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Inferring the trip purposes and uncovering spatio-temporal activity patterns from dockless shared bike dataset in Shenzhen, China

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  • Li, Shaoying
  • Zhuang, Caigang
  • Tan, Zhangzhi
  • Gao, Feng
  • Lai, Zhipeng
  • Wu, Zhifeng

Abstract

Trip purpose is closely related to travel patterns and plays an important role in urban planning and transportation management. Recently, there has been a growing interest in investigating the spatio-temporal patterns of dockless shared-bike usage and its influencing mechanisms. Few, however, have focused on revealing the travel patterns by inferring the purpose of dockless shared-bike trips at the individual level. We present a framework for inferring the purpose of dockless shared-bike users, based on gravity model and Bayesian rules, and conduct it in Shenzhen, China. We consider the comprehensive factors including distance, time, environment, activity type proportion, and service capacity of points of interest (POIs), the last two factors of which were usually neglected in previous transport studies. Especially, we integrated areas of interest (AOIs) and Tencent User density (TUD) social media data characterize the service capacity of POIs, which reflect the area and scale differences of different POI categories. Through the comparison between two improved models and the basic model, it is demonstrated that the introduction of activity type proportion and service capacity of POIs can improve the effectiveness of model for inferring the purposes of dockless shared-bike trips. Based on the obtained trip purposes, we further explore the spatio-temporal patterns of different activities and gain some insights into bike travel demand, which can inform scientific decisions for bicycle infrastructure planning and dockless shared- bike management.

Suggested Citation

  • Li, Shaoying & Zhuang, Caigang & Tan, Zhangzhi & Gao, Feng & Lai, Zhipeng & Wu, Zhifeng, 2021. "Inferring the trip purposes and uncovering spatio-temporal activity patterns from dockless shared bike dataset in Shenzhen, China," Journal of Transport Geography, Elsevier, vol. 91(C).
    • Handle: RePEc:eee:jotrge:v:91:y:2021:i:c:s0966692321000272
    DOI: 10.1016/j.jtrangeo.2021.102974
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    References listed on IDEAS

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    4. Caigang, Zhuang & Shaoying, Li & Zhangzhi, Tan & Feng, Gao & Zhifeng, Wu, 2022. "Nonlinear and threshold effects of traffic condition and built environment on dockless bike sharing at street level," Journal of Transport Geography, Elsevier, vol. 102(C).
    5. 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).
    6. Zhihan Cui & Boyu Huang & Haowen Dou & Yan Cheng & Jitian Guan & Teng Zhou, 2022. "A Two-Stage Hybrid Extreme Learning Model for Short-Term Traffic Flow Forecasting," Mathematics, MDPI, vol. 10(12), pages 1-17, June.
    7. Zhang, Ziru & Krishnakumari, Panchamy & Schulte, Frederik & van Oort, Niels, 2023. "Improving the service of E-bike sharing by demand pattern analysis: A data-driven approach," Research in Transportation Economics, Elsevier, vol. 101(C).
    8. 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).
    9. Gao, Kun & Yang, Ying & Gil, Jorge & Qu, Xiaobo, 2023. "Data-driven interpretation on interactive and nonlinear effects of the correlated built environment on shared mobility," Journal of Transport Geography, Elsevier, vol. 110(C).
    10. Cui, Xuezhu & Zhuang, Caigang & Jiao, Zhenzhi & Tan, Zhangzhi & Li, Shaoying, 2023. "How can urban built environment (BE) influence on-road (OR) carbon emissions? A road segment scale quantification based on massive vehicle trajectory big data," Journal of Transport Geography, Elsevier, vol. 111(C).

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