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Can dynamic ride-sharing reduce traffic congestion?

Author

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  • Alisoltani, Negin
  • Leclercq, Ludovic
  • Zargayouna, Mahdi

Abstract

Can dynamic ride-sharing reduce traffic congestion? In this paper we show that the answer is yes if the trip density is high, which is usually the case in large-scale networks but not in medium-scale networks where opportunities for sharing in time and space become rather limited. When the demand density is high, the dynamic ride-sharing system can significantly improve traffic conditions, especially during peak hours. Sharing can compensate extra travel distances related to operating a mobility service. The situation is entirely different in small and medium-scale cities when trip shareability is small, even if the ride-sharing system is fully optimized based on the perfect demand prediction in the near future. The reason is simple, mobility services significantly increase the total travel distance, and sharing is simply a means of combating this trend without eliminating it when the trip density is not high enough. This paper proposes a complete framework to represent the functioning of the ride-sharing system and multiple steps to tackle the curse of dimensionality when solving the problem. We address the problem for two city scales in order to compare different trip densities. A city scale of 25 km2 with a total market of 11,235 shareable trips for the medium-scale network and a city scale of 80 km2 with 205,308 demand for service vehicles for the large-scale network over a 4-hour period with a rolling horizon of 20 minutes. The solutions are assessed using a dynamic trip-based macroscopic simulation to account for the congestion effect and dynamic travel times that may influence the optimal solution obtained with predicted travel times. This outperforms most previous studies on optimal fleet management that usually consider constant and fully deterministic travel time functions.

Suggested Citation

  • Alisoltani, Negin & Leclercq, Ludovic & Zargayouna, Mahdi, 2021. "Can dynamic ride-sharing reduce traffic congestion?," Transportation Research Part B: Methodological, Elsevier, vol. 145(C), pages 212-246.
    • Handle: RePEc:eee:transb:v:145:y:2021:i:c:p:212-246
    DOI: 10.1016/j.trb.2021.01.004
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    Citations

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    Cited by:

    1. Qizhao Peng & Weiwei Wang & Xiaoyan Yang & Yi Wang & Jian Chen, 2023. "Research on Affective Interaction in Mini Public Transport Based on IPA-FMEA," Sustainability, MDPI, vol. 15(9), pages 1-21, April.
    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. Feng, Xuan & Lin, Qinping & Jia, Ning & Tian, Junfang, 2024. "The actual impact of ride-splitting: An empirical study based on large-scale GPS data," Transport Policy, Elsevier, vol. 147(C), pages 94-112.
    4. Mayara Moraes Monteiro & Carlos M. Lima Azevedo & Maria Kamargianni & Yoram Shiftan & Ayelet Gal-Tzur & Sharon Shoshany Tavory & Constantinos Antoniou & Guido Cantelmo, 2022. "Car-Sharing Subscription Preferences: The Case of Copenhagen, Munich, and Tel Aviv-Yafo," Papers 2206.02448, arXiv.org.
    5. 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).
    6. Hao, Wu & Martin, Layla, 2022. "Prohibiting cherry-picking: Regulating vehicle sharing services who determine fleet and service structure," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    7. Zhang, Wenqing & Liu, Liangliang, 2022. "Exploring non-users' intention to adopt ride-sharing services: Taking into account increased risks due to the COVID-19 pandemic among other factors," Transportation Research Part A: Policy and Practice, Elsevier, vol. 158(C), pages 180-195.
    8. Wang, Dujuan & Wang, Qi & Yin, Yunqiang & Cheng, T.C.E., 2023. "Optimization of ride-sharing with passenger transfer via deep reinforcement learning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 172(C).
    9. Mohandu Anjaneyulu & Mohan Kubendiran, 2022. "Short-Term Traffic Congestion Prediction Using Hybrid Deep Learning Technique," Sustainability, MDPI, vol. 15(1), pages 1-18, December.
    10. Ma, Wenfei & Huang, Yunping & Jin, Xiao & Zhong, Renxin, 2024. "Functional form selection and calibration of macroscopic fundamental diagrams," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 640(C).
    11. André Palma & Lucas Javaudin & Patrick Stokkink & Léandre Tarpin-Pitre, 2024. "Ride-sharing with inflexible drivers in the Paris metropolitan area," Transportation, Springer, vol. 51(3), pages 963-986, June.
    12. Künnen, Jan-Rasmus & Strauss, Arne K., 2022. "The value of flexible flight-to-route assignments in pre-tactical air traffic management," Transportation Research Part B: Methodological, Elsevier, vol. 160(C), pages 76-96.
    13. Beojone, Caio Vitor & Geroliminis, Nikolas, 2023. "A dynamic multi-region MFD model for ride-sourcing with ridesplitting," Transportation Research Part B: Methodological, Elsevier, vol. 177(C).
    14. Seyed Omid Hasanpour Jesri & Mohsen Akbarpour Shirazi, 2022. "Bi Objective Peer-to-Peer Ridesharing Model for Balancing Passengers Time and Costs," Sustainability, MDPI, vol. 14(12), pages 1-24, June.

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