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Understanding bike sharing travel patterns: An analysis of trip data from eight cities

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  • Kou, Zhaoyu
  • Cai, Hua

Abstract

As a new mobility option, bike sharing is gaining popularity around the world. Understanding the travel patterns of bike sharing trips can provide fundamental basis for researchers to model the use of bike sharing and the associated multi-modal transportation systems, inform bike sharing system design and operation, and guide policy decisions for sustainable transportation development. Using bike sharing trip data from eight cities in the United States, we analyzed the distributions of trip distance and trip duration for bike sharing trips for commuting and touristic purposes. Our results show that both the trip distance and duration follows a lognormal distribution in larger bike sharing systems (e.g., in Boston, Washington DC, Chicago, and New York), while the distribution for smaller systems varies among Weibull, gamma, and lognormal because the systems’ geographical boundary restricts the movement of users. Our analysis of the long trips also show that the trip distance and duration also displays a power law decay in the larger systems.

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  • Kou, Zhaoyu & Cai, Hua, 2019. "Understanding bike sharing travel patterns: An analysis of trip data from eight cities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 785-797.
    • Handle: RePEc:eee:phsmap:v:515:y:2019:i:c:p:785-797
    DOI: 10.1016/j.physa.2018.09.123
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    Cited by:

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    4. Todd, James & O'Brien, Oliver & Cheshire, James, 2021. "A global comparison of bicycle sharing systems," Journal of Transport Geography, Elsevier, vol. 94(C).
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    7. Ross-Perez, Antonio & Walton, Neil & Pinto, Nuno, 2022. "Identifying trip purpose from a dockless bike-sharing system in Manchester," Journal of Transport Geography, Elsevier, vol. 99(C).
    8. Chengming Li & Zhaoxin Dai & Weixiang Peng & Jianming Shen, 2019. "Green Travel Mode: Trajectory Data Cleansing Method for Shared Electric Bicycles," Sustainability, MDPI, vol. 11(5), pages 1-14, March.
    9. Michel Noussan & Giovanni Carioni & Francesco Davide Sanvito & Emanuela Colombo, 2019. "Urban Mobility Demand Profiles: Time Series for Cars and Bike-Sharing Use as a Resource for Transport and Energy Modeling," Data, MDPI, vol. 4(3), pages 1-12, July.
    10. Andrea Bardi & Luca Mantecchini & Denis Grasso & Filippo Paganelli & Caterina Malandri, 2019. "Flexible Mobile Hub for E-Bike Sharing and Cruise Tourism: A Case Study," Sustainability, MDPI, vol. 11(19), pages 1-15, October.
    11. 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.
    12. Xavier Bach & Carme Miralles-Guasch & Oriol Marquet, 2023. "Spatial Inequalities in Access to Micromobility Services: An Analysis of Moped-Style Scooter Sharing Systems in Barcelona," Sustainability, MDPI, vol. 15(3), pages 1-19, January.
    13. Xin, Rui & Yang, Jian & Ai, Bo & Ding, Linfang & Li, Tingting & Zhu, Ruoxin, 2023. "Spatiotemporal analysis of bike mobility chain: A new perspective on mobility pattern discovery in urban bike-sharing system," Journal of Transport Geography, Elsevier, vol. 109(C).
    14. 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).
    15. Fan Yang & Fan Ding & Xu Qu & Bin Ran, 2019. "Estimating Urban Shared-Bike Trips with Location-Based Social Networking Data," Sustainability, MDPI, vol. 11(11), pages 1-14, June.
    16. Bi, Hui & Li, Aoyong & Hua, Mingzhuang & Zhu, He & Ye, Zhirui, 2022. "Examining the varying influences of built environment on bike-sharing commuting: Empirical evidence from Shanghai," Transport Policy, Elsevier, vol. 129(C), pages 51-65.
    17. Mingyang Du & Lin Cheng & Xuefeng Li & Jingzong Yang, 2019. "Investigating the Influential Factors of Shared Travel Behavior: Comparison between App-Based Third Taxi Service and Free-Floating Bike Sharing in Nanjing, China," Sustainability, MDPI, vol. 11(16), pages 1-18, August.
    18. Fu, Chenyi & Ma, Shoufeng & Zhu, Ning & He, Qiao-Chu & Yang, Hai, 2022. "Bike-sharing inventory management for market expansion," Transportation Research Part B: Methodological, Elsevier, vol. 162(C), pages 28-54.
    19. Xia, Dawen & Jiang, Shunying & Yang, Nan & Hu, Yang & Li, Yantao & Li, Huaqing & Wang, Lin, 2021. "Discovering spatiotemporal characteristics of passenger travel with mobile trajectory big data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    20. Deng, Yue & Wang, Jiaxin & Gao, Chao & Li, Xianghua & Wang, Zhen & Li, Xuelong, 2021. "Assessing temporal–spatial characteristics of urban travel behaviors from multiday smart-card data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 576(C).
    21. Wu, Chunliang & Kim, Inhi, 2020. "Analyzing the structural properties of bike-sharing networks: Evidence from the United States, Canada, and China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 140(C), pages 52-71.
    22. Smith, C. Scott & Schwieterman, Joseph P., 2021. "Using multivariate adaptive regression splining (MARS) to identify factors affecting the performance of dock-based bikesharing: The case of Chicago’s Divvy system," Research in Transportation Economics, Elsevier, vol. 89(C).
    23. Quan-Lin Li & Rui-Na Fan, 2022. "A mean-field matrix-analytic method for bike sharing systems under Markovian environment," Annals of Operations Research, Springer, vol. 309(2), pages 517-551, February.
    24. Tomasz Bieliński & Agnieszka Kwapisz & Agnieszka Ważna, 2019. "Bike-Sharing Systems in Poland," Sustainability, MDPI, vol. 11(9), pages 1-14, April.

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