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Incentive-driven transition to high ride-sharing adoption

Author

Listed:
  • David-Maximilian Storch

    (Technical University of Dresden)

  • Marc Timme

    (Technical University of Dresden
    Lakeside Labs)

  • Malte Schröder

    (Technical University of Dresden)

Abstract

Ride-sharing—the combination of multiple trips into one—may substantially contribute towards sustainable urban mobility. It is most efficient at high demand locations with many similar trip requests. However, here we reveal that people’s willingness to share rides does not follow this trend. Modeling the fundamental incentives underlying individual ride-sharing decisions, we find two opposing adoption regimes, one with constant and another one with decreasing adoption as demand increases. In the high demand limit, the transition between these regimes becomes discontinuous, switching abruptly from low to high ride-sharing adoption. Analyzing over 360 million ride requests in New York City and Chicago illustrates that both regimes coexist across the cities, consistent with our model predictions. These results suggest that even a moderate increase in the financial incentives may have a disproportionately large effect on the ride-sharing adoption of individual user groups.

Suggested Citation

  • David-Maximilian Storch & Marc Timme & Malte Schröder, 2021. "Incentive-driven transition to high ride-sharing adoption," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-23287-6
    DOI: 10.1038/s41467-021-23287-6
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    Cited by:

    1. Knierim, Lukas & Schlüter, Jan Christian, 2021. "The attitude of potentially less mobile people towards demand responsive transport in a rural area in central Germany," Journal of Transport Geography, Elsevier, vol. 96(C).
    2. Yining Liu & Yanfeng Ouyang, 2022. "Planning ride-pooling services with detour restrictions for spatially heterogeneous demand: A multi-zone queuing network approach," Papers 2208.02219, arXiv.org, revised Jun 2023.
    3. Liu, Yining & Ouyang, Yanfeng, 2023. "Planning ride-pooling services with detour restrictions for spatially heterogeneous demand: A multi-zone queuing network approach," Transportation Research Part B: Methodological, Elsevier, vol. 174(C).
    4. Siddharth Patwardhan & Marc Barthelemy & Şirag Erkol & Santo Fortunato & Filippo Radicchi, 2024. "Symmetry breaking in optimal transport networks," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    5. Lei Wang & Wenxiang Li & Jinxian Weng & Dong Zhang & Wanjing Ma, 2023. "Do low-carbon rewards incentivize people to ridesplitting? Evidence from structural analysis," Transportation, Springer, vol. 50(5), pages 2077-2109, October.
    6. 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).
    7. Suprava Chakraborty & Nallapaneni Manoj Kumar & Arunkumar Jayakumar & Santanu Kumar Dash & Devaraj Elangovan, 2021. "Selected Aspects of Sustainable Mobility Reveals Implementable Approaches and Conceivable Actions," Sustainability, MDPI, vol. 13(22), pages 1-31, November.

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