A new research paper on urban rail passenger demand forecasting in Tokyo

A research paper on the Tokyo Metropolitan Area rail demand forecasting model used in the April 2016 Ministry of Land, Infrastructure, Transport and Tourism Transport Policy Council report, “Future Urban Railways in the Tokyo Area“, has been accepted for publication in the international journal “Transportation Research Record” (The conference paper can be viewed here). This is the result of several years of discussion and analysis with Prof. Iwakura of Shibaura Institute of Technology, Prof. Kato of the University of Tokyo, Mr. Ito of the Japan Transport and Tourism Research Institute, and members of Shakai Systems Co. We would like to express our deepest gratitude to everyone involved.


Latest Urban Rail Demand Forecast Model System in the Tokyo Metropolitan Area

By Hironori Kato, Daisuke Fukuda, Yoshihisa Yamashita, Seiji Iwakura and Tetsuo Yai

This paper reports on an urban rail travel demand forecast model system, which technically supported the formulation of the Tokyo Urban Rail Development Master-plan 2016. The model system was included in the forthcoming 15-year urban rail investment strategy for Tokyo. The model system was utilized to quantitatively assess urban rail projects including 24 new rail development projects, which had been proposed in response to expected changes in socio-demographic patterns, land-use market, and the government’s latest transportation policy goals. The system covers the entire urban rail network within the Tokyo Metropolitan Area (TMA) of approximately 50-km radius with a population of over 34 million. The system must handle over 80 million trips per day. Three demand models are used to predict daily rail passenger link flows: the urban rail demand model, the airport rail access demand model, and the high-speed-rail rail access demand model. These practical models have unique characteristics such as incorporating differences in behavior between aged and non-aged travelers, reflecting expected influences of urban redevelopment on trip generation and distribution, highlighting urban rail access to airports or high-speed-rail stations, examining impacts of in-vehicle crowding on rail route choice, and deploying urban rail-station access/egress mode choice models for rail route choice. It is concluded that the model system would be well calibrated with observed data for reproducing travel patterns, identifying potential problems, assessing proposed projects, presenting results with high accuracy, and assisting decision-making of urban rail planners.


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