Real-Time Scheduling Model for Shared Autonomous Vehicles in Ride-Sharing Mode
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Numerous studies have demonstrated that ride-sharing optimisation for shared autonomous vehicles (SAVs) can effectively address the efficiency limitations of conventional shared mobility systems, realising the sustainable urban transportation vision of “serving more passengers with fewer vehicles”. However, current shared autonomous vehicle (SAV) ride-sharing models exhibit inefficiencies. To overcome this, we propose a real-time SAV scheduling model with dynamic detour ride-sharing, minimising total passenger travel time while maximising ride-sharing distance ratios, thus fulfilling ride-sharing demands within defined spatial ranges. To solve this model, a two-stage ride-sharing scheduling matching approach was implemented, which integrates feasible matching pair acquisition and vehicle scheduling optimisation, with robustness and opportunism evaluated via Information Gap Decision Theory. MATLAB simulations validate the model by comparing dynamic detour ride-sharing with traditional overlapping modes. Results show the dynamic detour mode achieves a 64% ride-sharing rate using 68 vehicles, outperforming conventional modes with a 22% higher ride-sharing rate and 11 fewer vehicles required. This approach enhances ride-sharing adoption, alleviates congestion, lowers travel costs and maximises passenger service efficiency with minimal fleet size.
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Santhanakrishnan N, Emmanouil C, Constantinos A. Shared autonomous vehicle services: A comprehensive review. Transportation Research Part C: Emerging Technologies. 2020;111:255-293. DOI: 10.1016/j.trc.2019.12.008.
Rongjie Y, Ye T, Jian S. Highly automated vehicle virtual testing: A review of recent developments and research frontiers. China Journal of Highway and Transport. 2020;33(11):125-138. DOI: 10.19721/j.cnki.1001-7372.2020.11.011.
Zhuoping Y, Xingyu X, Junyi C. Review on automated vehicle testing technology and its application. Journal of Tongji University (Natural Science). 2019;47(4):540-547. DOI: 10.11908/j.issn.0253-374x.2019.04.013.
Karolemeas C, et al. Shared autonomous vehicles and agent-based models: A review of methods and impacts. Eur. Transp. Res. Rev. 2024;25:16. DOI: 10.1186/s12544-024-00644-2.
Prashanth V, Michael WL. A congestion-aware Tabu search heuristic to solve the shared autonomous vehicle routing problem. Journal of Intelligent Transportation Systems. 2021;25(4):343-355. DOI: 10.1080/15472450.2019.1665521.
Becker F, Axhausen KW. Literature review on surveys investigating the acceptance of automated vehicles. Transportation. 2017;44(6):1293-1306. DOI: 10.1007/s11116-017-9808-9.
Doina O, et al. Peer-to-Peer (P2P) carsharing and driverless vehicles: Attitudes and values of vehicle owners. Transportation Research Part A: Policy and Practice. 2021;151:180-194. DOI: 10.1016/j.tra.2021.07.008.
Paddeu D, et al. A study of users’ preferences after a brief exposure in a Shared Autonomous Vehicle (SAV). Transportation Research Procedia. 2021;52(6):533-540. DOI: 10.1016/j.trpro.2021.01.063.
Krueger R, Rashidi TH, Rose JM. Preferences for shared autonomous vehicles. Transportation Research Part C: Emerging Technologies. 2019;69:343-355. DOI: 10.1016/j.trc.2016.06.015.
Oama B, et al. Shared autonomous vehicle services and user taste variations: Survey and model applications. Transportation Research Procedia. 2020;47:3-10. DOI: 10.1016/j.trpro.2020.03.066.
Alexandra K, Jan G. Travellers’ willingness to share rides in autonomous mobility on demand systems depending on travel distance and detour. Travel Behaviour and Society. 2020;21:188-202. DOI: 10.1016/j.tbs.2020.06.010.
Pourgholamali M, et al. Sustainable deployment of autonomous vehicles dedicated lanes in urban traffic networks. Sustainable Cities and Society. 2023;99:104969. DOI: 10.1016/j.scs.2023.104969.
Panick K, et al. Autonomous taxis and ride-sharing vehicles: A social construct perspective for future mobility and infrastructure readiness. Sustainable Cities and Society. 2025;118:106060. DOI: 10.1016/j.scs.2024.106060.
