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Nonlinear Influence and Interaction Effect on the Imbalance of Metro-Oriented Dockless Bike-Sharing System

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  • Yancun Song

    (Institute of Intelligent Transportation Systems, Zhejiang University, Hangzhou 310058, China
    Zhejiang Provincial Engineering Research Center for Intelligent Transportation, Hangzhou 310058, China
    Polytechnic Institute, Zhejiang University, Hangzhou 310015, China
    These authors contributed equally to this work.)

  • Kang Luo

    (Polytechnic Institute, Zhejiang University, Hangzhou 310015, China
    These authors contributed equally to this work.)

  • Ziyi Shi

    (Institute of Intelligent Transportation Systems, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China)

  • Long Zhang

    (Institute of Intelligent Transportation Systems, Zhejiang University, Hangzhou 310058, China
    Zhejiang Provincial Engineering Research Center for Intelligent Transportation, Hangzhou 310058, China
    Polytechnic Institute, Zhejiang University, Hangzhou 310015, China)

  • Yonggang Shen

    (Zhejiang Provincial Engineering Research Center for Intelligent Transportation, Hangzhou 310058, China
    Institute of Intelligent Transportation Systems, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China)

Abstract

Dockless Bike-Sharing (DBS) is an eco-friendly, convenient, and popular form of ride-sharing. Metro-oriented DBS systems have the potential to promote sustainable transportation. However, the availability of DBS near metro stations often suffers from either scarcity or overabundance. To investigate the factors contributing to this imbalance, this paper examines the nonlinear influences and interactions that impact the DBS system near metro stations, with Shenzhen, China serving as a case study. An ensemble learning approach is employed to predict the imbalance state. Then, the machine learning interpretation method (i.e., SHapley Additive exPlanations) is used to quantify the contribution of effects, discover the strength of interactions between factors and uncover their underlying interactive connections. The results indicate the influence of external factors and the relations between pairwise variables (e.g., road density and the day of the week) for each imbalanced state. Provide two quantized sets of factors that can result in the supply-demand imbalance and support future transport planning decisions to enhance the accessibility and sustainability of Metro-oriented DBS systems.

Suggested Citation

  • Yancun Song & Kang Luo & Ziyi Shi & Long Zhang & Yonggang Shen, 2023. "Nonlinear Influence and Interaction Effect on the Imbalance of Metro-Oriented Dockless Bike-Sharing System," Sustainability, MDPI, vol. 16(1), pages 1-18, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2023:i:1:p:349-:d:1310505
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    References listed on IDEAS

    as
    1. Zhang, Yongping & Mi, Zhifu, 2018. "Environmental benefits of bike sharing: A big data-based analysis," Applied Energy, Elsevier, vol. 220(C), pages 296-301.
    2. Zhitao Li & Yuzhen Shang & Guanwei Zhao & Muzhuang Yang, 2022. "Exploring the Multiscale Relationship between the Built Environment and the Metro-Oriented Dockless Bike-Sharing Usage," IJERPH, MDPI, vol. 19(4), pages 1-21, February.
    3. Ding, Chuan & Cao, Xinyu (Jason) & Næss, Petter, 2018. "Applying gradient boosting decision trees to examine non-linear effects of the built environment on driving distance in Oslo," Transportation Research Part A: Policy and Practice, Elsevier, vol. 110(C), pages 107-117.
    4. Kim, Kyoungok, 2023. "Investigation of modal integration of bike-sharing and public transit in Seoul for the holders of 365-day passes," Journal of Transport Geography, Elsevier, vol. 106(C).
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