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A Sustainable Dynamic Capacity Estimation Method Based on Bike-Sharing E-Fences

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  • Chen Deng

    (School of Art & Design, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China)

  • Houqiang Ma

    (Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, Beijing 100021, China)

Abstract

Increasing urban traffic congestion and environmental pollution have led to the embrace of bike-sharing for its low-carbon convenience. This study enhances the operational efficiency and environmental benefits of bike-sharing systems by optimizing electronic fences (e-fences). Using bike-sharing order data from Shenzhen, China, a data-driven multi-objective optimization approach is proposed to design the sustainable dynamic capacity of e-fences. A dynamic planning model, solved with an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II), adjusts e-fence capacities to match fluctuating user demand, optimizing resource utilization. The results show that an initial placement of 20 bicycles per e-fence provided a balance between cost efficiency and user convenience, with the enterprise cost being approximately 76,000 CNY and an extra walking distance for users of 15.1 m. The optimal number of e-fence sites was determined to be 40 based on the solution algorithm constructed in the study. These sites are strategically located in high-demand areas, such as residential zones, commercial districts, educational institutions, subway stations, and parks. This strategic placement enhances urban mobility and reduces disorderly parking.

Suggested Citation

  • Chen Deng & Houqiang Ma, 2024. "A Sustainable Dynamic Capacity Estimation Method Based on Bike-Sharing E-Fences," Sustainability, MDPI, vol. 16(14), pages 1-17, July.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:14:p:6210-:d:1439221
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    References listed on IDEAS

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    1. Legros, Benjamin, 2019. "Dynamic repositioning strategy in a bike-sharing system; how to prioritize and how to rebalance a bike station," European Journal of Operational Research, Elsevier, vol. 272(2), pages 740-753.
    2. Yu, Qing & Xie, Yingkun & Li, Weifeng & Zhang, Haoran & Liu, Xiaolei & Shang, Wen-Long & Chen, Jinyu & Yang, Dongyuan & Yan, Jinyue, 2022. "GPS data in urban bicycle-sharing: Dynamic electric fence planning with assessment of resource-saving and potential energy consumption increasement," Applied Energy, Elsevier, vol. 322(C).
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    Cited by:

    1. Jiaxin Liang & Bo Zhang & Changhai Chen & Haoyu Cui & Yongcai Ma & Bin Chen, 2025. "The Establishment of a Geofencing Model for Automated Data Collection in Soybean Trial Plots," Agriculture, MDPI, vol. 15(20), pages 1-23, October.
    2. Chen Deng & Yunxuan Li, 2025. "Dynamic Demand Forecasting for Bike-Sharing E-Fences Using a Hybrid Deep Learning Framework with Spatio-Temporal Attention," Sustainability, MDPI, vol. 17(17), pages 1-21, August.

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