1999 Fiscal Year Final Research Report Summary
Travel Behaviour Analysis Model with Soft-computing Techniques
| Project/Area Number |
10650524
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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, Faculty of Engineering, Professor, 工学部, 教授 (70159341)
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| Co-Investigator(Kenkyū-buntansha) |
OGAWA Keiichi Gifu University, Faculty of Engineering, Assistant Professor, 工学部, 講師 (50303508)
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| Project Period (FY) |
1998 – 1999
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| Keywords | soft-computing / travel behaviour model / fuzzy reasoning / neural network / travel pattern / activity / fuzzy neural network / fuzzy logit model |
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| Research Abstract |
The travel behaviour analysis model is proposed to describe the multi-stage decision of trip maker in the research. The model is applied to evaluate the transport policy. Firstly, the travel pattern descriptive model is proposed on the basis of activity distribution to demonstrate the key structure of the travel behaviour model. Secondly, the extension of the model description is mentioned. The application to the evaluation of transport policy is discussed as well. The research results are summarized as follows:
1. The applicability of soft-computing technique is mentioned to investigate the knowledge based modelling for modal choice and route choice with fuzzy reasoning as well as neural network (NN). The descriptive models with uncertain events in human decision for travel behaviour are summarized.
2. The six activity based models in the stages of travel are constructed to describe the individual daily travel patterns. Particularly, fuzzy neural network (FN) model is mainly applied to create the model with the combination between fuzzy reasoning and neural network.
3. The hybrid model is proposed to combine the stochastic and soft-computing approaches complementally. The fuzzy logit model (FLM) is constructed with combining between fuzzy reasoning and logit model. The travel behaviour can be easily described with randomness as well as fuzziness.
4. The evaluation of transport policy is mentioned to consider the applicability of the proposed model with person trip survey data. It would be confirmed that the model could describe the travel patterns corresponding to the change of transport environments
5. The research results in the above stages have been reported in international and domestic conferences as JSCE and so on. The further study topics are summarized as well.
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