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Latest Urban Rail Demand Forecast Model System in the Tokyo Metropolitan Area
Author(s) -
Hironori Kato,
Daisuke Fukuda,
Yoshihisa Yamashita,
Seiji Iwakura,
Tetsuo Yai
Publication year - 2017
Publication title -
transportation research record journal of the transportation research board
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.624
H-Index - 119
eISSN - 2169-4052
pISSN - 0361-1981
DOI - 10.3141/2668-07
Subject(s) - metropolitan area , transport engineering , urban planning , urban rail transit , urban rail , rail network , investment (military) , trips architecture , business , geography , engineering , civil engineering , archaeology , politics , law , political science
A model system to forecast urban rail travel demand technically supported the formulation of the Tokyo Urban Rail Development Master Plan for 2016. The model system was included in the forthcoming 15-year urban rail investment strategy for Tokyo and was used to make a quantitative assessment of urban rail projects, including 24 new rail development projects that had been proposed in response to expected changes in sociodemographic patterns, land use markets, and the government’s latest transportation policy goals. The system covered the entire urban rail network within the Tokyo metropolitan area, with approximately a 50-km radius and a population of more than 34 million. The system would have to have handled more than 80 million trips per day. Three demand models were used to predict daily rail passenger link flows: urban rail, airport rail access, and high-speed rail access. These practical models had unique characteristics, such as incorporating differences in behavior between older and younger travelers, reflecting expected influences of urban redevelopment on trip generation and distribution, highlighting urban rail access to airports or high-speed-rail stations, examining effects of in-vehicle crowding on rail route choice, and deploying mode choice models for urban rail station access–egress for rail route choice. The authors 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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