| Co-Investigator(Kenkyū-buntansha) |
KURAUCHI Shinya Grad. School of Eng, Nagoya Univ., Research Assoc., 工学研究科, 助手 (90314038)
KAWAKAMI Shogo Grad. School of Eng, Nagoya Univ., Professor, 工学研究科, 教授 (60023058)
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| Research Abstract |
This research investigated the impact of travel information provision on micro level, that is, individual's travel behavior, and macro level, transportation network of metropolitan area. Firstly, we developed a methodology to quantitatively evaluate the impact of traveler's pre-trip information considering transaction cost for travel behavior changes. The transaction cost was defined as the difference of expected utilities which are observed before and after travel behavior changes, and was estimated using a discrete choice model which represents mode choice behavior with and without the information on travel time respectively. We, then, developed a discrete choice model that incorporates non-compensatory decision making rules and attribute cutoff to explicitly consider the individual's information-processing capability. The model was empirically applied to the travel mode choice behavior under on-route traveler's information, and the result showed that the proposed model was superior
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in both reproducibility and prediction to the existing model in which an utility maximizing decision maker is assumed.
Next, we investigated the impact of advanced traveler information system on transportation network demand of Chukyo metropolitan area using equilibrium assignment model. Two different approaches were proposed to relax the assumption of driver's perfect information. From the viewpoint that the actual route demand is equilibrate not in travel time level but in utility level, we first developed a methodology to amend link performance function in order to incorporate many unobservable factors other than travel time and cost. This method was empirically applied and the goodness-of-fit, especially in off-peak period, was dramatically increased. We, then, developed an equilibrium assignment method considering heterogeneity of network perception. More specifically, drivers were divided into different categories according to his/her cognitive level on the network configuration and travel time, and were assigned using different principle. The proposed model was applied changing the composition ratio of each categories, and the result showed that the goodness-of fit was the highest in the situation where about 20% of drivers chose his/her route with imperfect information. Finally, we estimated the user benefit of the advanced traveler information system that provides the shortest path information to the drivers. Less
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