| Project/Area Number |
07650619
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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 Faculty of Engineering, Gifu University Assisitant Professor, 工学部, 助教授 (70159341)
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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 |
¥2,200,000 (Direct Cost: ¥2,200,000)
Fiscal Year 1996: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 1995: ¥1,300,000 (Direct Cost: ¥1,300,000)
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| Keywords | Fuzzy Reasonig / Route Choice Behavior / Neural Networks / Trafic Information Service / Urban Road Networks / Fuzzy Neural Networks / Genetic Algorithm / Path Flow / 三角型ファジィ数 / 交通情報 / 多段推論 / 簡略ファジィ推論 / 交通行動分析 |
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| Research Abstract |
The route choice model with fuzzy reasoning is considered to be developed and applied to the traffic information service. The main research results are summarized as follows in the order of the research procedure :
1) The primitive survey to describe the route choice phenomena on the urban road network is done. The route choice model with fuzzy reasoning is provided using the data. The factors of route choice associated with traffic information are gathered and the knowledge based rules for the route choice are created with the linguistic expressions. The primitive model is established with referring to the related research results about T-norms and defuzzification.
2) The fuzzy reasoning model is practically modified. The simplified model is introduced to promote the efficiency of calculation in forecasting the path flows. And also the multi-stage fuzzy reasoning is introduced to describe sequential decision steps. The proper type of fuzzy reasoning model (FL model) is proposed with the
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se improvements and the impacts of traffic information to the flows are considered.
3) The estimation of the membership functions is discussed as a disadvantage of the FL model. The genetic algorithm may be used to estimate the parameters because the FL model involves non-linear calculating process. The parameters are obtained with considering the errors between real values and estimated values.
4) The model with fuzzy reasoning for route choice description is established with linguistic expressions. However, the crisp value estimation is recommended for the practical estimation of the path flows. The neural network model with error back propagation is introduced to modify at this point. The fuzzy reasoning model with neural network in conclusions of the rules are proposed as a hybrid model.
5) The several similar techniques are summarized as "soft computing" and their advantages to construct the travel behavior models are discussed. Furthermore, the effective use of the models to traffic information management is mentioned. The transforming the traffic information and information service are analyzed precisely. The result shows the proper way of information provision on urban transport networks.
6) The research results are summarized and reported in the several conferences and the further discussions are recommended. The some remarks are also planned to be reported the international and domestic conferences related with civil engineering. Less
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