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Improving ridesplitting services using optimization procedures on a shareability network: A case study of Chengdu

Author

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  • Tu, Meiting
  • Li, Ye
  • Li, Wenxiang
  • Tu, Minchao
  • Orfila, Olivier
  • Gruyer, Dominique

Abstract

Ridesourcing services play a crucial role in metropolitan transportation systems and aggravate urban traffic congestion and air pollution. Ridesplitting is one possible way to reduce these adverse effects and improve the transport efficiency, especially during rush hours. This paper aims to explore the potential of ridesplitting during peak hours using empirical ridesourcing data provided by DiDi Chuxing, which contains complete datasets of ridesourcing orders in the city of Chengdu, China. A ridesplitting trip identification algorithm based on a shareability network is developed to quantify the potential of ridesplitting. Then, we evaluate the gap between the potential and actual scales of ridesplitting. The results show that the percentage of potential cost savings can reach 18.47% with an average delay of 4.76 min, whereas the actual percentage is 1.22% with an average delay of 9.86 min. The percentage of shared trips can be increased from 7.85% to 90.69%, and the percentage of time savings can reach 25.75% from 2.38%. This is the first investigation of the gap between the actual scale and the potential of ridesplitting on a city scale. The proposed ridesplitting algorithm can not only bring benefits on a city level but also take passenger delays into consideration. The quantitative benefits could encourage transportation management agencies and transportation network companies to develop sensible policies to improve the existing ridesplitting services.

Suggested Citation

  • Tu, Meiting & Li, Ye & Li, Wenxiang & Tu, Minchao & Orfila, Olivier & Gruyer, Dominique, 2019. "Improving ridesplitting services using optimization procedures on a shareability network: A case study of Chengdu," Technological Forecasting and Social Change, Elsevier, vol. 149(C).
    • Handle: RePEc:eee:tefoso:v:149:y:2019:i:c:s0040162519306407
    DOI: 10.1016/j.techfore.2019.119733
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    2. Zhang, Xin & Zhong, Shiquan & Jia, Ning & Ling, Shuai & Yao, Wang & Ma, Shoufeng, 2024. "A barrier to the promotion of app-based ridesplitting: Travelers’ ambiguity aversion in mode choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 181(C).
    3. Xiaomei Li & Yiwen Zhang & Zijie Yang & Yijun Zhu & Cihang Li & Wenxiang Li, 2023. "Modeling Choice Behaviors for Ridesplitting under a Carbon Credit Scheme," Sustainability, MDPI, vol. 15(16), pages 1-17, August.
    4. Markov, Iliya & Guglielmetti, Rafael & Laumanns, Marco & Fernández-Antolín, Anna & de Souza, Ravin, 2021. "Simulation-based design and analysis of on-demand mobility services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 149(C), pages 170-205.
    5. Bi, Hui & Ye, Zhirui & Zhao, Jiahui & Chen, Enhui, 2020. "Real trip costs: Modelling intangible costs of urban online car-hailing in Haikou," Transport Policy, Elsevier, vol. 96(C), pages 128-140.
    6. Gurumurthy, Krishna Murthy & Kockelman, Kara M., 2022. "Dynamic ride-sharing impacts of greater trip demand and aggregation at stops in shared autonomous vehicle systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 160(C), pages 114-125.
    7. Du, Mingyang & Li, Xuefeng & Cheng, Lin & Lu, Weike & Li, Wenxiang, 2025. "Order matching optimization of the ridesplitting service: A scenario with midway stops," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 194(C).

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