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Integrating smart card records and dockless bike-sharing data to understand the effect of the built environment on cycling as a feeder mode for metro trips

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  • Zhang, Yuan
  • Chen, Xiao-Jian
  • Gao, Song
  • Gong, Yongxi
  • Liu, Yu

Abstract

Urban transportation and planning are at a pivotal juncture, requiring a nuanced understanding of the built environment's impact on dockless bike sharing (DBS) to metro transfer trips. Existing methodologies, often focused on DBS trips around metro stations or limited by scant datasets, overlook the pressing need for a method to discern large-scale DBS-metro trips as DBS becomes a standard feeder mode for metro trips and the yet unexplored spatial lag effects of the built environment on DBS-metro interactions. To bridge these gaps, we develop a method integrating smart card records and DBS data, revealing a comprehensive trip chain encompassing both metro and DBS. Our application of association rule algorithms to large-scale data provides detailed spatial insights into feeder trips. We employ a network-adjacency-based partial spatial Durbin model, tailored with a negative binomial regression for count data and maximum likelihood estimation for continuous data. Analysis from Shenzhen reveals: (1) A strong correlation is observed between the count of trips using cycling as a feeder mode (COUNT) and the location of stations within the metro network structure. Notably, the COUNT shows more significant aggregation when compared to the ratio of DBS-metro transfer trips to the total metro trips at each station (RATIO); (2) significant influence of both local and adjacent spatial variables of the built environment on the RATIO and COUNT of cycling trips; (3) specific factors like feeder station location, city center proximity, Street greenness view situation, and road intersection density significantly influencing the cycling feeder mode for metro trips; (4) Moreover, areas with more urban villages and industry appeared to contribute to the cycling feeder mode for metro trips, both in terms of RATIO and COUNT. This study underscores the necessity of fostering a conducive built environment to leverage DBS's potential to bridge the last-mile gap.

Suggested Citation

  • Zhang, Yuan & Chen, Xiao-Jian & Gao, Song & Gong, Yongxi & Liu, Yu, 2024. "Integrating smart card records and dockless bike-sharing data to understand the effect of the built environment on cycling as a feeder mode for metro trips," Journal of Transport Geography, Elsevier, vol. 121(C).
    • Handle: RePEc:eee:jotrge:v:121:y:2024:i:c:s0966692324002047
    DOI: 10.1016/j.jtrangeo.2024.103995
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    References listed on IDEAS

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