Papers - YAMAMOTO Toshiyuki

Driver's route choice behavior: Analysis by data mining algorithms Reviewed

Toshiyuki Yamamoto, Ryuichi Kitamura and Junichiro Fujii

Transportation Research Record   ( 1807 ) page： 59-66   2002

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Decision trees and production rules, which are among the methods used in knowledge discovery and data mining, are applied in this study to investigate drivers' route choice behavior. These methods have an advantage over artificial neural networks, another data mining method often used in the analysis of travel behavior, that they facilitate the determination of the relationships between the explanatory variables and the choice. Specifically, the C4.5 algorithm, which produces a decision tree and a set of production rules from the tree, is applied in this study. Two surveys were carried out to collect data on drivers' route choice behavior between two alternative routes on expressway networks. The two data sets include the expected minimum, maximum and average travel times along each alternative route as indicated by the respondent, as well as his socio-demographic attributes. The results of the analyses suggest that different expected travel times influence route choice in different cases, and that a maximum or average travel time determines route choice in some cases regardless of other attributes. The results of a comparison analysis between the C4.5 algorithm and discrete choice models indicate the superior ability offered by the former in representing drivers' route choice.

A Simulation Analysis for the Management of an Electric-Vehicle Sharing System: The Case of the Kyoto Public-Car System Reviewed

Shoichiro Nakayama, Toshiyuki Yamamoto, and Ryuichi Kitamura

Transportation Research Record   ( 1791 ) page： 99-104   2002

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A multi-station electric-vehicle (EV) sharing system has been in operation on an experimental basis in Kyoto, Japan. Members of the system can check out EVs at a station and return them at any station. To reduce the cost of system operation, EVs are not reallocated to stations by the operator. This feature, at the same time, may delimit the efficiency of the system because of the mismatch between the spatial distribution of the demand and the distribution of the EVs among the stations. We construct a simulation model of system operation, and configure the optimal management of the system using genetic algorithms, with the number of checkouts per vehicle set as the objective function to be maximized. The number of vehicles, parking capacity at each station and number of members are among the decision variables. The results suggest that the optimal number of EVs is about a half of the total number of parking stalls. This implies that the efficiency of the system does not necessarily improve as more EVs are introduced. Also, the results suggest that even if a new station is introduced into the system, the efficiency does not necessarily improve.

Modeling the Simultaneity in Injury Causation in Multivehicle Collisions Reviewed

Ouyang, Y., Shankar, V. and Yamamoto, T.

Transportation Research Record   ( 1784 ) page： 143-152   2002

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Language：English   Publishing type：Research paper (scientific journal)  

Constrained by the single-equation specification, traditional logit or probit models for traffic accident severities can only model one severity at a time. Single-equation models fails to account for the simultaneity of injury severity outcomes in multivehicle collisions. An attempt to account for this simultaneity is made by presenting a simultaneous logit model of endogenous interrelationships among injury severities in multivehicle collisions. Car-truck collision data in Washington State between 1990 and 1996 are used for a simultaneous binary logit model to determine the effects of various exogenous factors in the presence of injury simultaneity, and empirical comparisons with traditional single-equation logit models are also provided. The methodology presented provides for significant efficiency gain when correlation between injury severities is high and can easily be extended to multiple severity categories in multi-vehicle collisions. Empirically, it is also found that parameter estimates for key roadway design variables may vary greatly between a simultaneous logit and its single-equation counterparts. Another finding from this methodology is that preventive measures for head-on collisions, or enforcement of drunk-driving regulations among truck drivers, as well as limiting the occurrence of high-speed curve designs on truck routes, appear to be the most likely factors for significant potential savings in social costs from car-truck collisions.

路側障害物との接触事故による身体損傷程度の分析

山本俊行，Shankar, V.

交通科学   Vol. 32   page： 77-80   2002

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路面電車の需要予測手法に関する実務的課題と対応例

細川寛，山本俊行，飯田祐三，白水靖郎

土木学会第57回年次学術講演会講演概要集CD-ROM     page： IV-400   2002

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Equilibrium or Disequilibrium? Behavioral Aspect of Driver's Route Choice

Takayuki Morikawa, Shinya Kurauchi and Toshiyuki Yamamoto

Proceedings of the International Symposium on Transport Simulation, R. Kitamura and M. Kuwahara (eds.)     2002

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An Overview of PCATS/DEBNetS Micro-Simulation System: Its Development, Extension, and Application to Demand Forecasting

Ryuichi Kitamura, Akira Kikuchi, Satoshi Fujii and Toshiyuki Yamamoto

Proceedings of the International Symposium on Transport Simulation, R. Kitamura and M. Kuwahara (eds.)     page： 43-62   2002

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Modeling the Simultaneity in Injury Causation in Multi-vehicle Collisions Reviewed

Yanfeng Ouyang, Venkataraman N. Shankar, Toshiyuki Yamamoto

Preprint CD-ROM of the 81th Annual Meeting of the Transportation Research Board     2002

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A methodology for weighting data from complex endogenous sampling Reviewed

Ryuichi Kitamura, Toshiyuki Yamamoto and Hiromu Sakai

Preprint CD-ROM of the 81th Annual Meeting of the Transportation Research Board     2002

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The methodology for weighting endogenous samples is extended in this study and applied to a multi-dimensional choice-based sample and an enriched sample. It is shown that a unified weight exists for a sample obtained from a complex endogenous sampling scheme. Problems with conventional weighting methods for samples from endogenous sampling, such as roadside surveys, are pointed out and an improved weighting scheme is proposed. Coefficient estimates of discrete choice models with and without weights are compared, and the importance of applying appropriate weights and a valid covariance estimator is pointed out.

