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Joint analysis of the impacts of built environment on bikeshare station capacity and trip attractions

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  • Wang, Kailai
  • Chen, Yu-Jen

Abstract

Shared micromobility is proliferating throughout the world. Many researchers have extensively studied the links among factors representing the built and natural environment and bikeshare demand. One common feature of the existing demand models is that they view bikeshare infrastructure as a group of exogenous variables along with other influential factors. Indeed, this assumption is seldom true in planning practices. Bikeshare system operators usually allocate resources in dense urban areas based upon the environmental correlates. This study contributes to the literature by jointly exploring the determinants of bikeshare station capacity (i.e., the number of docking points) and trip arrivals at the station-level. The research dataset is constructed from the Citi Bike system in New York City in September 2016. The analytical results reveal that the effects of built environment characteristics on bikeshare usage could be carefully considered during the system installation process. We find existing bicycle facilities do not significantly influence the supply of docking points at the station-level. However, they exert direct and positive effects on hourly trip arrivals. The findings improve our understanding of the bikeshare system installation process.

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  • Wang, Kailai & Chen, Yu-Jen, 2020. "Joint analysis of the impacts of built environment on bikeshare station capacity and trip attractions," Journal of Transport Geography, Elsevier, vol. 82(C).
    • Handle: RePEc:eee:jotrge:v:82:y:2020:i:c:s0966692319303916
    DOI: 10.1016/j.jtrangeo.2019.102603
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    Cited by:

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    2. Morteza Hossein Sabbaghian & David Llopis-Castelló & Alfredo García, 2023. "A Safe Infrastructure for Micromobility: The Current State of Knowledge," Sustainability, MDPI, vol. 15(13), pages 1-20, June.
    3. Jin, Scarlett T. & Wang, Lei & Sui, Daniel, 2023. "How the built environment affects E-scooter sharing link flows: A machine learning approach," Journal of Transport Geography, Elsevier, vol. 112(C).
    4. Dehdari Ebrahimi, Zhila & Momenitabar, Mohsen & Nasri, Arefeh A. & Mattson, Jeremy, 2022. "Using a GIS-based spatial approach to determine the optimal locations of bikeshare stations: The case of Washington D.C," Transport Policy, Elsevier, vol. 127(C), pages 48-60.
    5. Hu, Songhua & Xiong, Chenfeng & Liu, Zhanqin & Zhang, Lei, 2021. "Examining spatiotemporal changing patterns of bike-sharing usage during COVID-19 pandemic," Journal of Transport Geography, Elsevier, vol. 91(C).
    6. Qiu, Waishan & Chang, Hector, 2021. "The interplay between dockless bikeshare and bus for small-size cities in the US: A case study of Ithaca," Journal of Transport Geography, Elsevier, vol. 96(C).
    7. Schimohr, Katja & Scheiner, Joachim, 2021. "Spatial and temporal analysis of bike-sharing use in Cologne taking into account a public transit disruption," Journal of Transport Geography, Elsevier, vol. 92(C).
    8. Steve O’Hern & Nora Estgfaeller, 2020. "A Scientometric Review of Powered Micromobility," Sustainability, MDPI, vol. 12(22), pages 1-21, November.
    9. Tyndall, Justin, 2022. "Complementarity of dockless mircomobility and rail transit," Journal of Transport Geography, Elsevier, vol. 103(C).

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