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Trip chain based usage patterns analysis of the round-trip carsharing system: A case study in Beijing

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  • Feng, Xiaoyan
  • Sun, Huijun
  • Wu, Jianjun
  • Liu, Zhiyuan
  • Lv, Ying

Abstract

In recent years, the concept of carsharing is rapidly gaining popularity in China, and the round-trip carsharing has become a common mode. However, few studies have revealed the role of round-trip carsharing in users’ travel. In this study, the round-trip GPS data provided by a carsharing company in Beijing, China is used to analyze the users’ usage patterns based on their trip chains. Through the extraction and analysis of trip information, all trip chains are grouped into three clusters, each of which has a different usage pattern. Then the consumption features and the shared car pick-up and return time of these three patterns are discussed. Further, the Bayes’ rule is used to predict the activity purpose, and the proportion and spatial distribution of different purposes are analyzed. Results reveal that the carsharing program presents multiple usage patterns to meet the different travel needs of users. Price incentives like coupons, discounts, and packages can attract more shared car trips. Users' demand for price incentives increases with longer travel distance and time. Also, users’ usage of vehicles and parking spaces has obvious peak hours. The spatial distribution of user activities has distinctly different hotspots. This paper can be beneficial for operators to set a reasonable pricing plan and provide better services.

Suggested Citation

  • Feng, Xiaoyan & Sun, Huijun & Wu, Jianjun & Liu, Zhiyuan & Lv, Ying, 2020. "Trip chain based usage patterns analysis of the round-trip carsharing system: A case study in Beijing," Transportation Research Part A: Policy and Practice, Elsevier, vol. 140(C), pages 190-203.
    • Handle: RePEc:eee:transa:v:140:y:2020:i:c:p:190-203
    DOI: 10.1016/j.tra.2020.08.017
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    References listed on IDEAS

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    6. Kim, Suji & Lee, Sujin & Ko, Eunjeong & Jang, Kitae & Yeo, Jiho, 2021. "Changes in car and bus usage amid the COVID-19 pandemic: Relationship with land use and land price," Journal of Transport Geography, Elsevier, vol. 96(C).
    7. Zhang, Si & Sun, Huijun & Liu, Yang & Lv, Ying & Wu, Jianjun & Feng, Xiaoyan, 2024. "Carsharing equitable relocation problem: A two-stage stochastic programming approach with learning-embedded endogenous uncertainty in demand," Transportation Research Part B: Methodological, Elsevier, vol. 179(C).
    8. Bitencourt, Leonardo & Dias, Bruno & Soares, Tiago & Borba, Bruno & Quirós-Tortós, Jairo, 2023. "e-Carsharing siting and sizing DLMP-based under demand uncertainty," Applied Energy, Elsevier, vol. 330(PB).
    9. Hu, Beibei & Sun, Yue & Li, Zixun & Zhang, Yanli & Sun, Huijun & Dong, Xianlei, 2024. "Competitive advantage of car-sharing based on travel costs comparison model: A case study of Beijing, China," Research in Transportation Economics, Elsevier, vol. 103(C).

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