A Model Analysis of Travel Behavior Changes by Advanced Traveler Information Systems and Their Implications to Transportation Planning

本研究从微观层面即个人出行行为和宏观层面即都市圈交通网络两个方面考察了出行信息提供的影响。首先，我们发展了一种方法来定量评估出行者出行前信息的影响，考虑交易成本的出行行为变化。交易成本定义为出行行为改变前后所观察到的期望效用之差，并使用离散选择模型进行估计，该模型分别代表有无出行时间信息的出行方式选择行为。然后，我们开发了一个离散的选择模型，采用非补偿性决策规则和属性截止明确考虑个人的信息处理能力。将该模型应用于在途出行者信息下的出行方式选择行为，实证结果表明，该模型具有上级的优越性 关于我们 其次，我们利用均衡分配模型研究了先进的出行者信息系统对中京都市圈交通网络需求的影响。提出了两种不同的方法来放松驾驶员完全信息的假设。从实际路径需求不是在行程时间水平上而是在效用水平上均衡的观点出发，我们首先开发了一种方法来修正路段性能函数，以便将行程时间和费用之外的许多不可观测因素纳入其中。该方法的实证应用和拟合优度，特别是在非高峰期，显着增加。在此基础上，提出了一种考虑网络感知异质性的均衡分配方法。根据驾驶员对网络配置和出行时间的认知程度，将其分为不同类别，并采用不同的分配原则进行分配。应用该模型改变各类别的构成比例，结果表明，当约20%的驾驶员在不完全信息下选择路径时，模型的拟合优度最高。最后，我们估计了先进的旅行者信息系统，提供最短路径信息的司机的用户效益。少

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 … More 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