direct Cost: ¥2,300,000)
Fiscal Year 1996: ¥100,000 (Direct Cost: ¥100,000)
Fiscal Year 1995: ¥2,200,000 (Direct Cost: ¥2,200,000)
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| Keywords | OD flows / Network / Link flows / Route choice / OD推定 / 観測交通量 / 時空間ネットワーク |
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| Research Abstract |
We propose the model for estimating time-dependent OD matrices from traffic counts in a general network with route choice activities.
Many dynamic traffic simulation models have been developed in order to reproduce traffic conditions and evaluate policies of traffic control, signal control, one-way traffic control and so on. Such a dynamic model needs a time-dependent OD,especially one composed of small OD zones. However, it is hard to estimate OD flows directly from OD survey. In this study, we thus propose a dynamic estimator using time-dependent traffic counts to obtain time-dependent OD volumes.
The model consists of two parts : (1) construction of the relationship between the time-dependent OD volumes and traffic counts at links and (2) estimation of a unique time-dependent OD matrices. In the first part, we define a three-dimensional network to relate OD matrices to traffic flow on links. We then propose a method of estimating route choice probabilities. In the second part, we employ the Entropy Maximizing method for the staic OD matrices estimation and extend it to time-dependent model. As an extension, we propose a simplified method of estimating route choice probability and a method to utilize aggregated prior OD information.
At the last, we apply the model to a test network and examine its validity.
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Report
(3 results)
Research Products
(10 results)
