2003 Fiscal Year Final Research Report Summary
Application of Travel Behavior Model with Intelligent Information Processing Application of Travel Behavior Model with Intelligent Information Processing
| Project/Area Number |
14550528
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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 | Gifu University |
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Principal Investigator |
AKIYAMA Takamasa Gifu University, Dept.of Civil Engineering, Professor, 工学部, 教授 (70159341)
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| Co-Investigator(Kenkyū-buntansha) |
OKUSHIMA Masashi Gifu University, Dept.of Civil Engineering, Research Associate, 工学部, 助手 (20345797)
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| Project Period (FY) |
2002 – 2003
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| Keywords | Travel Behavior Analysis / Fuzzy Reasoning / Soft-Computing / Time Space Constraints / Transportation Policy / Congestion Pricing / Artificial Intelligence / Machine Learning |
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| Research Abstract |
The travel behavior model with intelligent information processing is constructed. The model is calibrated for evaluation of the urban transportation policy. In particular, the decision process of trip makers is described explicitly in detail. The travel behavior model can be applied for the impact analysis of the urban transportation policy.
The elemental technologies of intelligent information processing are integrated to the decision process of trip makers. The sequential estimation of travel behavior has been formulated based on fuzzy logic as a soft-computing technique. Since the essential components of the estimation model should be mentioned, the advantages of fuzzy logic modeling can be summarized.
The advantages of the decision tree algorithms can be summarized through the analysis of modal choice for commuters in urban area with empirical data. The artificial life approach make clear the existence of self-organized phenomena with efficiency according to the individual decision results even if social optimization techniques cannot be introduced as transport policies.
The evaluation of road pricing policy can be realized with the particular assumption for urban area with established fuzzy travel behavior model. The options of trip maker to the transport policy are classified quite obviously. The essential changes of travel patterns are summarized with example cases through the estimation of fuzzy travel behavior model. It would be known from the observation that several alternative types of travel pattern might be realized in road pricing installation on the urban network.
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