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An approach to imputing destination activities for inclusion in measures of bicycle accessibility

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  • Li, Aoyong
  • Huang, Yizhe
  • Axhausen, Kay W.

Abstract

This paper proposes a new method to estimate bicycle accessibility for various trip purposes based on a massive dockless bike-sharing dataset in Shanghai, China. Specifically, a Dirichlet multinomial regression topic model (DMR model) is applied to identify bicycle trajectories' trip purposes, simultaneously considering arrival time and drop-off location. Based on obtained trip purposes, we estimate impedance functions using a negative exponential function. Finally, based on estimated impedance functions, two cases of bicycle accessibility for two different purposes - restaurant and hospital - are presented in Shanghais central area. The results show that almost 90% of bicycle trips are less than 30 min or 5 km. Although the difference between the impedance functions between various trip purposes is not significant, we find that trip purposes of “Work and School” have the highest travel impedance for bicyclists. Cyclists in Shanghai accept longer bicycle travel times for leisure (e.g., shopping) than for commuting (e.g., work or school).

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  • Li, Aoyong & Huang, Yizhe & Axhausen, Kay W., 2020. "An approach to imputing destination activities for inclusion in measures of bicycle accessibility," Journal of Transport Geography, Elsevier, vol. 82(C).
    • Handle: RePEc:eee:jotrge:v:82:y:2020:i:c:s0966692319300717
    DOI: 10.1016/j.jtrangeo.2019.102566
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    2. Gao, Kun & Yang, Ying & Li, Aoyong & Li, Junhong & Yu, Bo, 2021. "Quantifying economic benefits from free-floating bike-sharing systems: A trip-level inference approach and city-scale analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 89-103.
    3. Bi, Hui & Gao, Hui & Li, Aoyong & Ye, Zhirui, 2024. "Using topic modeling to unravel the nuanced effects of built environment on bicycle-metro integrated usage," Transportation Research Part A: Policy and Practice, Elsevier, vol. 185(C).
    4. Ross-Perez, Antonio & Walton, Neil & Pinto, Nuno, 2022. "Identifying trip purpose from a dockless bike-sharing system in Manchester," Journal of Transport Geography, Elsevier, vol. 99(C).
    5. Xue, Mengtian & Zhang, Bin & Chen, Siyuan & Zhao, Yuandong & Wang, Zhaohua, 2024. "How does extreme temperature affect shared travel? Evidence from bike-sharing order flow in China," Journal of Transport Geography, Elsevier, vol. 118(C).
    6. He, Zhangyuan & Zhao, Pengjun & Xiao, Zuopeng & Huang, Xin & Li, Zhaoxiang & Kang, Tingting, 2024. "Exploring the distance decay in port hinterlands under port regionalization using truck GPS data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 181(C).
    7. Agnieszka Jaszczak & Agnieszka Morawiak & Joanna Żukowska, 2020. "Cycling as a Sustainable Transport Alternative in Polish Cittaslow Towns," Sustainability, MDPI, vol. 12(12), pages 1-23, June.
    8. Bi, Hui & Gao, Hui & Li, Aoyong & Ye, Zhirui, 2024. "Investigation on the joint travel behavior in bike sharing systems during the COVID-19 pandemic: Insights from New York City," Journal of Transport Geography, Elsevier, vol. 117(C).
    9. Li, Aoyong & Zhao, Pengxiang & Huang, Yizhe & Gao, Kun & Axhausen, Kay W., 2020. "An empirical analysis of dockless bike-sharing utilization and its explanatory factors: Case study from Shanghai, China," Journal of Transport Geography, Elsevier, vol. 88(C).
    10. Li, Shaoying & Zhuang, Caigang & Tan, Zhangzhi & Gao, Feng & Lai, Zhipeng & Wu, Zhifeng, 2021. "Inferring the trip purposes and uncovering spatio-temporal activity patterns from dockless shared bike dataset in Shenzhen, China," Journal of Transport Geography, Elsevier, vol. 91(C).

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