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The determinants of travel demand between rail stations: A direct transit demand model using multilevel analysis for the Washington D.C. Metrorail system

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  • Iseki, Hiroyuki
  • Liu, Chao
  • Knaap, Gerrit

Abstract

Transit demand models have become indispensable tools for transit planners and mangers in the 21st century. By quantifying the relationship between transit ridership, the cost of travel, the character of the built environment, and the socio-economic characteristics of riders, such models enable transit planners and managers to make more informed decisions regarding transit routes, levels of service, transit fares, transit oriented development (TOD) and other transit supply parameters. Direct ridership models (DRMs) are now able to address transit ridership at each station directly with higher sensitivity to built environmental characteristics in well-defined station areas. More recently Origin-Destination DRMs have begun to use data on ridership between each origin and destination pair to facilitate more precise estimation of transit demand by origin-destination pair.

Suggested Citation

  • Iseki, Hiroyuki & Liu, Chao & Knaap, Gerrit, 2018. "The determinants of travel demand between rail stations: A direct transit demand model using multilevel analysis for the Washington D.C. Metrorail system," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 635-649.
    • Handle: RePEc:eee:transa:v:116:y:2018:i:c:p:635-649
    DOI: 10.1016/j.tra.2018.06.011
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    References listed on IDEAS

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    1. Shao, Qifan & Zhang, Wenjia & Cao, Xinyu & Yang, Jiawen & Yin, Jie, 2020. "Threshold and moderating effects of land use on metro ridership in Shenzhen: Implications for TOD planning," Journal of Transport Geography, Elsevier, vol. 89(C).
    2. Yan, Xiang & Liu, Xinyu & Zhao, Xilei, 2020. "Using machine learning for direct demand modeling of ridesourcing services in Chicago," Journal of Transport Geography, Elsevier, vol. 83(C).
    3. Du, Qiang & Zhou, Yuqing & Huang, Youdan & Wang, Yalei & Bai, Libiao, 2022. "Spatiotemporal exploration of the non-linear impacts of accessibility on metro ridership," Journal of Transport Geography, Elsevier, vol. 102(C).
    4. Li, Shaoying & Lyu, Dijiang & Huang, Guanping & Zhang, Xiaohu & Gao, Feng & Chen, Yuting & Liu, Xiaoping, 2020. "Spatially varying impacts of built environment factors on rail transit ridership at station level: A case study in Guangzhou, China," Journal of Transport Geography, Elsevier, vol. 82(C).
    5. Xinyu Zhuang & Li Zhang & Jie Lu, 2022. "Past—Present—Future: Urban Spatial Succession and Transition of Rail Transit Station Zones in Japan," IJERPH, MDPI, vol. 19(20), pages 1-35, October.
    6. Alimo, Philip Kofi & Agyeman, Stephen & Zankawah, Sanusi Mumuni & Yu, Chunhui & Cheng, Lin & Ma, Wanjing, 2022. "Factors causing low demand for a suburban passenger train in Sekondi-Takoradi," Journal of Transport Geography, Elsevier, vol. 98(C).
    7. Elisa Borowski & Jason Soria & Joseph Schofer & Amanda Stathopoulos, 2020. "Disparities in ridesourcing demand for mobility resilience: A multilevel analysis of neighborhood effects in Chicago, Illinois," Papers 2010.15889, arXiv.org.
    8. Xuesong Feng & Zhibin Tao & Xuejun Niu & Zejing Ruan, 2021. "Multi-Objective Land Use Allocation Optimization in View of Overlapped Influences of Rail Transit Stations," Sustainability, MDPI, vol. 13(23), pages 1-14, November.
    9. Ding, Chuan & Cao, Xinyu & Liu, Chao, 2019. "How does the station-area built environment influence Metrorail ridership? Using gradient boosting decision trees to identify non-linear thresholds," Journal of Transport Geography, Elsevier, vol. 77(C), pages 70-78.

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