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Dynamic scheduling of flexible bus services with hybrid requests and fairness: Heuristics-guided multi-agent reinforcement learning with imitation learning

Author

Listed:
  • Wu, Weitiao
  • Zhu, Yanchen
  • Liu, Ronghui

Abstract

Flexible bus is a class of demand-responsive transit that provides door-to-door service. It is gaining popularity now but also encounters many challenges, such as high dynamism, immediacy requirements, and financial sustainability. Scientific literature designs flexible bus services only for reservation demand, overlooking the potential market for immediate demand that can improve ride pooling and financial sustainability. The increasing availability of historical travel demand data provides opportunities for leveraging future demand prediction in optimizing fleet utilization. This study investigates prediction failure risk-aware dynamic scheduling flexible bus services with hybrid requests allowing for both reservation and immediate demand. Equity in request waiting time for immediate demand is emphasized as a key objective. We model this problem as a multi-objective Markov decision process to jointly optimize vehicle routing, timetable, holding control and passenger assignment. To solve this problem, we develop a novel heuristics-guided multi-agent reinforcement learning (MARL) framework entailing three salient features: 1) incorporating the demand forecasting and prediction error correction modules into the MARL framework; 2) combining the benefits of MARL, local search algorithm, and imitation learning (IL) to improve solution quality; 3) incorporating an improved strategy in action selection with time-related information about spatio-temporal relationships between vehicles and passengers to enhance training efficiency. These enhancements are general methodological contributions to the artificial intelligence and operations research communities. Numerical experiments show that our proposed method is comparable to prevailing benchmark methods both with respect to training stability and solution quality. The benefit of demand prediction is significant even when the prediction is imperfect. Our model and algorithm are applied to a real-world case study in Guangzhou, China. Managerial insights are also provided.

Suggested Citation

  • Wu, Weitiao & Zhu, Yanchen & Liu, Ronghui, 2024. "Dynamic scheduling of flexible bus services with hybrid requests and fairness: Heuristics-guided multi-agent reinforcement learning with imitation learning," Transportation Research Part B: Methodological, Elsevier, vol. 190(C).
  • Handle: RePEc:eee:transb:v:190:y:2024:i:c:s0191261524001930
    DOI: 10.1016/j.trb.2024.103069
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    References listed on IDEAS