A simulation analysis for the management of an electric-vehicle sharing system: the case of the Kyoto Public-Car System Reviewed

Shoichiro Nakayama, Toshiyuki Yamamoto and Ryuichi Kitamura

Preprint CD-ROM of the 81th Annual Meeting of the Transportation Research Board     2002

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Language：English  

A multi-station electric-vehicle (EV) sharing system has been in operation on an experimental basis in Kyoto, Japan. Members of the system can check out EVs at a station and return them at any station. To reduce the cost of system operation, EVs are not reallocated to stations by the operator. This feature, at the same time, may delimit the efficiency of the system because of the mismatch between the spatial distribution of the demand and the distribution of the EVs among the stations. We construct a simulation model of system operation, and configure the optimal management of the system using genetic algorithms, with the number of checkouts per vehicle set as the objective function to be maximized. The number of vehicles, parking capacity at each station and number of members are among the decision variables. The results suggest that the optimal number of EVs is about a half of the total number of parking stalls. This implies that the efficiency of the system does not necessarily improve as more EVs are introduced. Also, the results suggest that even if a new station is introduced into the system, the efficiency does not necessarily improve.

An analysis of the effects of French vehicle inspection program and grant for scrappage on household vehicle transaction Reviewed

Toshiyuki Yamamoto, Jean-Loup Madre and Ryuichi Kitamura

Preprint CD-ROM of the 81th Annual Meeting of the Transportation Research Board     2002

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French government has implemented a periodical vehicle inspection program, which aims at maintaining proper functioning of the vehicle and ensuring the emissions control systems installed on the vehicle work properly. Also, an incentive program for scrapping old vehicles was introduced in 1994 through 1996 to promote the replacement of those vehicles with higher emissions by newer vehicles with lower emissions. A hazard-based duration model of household vehicle transaction behavior has been developed in this study to examine the effects of the inspection program and the grant for scrappage on vehicle transaction timing. The model is developed as a competing risks model assuming the following three types of competing risks: replacing one of the vehicles in the household fleet, disposing of one vehicle in the fleet, and acquiring one vehicle to add to the fleet. The empirical analysis is carried out using the panel data of French households' vehicle ownership from 1984 to 1998, obtained by the panel survey called Parc-Auto, which has been conducted by a French marketing firm, SOFRES, since 1976. The long panel observation period facilitates the introduction of macro-economic indicators into the model, enabling the analysis to distinguish the effects of policy measures from macro-economic factors. The empirical results indicate that the expected vehicle holding duration becomes 1.23 times longer under the inspection program than before the program commenced, given that the vehicle is replaced by another vehicle at the end of the holding duration; and that the conditional probability of replacing a vehicle aged 10 years and over becomes 1.19 times higher when the grant for scrappage is available.

An analysis of driver's route choice behavior by data mining algorithms Reviewed

Toshiyuki Yamamoto, Ryuichi Kitamura and Junichiro Fujii

Preprint CD-ROM of the 81th Annual Meeting of the Transportation Research Board     2002

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Authorship：Lead author   Language：English  

Decision trees and production rules, which are among the methods used in knowledge discovery and data mining, are applied in this study to investigate drivers' route choice behavior. These methods have an advantage over artificial neural networks, another data mining method often used in the analysis of travel behavior, that they facilitate the determination of the relationships between the explanatory variables and the choice. Specifically, the C4.5 algorithm, which produces a decision tree and a set of production rules from the tree, is applied in this study. Two surveys were carried out to collect data on drivers' route choice behavior between two alternative routes on expressway networks. The two data sets include the expected minimum, maximum and average travel times along each alternative route as indicated by the respondent, as well as his socio-demographic attributes. The results of the analyses suggest that different expected travel times influence route choice in different cases, and that a maximum or average travel time determines route choice in some cases regardless of other attributes. The results of the comparison analyses between the C4.5 algorithm and discrete choice models indicate the superior ability offered by the algorithm in representing drivers' route choice.