Gkartzonikas C, Ke Y, Gkritza K. A tale of two modes: Who will use single user and shared autonomous vehicles. Case Studies on Transport Policy. 2022;10(3):1566-1580. DOI: 10.1016/j.cstp.2022.05.015.
Ronghan Y, et al. Empirical analysis of choice behavior for shared autonomous vehicles with concern of ride-sharing. Journal of Transportation Systems Engineering and Information Technology. 2020;20(1):228-233. DOI: 10.16097/j.cnki.1009-6744.2020.01.033.
Mohammadhossein A, et al. Unravelling interrelationships and moderators influencing the acceptance of shared autonomous vehicles: An end users’ perspective. Transportation Research Part F: Traffic Psychology and Behaviour. 2025;113:158-173. DOI: 10.1016/j.trf.2025.04.002.
Ouaïl AM, et al. Shared autonomous vehicle services and user taste variations: survey and model applications. Transportation Research Procedia. 2020;47:3-10. DOI: 10.1016/j.trpro.2020.03.066.
Zihe Z, et al. Charging infrastructure assessment for shared autonomous electric vehicles in 374 small and medium-sized urban areas: An agent-based simulation approach. Transport Policy. 2024;155:58-78. DOI: 10.1016/j.tranpol.2024.06.017.
Zihe Z, et al. Shared low-speed autonomous vehicles for short-distance trips: agent-based modeling with mode choice analysis. Transportation Planning and Technology. 2024;48(2):313-341. DOI: 10.1080/03081060.2024.2373322.
Mehdi N, Matthew JR. Agent based model for dynamic ridesharing. Transportation Research Part C: Emerging Technologies. 2016;64:117-132. DOI: 10.1016/j.trc.2015.07.016.
Mohamadhossein N, Bo Z. One-to-many matching and section-based formulation of autonomous ridesharing equilibrium. Transportation Research Part B: Methodological. 2022;155:72-100. DOI: 10.1016/j.trb.2021.11.002.
Chengqi L, et al. Multi-Agent reinforcement learning framework for addressing Demand-Supply imbalance of Shared Autonomous Electric Vehicle. Transportation Research Part E: Logistics and Transportation Review. 2025;197:104062. DOI: 10.1016/j.tre.2025.104062.
Hyland M, Mahmassani HS. Dynamic autonomous vehicle fleet operations: Optimization-based strategies to assign AVs to immediate traveller demand requests. Transportation Research Part C: Emerging Technologies. 2018;92:278-297. DOI: 10.1016/j.trc.2018.05.003.
Qian G, Ke H, Xiaobo L. Matching and routing for shared autonomous vehicles in congestible network. Transportation Research Part E: Logistics and Transportation Review. 2021;156:102513. DOI: 10.1016/j.tre.2021.102513.
Masoud N, Jayakrishnan R. A real-time algorithm to solve the peer-to-peer ride-matching problem in a flexible ridesharing system. Transportation Research Part B: Methodological. 2017;106:218-236. DOI: 10.1016/j.trb.2017.10.006.
Yantao H, Kara MK, Venu G. Shared automated vehicle fleet operations for first-mile last-mile transit connections with dynamic pooling. Computers, Environment and Urban Systems. 2022;92:101730. DOI: 10.1016/j.compenvurbsys.2021.101730.
Zhimian W, Kun A, Gonçalo C, et al. Real-time scheduling and routing of shared autonomous vehicles considering platooning in intermittent segregated lanes and priority at intersections in urban corridors. Transportation Research Part E: Logistics and Transportation Review. 2024;186:103546. DOI: 10.1016/j.tre.2024.103546.
Ben HY. Information gap decision theory. Decision Making under Deep Uncertainty. 2019:93-115. DOI: 10.1007/978-3-030-05252-2_5.
Kuhn HW. The Hungarian method for the assignment problem. Naval Research Logistics Quarterly. 1955;2(1):83-97. DOI: 10.1002/nav.3800020109.
Chopra S, et al. A distributed version of the hungarian method for multirobot assignment. IEEE Transactions on Robotics. 2017;1-16. DOI: 10.1109/TRO.2017.2693377.
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