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An optimization approach for equitable bicycle share station siting

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  • Conrow, Lindsey
  • Murray, Alan T.
  • Fischer, Heather A.

Abstract

Bicycle share systems are becoming an increasingly popular feature of many urban areas across the United States. While these systems aim to increase transit mode options as well as overall bicycle ridership, bike share programs also face challenges and criticisms related to density and inequitable distribution of services. Key factors in the success of bicycle share include high station density as well as services that reach a variety of neighborhoods, though many current systems do not reach low-income areas. Equitable station distribution therefore appears to be a complex problem to address. We propose utilizing spatial analytics, including GIS and spatial optimization, to help site bicycle share stations across an urban region. Specifically we seek to apply a covering model to assess how many bicycle stations are needed, and where they should be located, so no user would have to travel too far for access. The city of Phoenix, Arizona, is used as a case study to illustrate the coverage and access tradeoffs possible through different investment strategies. Accordingly, for a given investment level, the set of stations is identified that provides the best access to the designated bike path network for the greatest number of potential users. Further, tradeoff options that differentially favor either network or population coverage are possible, and can be identified and evaluated through the proposed analytical framework.

Suggested Citation

  • Conrow, Lindsey & Murray, Alan T. & Fischer, Heather A., 2018. "An optimization approach for equitable bicycle share station siting," Journal of Transport Geography, Elsevier, vol. 69(C), pages 163-170.
    • Handle: RePEc:eee:jotrge:v:69:y:2018:i:c:p:163-170
    DOI: 10.1016/j.jtrangeo.2018.04.023
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    Cited by:

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    2. Feng, Xin & Murray, Alan T. & Church, Richard L., 2021. "Drone service response: Spatiotemporal heterogeneity implications," Journal of Transport Geography, Elsevier, vol. 93(C).
    3. Beairsto, Jeneva & Tian, Yufan & Zheng, Linyu & Zhao, Qunshan & Hong, Jinhyun, 2020. "Identifying locations for new bike-sharing stations in Glasgow: an analysis of spatial equity and demand factors," OSF Preprints apyfn, Center for Open Science.
    4. Li, Yue & Luo, Hao & Cai, Hua, 2023. "Photovoltaic-battery powered bike share stations are not necessarily energy self-sufficient," Applied Energy, Elsevier, vol. 348(C).
    5. Mix, Richard & Hurtubia, Ricardo & Raveau, Sebastián, 2022. "Optimal location of bike-sharing stations: A built environment and accessibility approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 160(C), pages 126-142.
    6. Gabriella Balacco & Mario Binetti & Leonardo Caggiani & Michele Ottomanelli, 2021. "A Novel Distributed System of e-Vehicle Charging Stations Based on Pumps as Turbine to Support Sustainable Micromobility," Sustainability, MDPI, vol. 13(4), pages 1-14, February.
    7. Qian, Xiaodong & Jaller, Miguel & Circella, Giovanni, 2022. "Equitable distribution of bikeshare stations: An optimization approach," Journal of Transport Geography, Elsevier, vol. 98(C).
    8. Kim, Minjun & Cho, Gi-Hyoug, 2021. "Analysis on bike-share ridership for origin-destination pairs: Effects of public transit route characteristics and land-use patterns," Journal of Transport Geography, Elsevier, vol. 93(C).
    9. Koglin, Till & Mukhtar-Landgren, Dalia, 2021. "Contested values in bike-sharing mobilities – A case study from Sweden," Journal of Transport Geography, Elsevier, vol. 92(C).
    10. Ospina, Juan P. & Duque, Juan C. & Botero-Fernández, Verónica & Montoya, Alejandro, 2022. "The maximal covering bicycle network design problem," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 222-236.
    11. Yang, Lin & Zhang, Fayong & Kwan, Mei-Po & Wang, Ke & Zuo, Zejun & Xia, Shaotian & Zhang, Zhiyong & Zhao, Xinpei, 2020. "Space-time demand cube for spatial-temporal coverage optimization model of shared bicycle system: A study using big bike GPS data," Journal of Transport Geography, Elsevier, vol. 88(C).

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