Bivariate ordered-response probit model of driver's and passenger's injury severities in collision with fixed objects Reviewed

Toshiyuki Yamamoto and Venkataraman N. Shankar

Preprint CD-ROM of the 81th Annual Meeting of the Transportation Research Board     2002

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Previous studies have examined injury severity of vehicle accidents in relation with human factors, vehicle type, highway design, and environmental conditions. Several types of statistical models of injury severity have been developed based on driver's injury in some studies, and on most severely injured occupant's severity in others. A bivariate ordered-response probit model of driver's and most severely injured passenger's severity in collisions with fixed objects is developed in this study. Exact severity of passenger is not necessarily observed, especially when only most severe injury of the accident and driver's injury are recorded in the police reports. To accommodate passenger severities as well, we explicitly develop a partial observability model of passengers' injury severity in multi-occupant vehicle. The model has consistent coefficients for the driver's severity between single-occupant vehicle and multiple-occupant vehicle accidents, and provides more efficient coefficient estimates by taking into account the common unobserved factors between driver's severity and passenger's severity. The results of the empirical analysis using 4-year statewide accident data in Washington State reveal the effects of driver's characteristics, vehicle attributes, types of objects, and environmental conditions on both driver's and passengers' injury severities, and that their severities have different elasticities to some of the risk factors.

A Study for Efficient Management of an Electric-Vehicle Sharing System without Vehicle Dispatching Reviewed

Shoichiro Nakayama, Toshiyuki Yamamoto and Ryuichi Kitamura

Infrastructure Planning Review   Vol. 19   page： 481-487   2002

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Language：Japanese   Publishing type：Research paper (scientific journal)  

A promising approach for expedient market penetration of electric vehicles is a multi-station electric vehicle sharing system. Such a system, however, contains several problems, e.g., vehicles may concentrate at particular stations, deterring efficient operation of the system. The parameters that define system operation must be set at their optimal values for efficient operation. Genetic algorithms are used in this study along with a simulation model of the vehicle sharing system to optimize system operation and to examine ways to attain efficiency in system operation. It is founded from the results that the total number of parking slots needs to be twice as many as the number of vehicles and that adding a new station does not necessarily improve the efficiency of system operation.

In-vehicle route recommendation system with learning by neural networks Reviewed

Toshiyuki Yamamoto, Shoichiro Nakayama and RYuichi Kitamura

Proceedings of Joint 1st International Conference on Soft Computing and Intelligent Systems and 3rd International Symposium on Advanced Intelligent Systems (CD-ROM)     2002

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A two-stage route recommendation system is developed. The system consists of the minimum cost path search stage and the route choice stage. Several routes are selected according to multiple additive cost functions in the former stage, and a recommendation route is chosen from among the selected routes according to another function capable of nonlinear manipulations in the latter stage. These functions are developed as neural networks. The effectiveness of the system is examined using an experimental data set.

Modelling the Effects of a Periodical Vehicle Inspection Program on Household Vehicle Ownership Reviewed

Toshiyuki Yamamoto, Ryuichi Kitamura and Hiroaki Fujii

Travel Behaviour Research: The Leading Edge     page： 435-450   2001

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路側障害物との接触事故による身体損傷程度の分析

山本俊行，Shankar, V.

大阪交通科学研究会平成13年度学術研究発表会講演論文集     2001

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The Effects of a Periodic Vehicle Inspection Program on Household Vehicle Transaction Behavior Reviewed

Toshiyuki Yamamoto, Ryuichi Kitamura and Hiroaki Fujii

Journal of Infrastructure Planning and Manegement   ( 667/IV-50 ) page： 137-146   2001

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A hazard-based duration model of household vehicle transactions behavior is developed in this study to examine the effects of Japanese periodic inspection program, and an empirical analysis is carried out using the data set obtained by a panel survey conducted in Kyoto City. The results of model estimation suggest a higher probability of replacing or disposing of a vehicle whose inspection is scheduled within the next 6 months. Results of a simulation-based scenario analysis indicate that a raise in the inspection cost accelerates the replacement of vehicles households own without affecting the number of vehicles they own, and that the extension of the inspection period decelerates the scrappage of vehicles and increases the number of vehicles households own.

Weighted Estimation of Discrete Choice Models with Data from Complex Endogenous Sampling Reviewed

Ryuichi Kitamura, Hiromu Sakai and Toshiyuki Yamamoto

Journal of Infrastructure Planning and Management   ( 667/IV-50 ) page： 103-111   2001

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Language：Japanese   Publishing type：Research paper (scientific journal)  

The methodology for weighting endogenous samples is extended in this study and applied to a multi-dimensional choice-based sample. It is shown that a unified weight exists for samples obtained from complex endogenous sampling schemes. Problems with conventional weighting methods for samples from endogenous sampling, such as roadside surveys, are pointed out and an improved weighting scheme is proposed. Coefficient estimates of discrete choice models with and without weights are compared, and the importance of applying an appropriate covariance estimator is pointed out.

An Analysis of Route and Departure Time Choice Behavior for Business Trip Chains Reviewed

Toshiyuki Yamamoto, Ryuichi Kitamura and Yoshiaki Kumada

Journal of Infrastructure Planning and Management   ( 667/IV-50 ) page： 33-40   2001

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Unlike commute trips, business trips are generated throughout business hours, and several destinations tend to be visited in a trip chain. In this study, a route and departure time choice model for a business trip chain is developed based on the hypothesis that the trip chain is made so as to minimize the total cost, including costs associated with other business activities before and after the trip chain, as well as costs associated with travel time, toll fees, and the probability being late. The empirical analysis is carried out using a data set obtained from a survey of business trip drivers. The properties of the estimated model are examined on a sample case