    as
    1. Ho, Sin C. & Szeto, W.Y. & Kuo, Yong-Hong & Leung, Janny M.Y. & Petering, Matthew & Tou, Terence W.H., 2018. "A survey of dial-a-ride problems: Literature review and recent developments," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 395-421.
    2. Hai Wang, 2019. "Routing and Scheduling for a Last-Mile Transportation System," Service Science, INFORMS, vol. 53(1), pages 131-147, February.
    3. Wu, Weitiao & Li, Yu, 2024. "Pareto truck fleet sizing for bike relocation with stochastic demand: Risk-averse multi-stage approximate stochastic programming," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).
    4. Detti, Paolo & Papalini, Francesco & Lara, Garazi Zabalo Manrique de, 2017. "A multi-depot dial-a-ride problem with heterogeneous vehicles and compatibility constraints in healthcare," Omega, Elsevier, vol. 70(C), pages 1-14.
    5. Braekers, Kris & Kovacs, Attila A., 2016. "A multi-period dial-a-ride problem with driver consistency," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 355-377.
    6. Marcus Posada & Henrik Andersson & Carl H. Häll, 2017. "The integrated dial-a-ride problem with timetabled fixed route service," Public Transport, Springer, vol. 9(1), pages 217-241, July.
    7. Ying, Cheng-shuo & Chow, Andy H.F. & Nguyen, Hoa T.M. & Chin, Kwai-Sang, 2022. "Multi-agent deep reinforcement learning for adaptive coordinated metro service operations with flexible train composition," Transportation Research Part B: Methodological, Elsevier, vol. 161(C), pages 36-59.
    8. Delle Donne, Diego & Alfandari, Laurent & Archetti, Claudia & Ljubić, Ivana, 2023. "Freight-on-Transit for urban last-mile deliveries: A strategic planning approach," Transportation Research Part B: Methodological, Elsevier, vol. 169(C), pages 53-81.
    9. Chen, Peng Will & Nie, Yu Marco, 2017. "Analysis of an idealized system of demand adaptive paired-line hybrid transit," Transportation Research Part B: Methodological, Elsevier, vol. 102(C), pages 38-54.
    10. Masmoudi, Mohamed Amine & Hosny, Manar & Braekers, Kris & Dammak, Abdelaziz, 2016. "Three effective metaheuristics to solve the multi-depot multi-trip heterogeneous dial-a-ride problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 96(C), pages 60-80.
    11. Ahamed, Tanvir & Zou, Bo & Farazi, Nahid Parvez & Tulabandhula, Theja, 2021. "Deep Reinforcement Learning for Crowdsourced Urban Delivery," Transportation Research Part B: Methodological, Elsevier, vol. 152(C), pages 227-257.
    12. Diana, Marco & Dessouky, Maged M. & Xia, Nan, 2006. "A model for the fleet sizing of demand responsive transportation services with time windows," Transportation Research Part B: Methodological, Elsevier, vol. 40(8), pages 651-666, September.
    13. Lee, Enoch & Cen, Xuekai & Lo, Hong K., 2022. "Scheduling zonal-based flexible bus service under dynamic stochastic demand and Time-dependent travel time," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).
    14. Harilaos N. Psaraftis, 1980. "A Dynamic Programming Solution to the Single Vehicle Many-to-Many Immediate Request Dial-a-Ride Problem," Transportation Science, INFORMS, vol. 14(2), pages 130-154, May.
    15. Molenbruch, Yves & Braekers, Kris & Caris, An, 2017. "Benefits of horizontal cooperation in dial-a-ride services," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 107(C), pages 97-119.
    16. Cavallaro, Federico & Nocera, Silvio, 2023. "Flexible-route integrated passenger–freight transport in rural areas," Transportation Research Part A: Policy and Practice, Elsevier, vol. 169(C).
    17. Kim, Myungseob (Edward) & Schonfeld, Paul, 2015. "Maximizing net benefits for conventional and flexible bus services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 80(C), pages 116-133.
    18. Quadrifoglio, Luca & Li, Xiugang, 2009. "A methodology to derive the critical demand density for designing and operating feeder transit services," Transportation Research Part B: Methodological, Elsevier, vol. 43(10), pages 922-935, December.
    19. Kim, Myungseob (Edward) & Schonfeld, Paul, 2014. "Integration of conventional and flexible bus services with timed transfers," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 76-97.
    20. Fehn, Fabian & Engelhardt, Roman & Dandl, Florian & Bogenberger, Klaus & Busch, Fritz, 2023. "Integrating parcel deliveries into a ride-pooling service—An agent-based simulation study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 169(C).
    21. He, Dongdong & Ceder, Avishai (Avi) & Zhang, Wenyi & Guan, Wei & Qi, Geqi, 2023. "Optimization of a rural bus service integrated with e-commerce deliveries guided by a new sustainable policy in China," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 172(C).
    22. Andrew Lim & Zhenzhen Zhang & Hu Qin, 2017. "Pickup and Delivery Service with Manpower Planning in Hong Kong Public Hospitals," Transportation Science, INFORMS, vol. 51(2), pages 688-705, May.
    23. Schasché, Stephanie E. & Sposato, Robert G. & Hampl, Nina, 2022. "The dilemma of demand-responsive transport services in rural areas: Conflicting expectations and weak user acceptance," Transport Policy, Elsevier, vol. 126(C), pages 43-54.
    24. Braekers, Kris & Caris, An & Janssens, Gerrit K., 2014. "Exact and meta-heuristic approach for a general heterogeneous dial-a-ride problem with multiple depots," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 166-186.
    25. Liu, Yang & Wu, Fanyou & Lyu, Cheng & Li, Shen & Ye, Jieping & Qu, Xiaobo, 2022. "Deep dispatching: A deep reinforcement learning approach for vehicle dispatching on online ride-hailing platform," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
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