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Spatiotemporal Characteristics and Factors Influencing the Cycling Behavior of Shared Electric Bike Use in Urban Plateau Regions

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  • Miqi Guo

    (School of Earth Sciences, Yunnan University, Kunming 650500, China
    Yunnan International Joint Laboratory of Critical Mineral Resource, Kunming 650500, China
    These authors contributed equally to this work.)

  • Chaodong Gou

    (School of Earth Sciences, Yunnan University, Kunming 650500, China
    Yunnan International Joint Laboratory of Critical Mineral Resource, Kunming 650500, China
    These authors contributed equally to this work.)

  • Shucheng Tan

    (School of Earth Sciences, Yunnan University, Kunming 650500, China
    Yunnan International Joint Laboratory of Critical Mineral Resource, Kunming 650500, China)

  • Churan Feng

    (School of Earth Sciences, Yunnan University, Kunming 650500, China
    Yunnan International Joint Laboratory of Critical Mineral Resource, Kunming 650500, China)

  • Fei Zhao

    (School of Earth Sciences, Yunnan University, Kunming 650500, China)

Abstract

At present, most of the research on shared electric bikes mostly focuses on the scheduling, operation and maintenance of shared electric bikes, while insufficient attention has been paid to the behavioral characteristics and influencing factors of shared cycling in plateau cities. This paper takes Kunming as a research case. According to the user’s cycling behavior, the spatiotemporal cube model and emerging hotspot analysis are used to explore the spatiotemporal characteristics of the citizens’ cycling in the plateau city represented by Kunming, and the method of geographical detectors is used to study the specific factors affecting the shared travel of citizens in Kunming and conduct interactive detection. The findings are as follows: ① the use of shared electric bikes in Kunming varies greatly on weekdays, showing a bimodal feature. In space, the overall distribution of cycling presents a “multi-center” agglomeration feature with distance decay from the center of the main urban area. ② The geographic detector factor detection model quantitatively analyzes the interactive influence between factors, providing a good supplement to the independent influence results of each factor. Through the dual factor interactive detection model, we found that the overall spatiotemporal distribution of cycling during each time period is most significantly affected by the distribution of service facilities, followed by transportation accessibility, land use, and the natural environment. The research results can assist relevant departments in governance of urban shared transportation and provide a reference basis, and they also have certain reference value in urban pattern planning.

Suggested Citation

  • Miqi Guo & Chaodong Gou & Shucheng Tan & Churan Feng & Fei Zhao, 2024. "Spatiotemporal Characteristics and Factors Influencing the Cycling Behavior of Shared Electric Bike Use in Urban Plateau Regions," Sustainability, MDPI, vol. 16(15), pages 1-18, July.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:15:p:6570-:d:1447275
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    References listed on IDEAS

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    1. Duan, Yimeng & Zhang, Shen & Yu, Zhuoran, 2021. "Applying Bayesian spatio-temporal models to demand analysis of shared bicycle," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    2. Foschi, Rachele, 2023. "A Point Processes approach to bicycle sharing systems’ design and management," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    3. Yan Pan & Yanzhe Li & Shouzhen Zeng & Junfang Hu & Kifayat Ullah, 2022. "Green Recycling Supplier Selection of Shared Bicycles: Interval-Valued Pythagorean Fuzzy Hybrid Weighted Methods Based on Self-Confidence Level," IJERPH, MDPI, vol. 19(9), pages 1-21, April.
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