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Analysis of multi-modal commute behavior with feeding and competing ridesplitting services

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  • Zhu, Zheng
  • Qin, Xiaoran
  • Ke, Jintao
  • Zheng, Zhengfei
  • Yang, Hai

Abstract

Public transit is an essential travel mode in many urban areas. Emerging dynamic ridesplitting programs provided by transportation network companies (TNCs) can be a double-edged sword to public transit. On the one hand, the program provides convenient services to solve first- and last-mile problems. On the other hand, long-distance ridesplitting services may also draw passengers away from public transit. In this paper, we propose a network model to analyze multi-modal commute behavior with ridesplitting programs as both feeders and competitors to public transit, which is with limited accessibility to passengers. The ridesplitting priority and ridesplitting fare ratio (i.e., ridesplitting fare over non-ridesplitting fare) are incorporated as operational strategies of the TNC. Through numerical studies, we find that a significant number of public transit passengers will shift to long-distance ridesplitting services under low fare ratios; and a high ridesplitting priority can lead to a demand drawback for long-distance ridesplitting, which raises public transit ridership. To maintain public transit ridership, the TNC needs to keep a high fare ratio and a high priority; meanwhile, the number of short-distance ridesplitting orders can also decrease dramatically, which may lead to a loss in unit time revenue of the TNC. We note that a win–win condition can be reached through a separated discount strategy for first- and last-mile ridesplitting services. Such a strategy can both increase the number of short-distance ridesplitting orders for the TNC and boost transit ridership for the government, as well as provide low-cost services to passengers.

Suggested Citation

  • Zhu, Zheng & Qin, Xiaoran & Ke, Jintao & Zheng, Zhengfei & Yang, Hai, 2020. "Analysis of multi-modal commute behavior with feeding and competing ridesplitting services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 713-727.
    • Handle: RePEc:eee:transa:v:132:y:2020:i:c:p:713-727
    DOI: 10.1016/j.tra.2019.12.018
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    19. Haoran Chen & Xuedong Yan & Xiaobing Liu & Tao Ma, 2023. "Exploring the operational performance discrepancies between online ridesplitting and carpooling transportation modes based on DiDi data," Transportation, Springer, vol. 50(5), pages 1923-1958, October.
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    25. Fayed, Lynn & Nilsson, Gustav & Geroliminis, Nikolas, 2023. "On the utilization of dedicated bus lanes for pooled ride-hailing services," Transportation Research Part B: Methodological, Elsevier, vol. 169(C), pages 29-52.

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