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Understanding the influencing factors of bicycle-sharing demand based on residents’ trips

Author

Listed:
  • Hu, Beibei
  • Zhong, Zhenfang
  • Zhang, Yanli
  • Sun, Yue
  • Jiang, Li
  • Dong, Xianlei
  • Sun, Huijun

Abstract

Bicycle-sharing is an eco-friendly transportation operating model in the context of “Internet Plus” and the sharing economy. It not only meets the short-distance travel needs of residents but has great significance for promoting the sustainable development of urban public transportation. However, a series of problems have appeared in the bicycle-sharing market, such as unreasonable resource allocation, low operating efficiency and management difficulties. Based on booking data and GPS trajectory data in Beijing of Mobike, this paper statistically analyzes the spatial and temporal distribution characteristics of residents’ bicycle-sharing trips. Then, we construct a multi-factor influence model of bicycle-sharing demand based on a negative binomial regression and variable selection model, which quantifies a series of factors that influence bicycle-sharing trips, such as population and the regional economy, building land attributes, transportation accessibility, weather, and climatic conditions, etc. The results show that, firstly, there is a spatial imbalance in the distribution of bicycle-sharing demand among different districts in Beijing. Bicycle-sharing demand is mainly concentrated in the six core districts of the city, with more than 80% of all demand. We also find that the bicycle-sharing demand has different distribution characteristics on working days and nonworking days. Compared with nonworking days, residents’ demand for bicycle-sharing on weekdays shows obvious peak periods in the morning, noon, and evening. Secondly, factors that have a major impact on the demand for bicycle-sharing include: per capita disposable income, pass facilities, parking lots etc. Among them, factors such as per capita disposable income, pass facilities, parking lots and bus/subway stations have a significant positive influence on bicycle-sharing demand. However, the number of functional zones such as airports, ports and marinas, tourist attraction and automobile sales has a significant negative influence. In addition, a comfortable temperature and good air quality encourage residents to use bicycle-sharing more for travel, while high humidity is not conducive to bicycle-sharing. We suggest that companies and related departments should jointly participate in the regulation and management of the bicycle-sharing industry, in various aspects such as bicycle scheduling, bicycle management and industry systems. In this way, cities can allocate bicycle-sharing resources reasonably and improve overall operating efficiency. The advantages of bicycle-sharing can be better used to promote the sustainable development of urban public transportation in the future.

Suggested Citation

  • Hu, Beibei & Zhong, Zhenfang & Zhang, Yanli & Sun, Yue & Jiang, Li & Dong, Xianlei & Sun, Huijun, 2022. "Understanding the influencing factors of bicycle-sharing demand based on residents’ trips," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    • Handle: RePEc:eee:phsmap:v:586:y:2022:i:c:s0378437121007457
    DOI: 10.1016/j.physa.2021.126472
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    References listed on IDEAS

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    Cited by:

    1. Dong, Xianlei & Wang, Ying & Li, Xufeng & Zhong, Zhenfang & Shen, Xinyi & Sun, Huijun & Hu, Beibei, 2023. "Understanding the influencing factors of taxi ride-sharing: A case study of Chengdu, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 176(C).
    2. Hua, Mingzhuang & Chen, Xuewu & Chen, Jingxu & Huang, Di & Cheng, Long, 2022. "Large-scale dockless bike sharing repositioning considering future usage and workload balance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
    3. Hu, Sangen & Li, Chun & Wu, Weitiao & Yang, Ying, 2025. "Exploring the determinants of demand-responsive transit acceptance in China," Transport Policy, Elsevier, vol. 165(C), pages 150-163.
    4. Li, Lili & Li, Xiaohan & Yu, Senbin & Li, Xiaojia & Dai, Jiaqi, 2022. "Unbalanced usage of free-floating bike sharing connecting with metro stations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    5. Ma, Changxi & Liu, Tao, 2024. "Demand forecasting of shared bicycles based on combined deep learning models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 635(C).
    6. Sun, Shouheng & Wang, Zhenqin & Wang, Weicai, 2023. "The impact of regulatory policy on the growth of ride-hailing platform: System dynamics perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P2).

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