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Examining factors associated with bike-and-ride (BnR) activities around metro stations in large-scale dockless bikesharing systems

Author

Listed:
  • Hu, Songhua
  • Chen, Mingyang
  • Jiang, Yuan
  • Sun, Wei
  • Xiong, Chenfeng

Abstract

Dockless bikesharing (DBS) has been considered as a solution to the first and last mile problem of metro connectivity. Leveraging data covering all DBS programs in Shanghai, China, this study evaluated bike-and-ride (BnR) activities in DBS-metro systems via four metrics: BnR trip count, BnR rate, shared-bike utilization rate, and catchment size (85th percentile transfer distance). A set of generalized additive models considering marginal nonlinear interactions was fitted to examine associations between the four metrics and external environment, including land use, socio-demographics, roadway designs, transportation facilities, metro station features, and DBS operator features. Different buffer sizes measured by network distance were tested to check model robustness and find optimal buffers. Results showed that: 1) metro stations near the city center exhibited greater BnR trip count, higher BnR rate, lower shared-bike utilization rate, and smaller catchment size; 2) proportion of public and residential land suggested positive relationships with BnR trip count but lose their significances after offsetting metro ridership; 3) numbers of colleges, shopping malls, and carsharing stations presented positive relationships with both BnR trip count and BnR rate; 4) land use mix was significantly positively associated with BnR trip count only when buffer size was larger than 1.5 km; 5) regions with higher population density went from less BnR activities in the city center to more BnR activities in the suburbs; 6) Large DBS operators outperformed small ones in BnR trip count but not in bike utilization rate. Taken together, this study uncovers a spatially disproportionate and supply-demand unbalanced distribution of DBS resources, which could attenuate the efficiency and attractiveness of using DBS to BnR. DBS operators and local governments should evaluate DBS systems from multiple perspectives to avoid an oversupplied and over-competing market.

Suggested Citation

  • Hu, Songhua & Chen, Mingyang & Jiang, Yuan & Sun, Wei & Xiong, Chenfeng, 2022. "Examining factors associated with bike-and-ride (BnR) activities around metro stations in large-scale dockless bikesharing systems," Journal of Transport Geography, Elsevier, vol. 98(C).
    • Handle: RePEc:eee:jotrge:v:98:y:2022:i:c:s0966692321003240
    DOI: 10.1016/j.jtrangeo.2021.103271
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    Cited by:

    1. Hu, Songhua & Xiong, Chenfeng & Chen, Peng & Schonfeld, Paul, 2023. "Examining nonlinearity in population inflow estimation using big data: An empirical comparison of explainable machine learning models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
    2. Cheng, Long & Huang, Jie & Jin, Tanhua & Chen, Wendong & Li, Aoyong & Witlox, Frank, 2023. "Comparison of station-based and free-floating bikeshare systems as feeder modes to the metro," Journal of Transport Geography, Elsevier, vol. 107(C).
    3. Liu, Yang & Feng, Tao & Shi, Zhuangbin & He, Mingwei, 2022. "Understanding the route choice behaviour of metro-bikeshare users," Transportation Research Part A: Policy and Practice, Elsevier, vol. 166(C), pages 460-475.

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