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What influences people to choose ridesharing? An overview of the literature

Author

Listed:
  • Hongyun Si
  • Jiangang Shi
  • Wenwen Hua
  • Long Cheng
  • Jonas De Vos
  • Wenxiang Li

Abstract

Ridesharing is a shared mobility service in which passengers and drivers with similar origins and destinations are matched to travel in the same vehicle. This service utilises unused seats in vehicles and multi-passenger rides to reduce the cost of travel. To promote ridesharing, both service providers and policymakers should carefully analyse passenger adoption behaviour to support future decision-making and planning. In this paper, 80 studies on passenger ridesharing behaviour published since 2004 are reviewed. The motivating factors and barriers are analysed and classified in terms of demographic factors, psychological factors, and situational factors, and boundary conditions are included. The work provides a corresponding research framework on ridesharing behaviour. Finally, the current literature gaps are summarised and research recommendations are provided. This study provides a comprehensive and systematic research basis for ridesharing studies, and presents important theoretical and practical contributions to guide sustainable ridesharing behaviour.

Suggested Citation

  • Hongyun Si & Jiangang Shi & Wenwen Hua & Long Cheng & Jonas De Vos & Wenxiang Li, 2023. "What influences people to choose ridesharing? An overview of the literature," Transport Reviews, Taylor & Francis Journals, vol. 43(6), pages 1211-1236, November.
    • Handle: RePEc:taf:transr:v:43:y:2023:i:6:p:1211-1236
    DOI: 10.1080/01441647.2023.2208290
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    Citations

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    Cited by:

    1. Collins, Mor & Etzioni, Shelly & Ben-Elia, Eran, 2024. "Travel behavior and system dynamics in a simple gamified automated multimodal network," Transportation Research Part A: Policy and Practice, Elsevier, vol. 183(C).
    2. Ma, Xinwei & Tian, Xiaolin & Jin, Zejin & Cui, Hongjun & Ji, Yanjie & Cheng, Long, 2024. "Evaluation and determinants of metro users' regularity: Insights from transit one-card data," Journal of Transport Geography, Elsevier, vol. 118(C).
    3. Cheng, Long & Cai, Xinmei & Liu, Zhuo & Huang, Zhiren & Chen, Wendong & Witlox, Frank, 2024. "Characterising travel behaviour patterns of transport hub station area users using mobile phone data," Journal of Transport Geography, Elsevier, vol. 116(C).
    4. Fudong Xie & Ce Wang & Housheng Duan, 2024. "Optimizing Fleet Size in Point-to-Point Shared Demand Responsive Transportation Service: A Network Decomposition Approach," Mathematics, MDPI, vol. 12(19), pages 1-20, September.
    5. Bi, Hui & Gao, Hui & Li, Aoyong & Ye, Zhirui, 2024. "Using topic modeling to unravel the nuanced effects of built environment on bicycle-metro integrated usage," Transportation Research Part A: Policy and Practice, Elsevier, vol. 185(C).
    6. Zheyin Jin & Ye Li & Dominique Gruyer & Meiting Tu, 2024. "Enhancing the Carbon Reduction Potential in Ridesplitting through Evolutionary Game Strategies of Tripartite Stakeholders under Carbon-Inclusive Policy," Energies, MDPI, vol. 17(16), pages 1-21, August.
    7. Xiao, Lin & Wu, Jiyan & Sun, Jian & Tian, Ye, 2024. "Money is power: Carpooling stimulus with evidence from an interactive long-term laboratory experiment," Transport Policy, Elsevier, vol. 152(C), pages 55-70.

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