Conventional dynamic discrete choice models have not satisfactorily represented individual heterogeneity due to the insufficient utilization of individual attributes and behavior data. This paper therefore attempts to establish a new dynamic travel mode choice model simultaneously incorporating cross-sectional and longitudinal heterogeneity by decomposing taste parameter of travel level-of-service over time. Throughout an empirical analysis sing revealed preference panel data, it is confirmed that the established model can flexibly describe choice behavior dynamics with a higher accuracy. By introducing the above-defined taste structure into dynamic GEV model, it is shown that the closing timing of panel survey affects the model estimation results as well as initial condition of panel survey.

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Conventional factor analysis is difficult to satisfactorily represent fuzziness in subjective evaluation data. This paper therefore attempts to tackle this issue by applying a factor analysis based on fuzzy theory. Moreover, a new discrete choice model (called hybrid discrete choice model) is proposed by incorporating fuzzy latent factors calculated from the above-mentioned factor analysis. Reflecting the influence of fuzziness, utility functions of hybrid discrete choice model are defined as those having interval values. An empirical study, using stated preference data for choice of travel mode under information provision, confirmed the effectiveness of factor analysis based on fuzzy theory and the hybrid discrete choice model.

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This focus of this study is estimation of stable parameter of SP model using stated choice (SC) and attitudinal indicators. Attitudinal indicators used in this study are importance rating data for attributes in SP model. This kind of data is expected to be stable for a time. This feature will supplement SC model, which is sometime unstable. Three methods for integration are adopted in this study, which are based on behavioral hypothesis. These methods are compared on their efficiency and validity through a empirical analysis.

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The aim of this paper is to show some observation results on the travellers' perception error of service level of the Tokaido rail line and the Yokosuka rail line. The results of this survey are that observation results are explicitly confirmed perception errors on travel time and congestion rate and that perception error is attributable to the past experience of route service level, satisfaction level of service and information-gathering ability. Consequently, we proposed perception error models and route choice model incorporates probabilistic choice set generation in consideration of habitual behavior.

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In many social phenomena, including the ones related to transportation planning, human behaviors involve positive social interactions and various kinds of travel behaviors tend to conform to the actions of the majority. This paper overviews the method of measuring and modeling social interactions recently developed in microeconometrics, and examines their applicability to travel behavior analysis. In addition, a discrete choice model with enodogenous social interactions is proposed and applied to the micro data of illegal bicycle-parking behavior collected in Metropolitan Tokyo. With the proposed model, the level of optimal policy interventions such as police patrols and/or penalties to achieve the desirable and stable aggregate behaviors are empirically measured and evaluated.

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Travel demand of a new bus route opened in 2002 was forecasted in 2001 using the Behavioral Intention Method. In this paper, the predictive ability of the method was tested using actual travel demand and panel data from a survey implemented before and after the opening of the bus route. The tests indicated that behavioral-intention consistency used for the prediction was approximately compatible with the actual behavior-intention consistency. It was also indicated that there was a discrepancy between actual travel demand and predicted travel demand, but the discrepancy diminished by adjusting the numerical formulation used in the prediction The behavior-intention consistency was then analyzed using regression analysis for the purpose of future travel demand forecasting using the Behavioral Intention Method.

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C4.5, one of the popular data mining algorithms, is applied to represent non-compensatory choice behavior, and the predictability of the choice behavior is compared with the conventional logit model. The empirical analysis of this study is based on the SP data about the use of a hypothetical dynamic park and ride system. The results suggest that C4.5 and the conventional model have a same predictability in terms of the hit ratio. Though, the log-likelihood at convergence of the logit model with the dummy variables representing if-then rules produced by C4.5 is lower than that of the conventional logit model. When the dummy variables are added to the conventional logit model with conventional independent variables, the higher log-likelihood at convergence than that of the conventional logit model is given even after the insignificant variables are excluded.

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Despite the recent attention to schedule management of infrastructure projects, there are few studies that estimate the effects of implementing schedule management policies. This paper aims to evaluate the economic effects by implementing such policies. In order to estimate the economic effects by shortening infrastructure project periods, a classical economic growth model is formulated with the explicit consideration of project periods. The effects on some indexes such as GDP, the tax rate and the saving rate by shortening the periods of all the projects in a nation are examined. Finally, the model is applied to Japan and the increases in GDP based on some scenarios are calculated.

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Time-distance mapping is a method to obtain a spatial configuration of points, so that the Euclidean distances between points consist with the given time-distances. The approaches are divided broadly into two categories. One deals with the time-distances between all pairs of points. Multidimensional scaling (MDS) is generally applied to the solution. Another deals with the limited number of time-distances given to some of point-pairs such as links of a transportation network. However, no mathematical solution has been proposed for this type of time-distance mapping.
This paper proposes the generalized solution of the two types of time-distance mapping based on the non-linear least squares, which is mathematically clear and of practical use.

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In this paper, the mechanism that the modal choice in the outward trip of the round trip constrains that in the return trip and vise versa is characterized as strategic complementarity in modal choice. The positive feedbacks between bus demands and the number of bus operation functions in the bus market, if there exists strategic complementarity in modal choice by households. The market equilibrium model is formulated to investigate the multiplicity of the equilibrium and the impacts of transportation policy, such as car sharing, rental cycles, etc., which is designed to remedy strategic complementarity in modal choice, upon the market equilibrium and social welfare.

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Tokyo Metropolitan Area (TMA) has one of the densest railway networks. Railway passengers can choose one route from among several alternative mutes in consideration of the level of whole railway services from origin to destination. On the other hand, it is found that there is a similarity among the alternative routes. It suggests that the non-IIA route choice models (e. g., Multinomial Probit model with structured covariance matrix, Mixed Logit model, C-Logit model etc.) need to be applied to the analysis of the railway route choice behavior in the TMA. The characteristics of the models, however, have not been fully examined This study (1) compares with three non-IIA route choice models mentioned above in order to illustrate the characteristics and (2) proposes C*-Logit model on the basis of the characteristics by improvement of C-Logit model.

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In this study, Evaluation Weights Adjustment Model using AHP, that expresses the process of group decision making, is developed for the purpose of group decision making support. In the model, interindividual similarities regarding their preferences are considered. A group decision support system combined Evaluation Weights Adjustment Model and Group Decision Making Stress Method is also proposed. Two numerical examples calculated by using test data are shown. As the results, it is clarified that the process of group decision making is expressed by the system and that the system has effect on evaluators who would not change their preferences to change their minds.

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This study proposes a unified framework for formulating and computing the financial value of various infrastructure projects (real options). We first show that the pricing problems of typical real options can be represented as a VIP (variational inequality problem). Our analysis then reveals that the VIP reduces to a LCP (linear complementarity problem) by using certain function transformation techniques. This enables us to develop an efficient method for solving the real option problems in a unified manner, exploiting the recent advances in the theory of complementarity problems.

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