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Optimizing Multi-Vehicle Demand-Responsive Bus Dispatching: A Real-Time Reservation-Based Approach

Author

Listed:
  • Xuemei Zhou

    (College of Transportation Engineering, Tongji University, Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, Key Laboratory of Road and Traffic Engineering of the State Ministry of Education, 4800 Caoan Highway, Shanghai 201804, China
    Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Dongnandaxue Road #2, Nanjing 211189, China)

  • Guohui Wei

    (College of Transportation Engineering, Tongji University, Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, Key Laboratory of Road and Traffic Engineering of the State Ministry of Education, 4800 Caoan Highway, Shanghai 201804, China)

  • Yunbo Zhang

    (College of Transportation Engineering, Tongji University, Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, Key Laboratory of Road and Traffic Engineering of the State Ministry of Education, 4800 Caoan Highway, Shanghai 201804, China)

  • Qianlin Wang

    (College of Transportation Engineering, Tongji University, Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, Key Laboratory of Road and Traffic Engineering of the State Ministry of Education, 4800 Caoan Highway, Shanghai 201804, China)

  • Huanwu Guo

    (College of Transportation Engineering, Tongji University, Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, Key Laboratory of Road and Traffic Engineering of the State Ministry of Education, 4800 Caoan Highway, Shanghai 201804, China)

Abstract

The demand-responsive public transport system with multi-vehicles has the potential to efficiently meet real-time and high-volume transportation needs through effective scheduling. This paper focuses on studying the real-time vehicle scheduling problem, which involves dispatching and controlling different model vehicles uniformly based on generated vehicle number tasks at a given point in time. By considering the immediacy of real-time itinerary tasks, this paper optimizes the vehicle scheduling problem at a single time point. The objective function is to minimize the total operating cost of the system while satisfying constraints such as passenger capacity and vehicle transfer time. To achieve this, a vehicle scheduling optimization model is constructed, and a solution approach is proposed by integrating bipartite graph optimal matching theory and the Kuhn–Munkres algorithm. The effectiveness of the proposed approach is demonstrated by comparing it with a traditional greedy algorithm using the same calculation example. The results show that the optimization method has higher solution efficiency and can generate a scheduling scheme that effectively reduces operating costs, improves transportation efficiency, and optimizes the operation organization process for demand-responsive buses.

Suggested Citation

  • Xuemei Zhou & Guohui Wei & Yunbo Zhang & Qianlin Wang & Huanwu Guo, 2023. "Optimizing Multi-Vehicle Demand-Responsive Bus Dispatching: A Real-Time Reservation-Based Approach," Sustainability, MDPI, vol. 15(7), pages 1-18, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:5909-:d:1110232
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    References listed on IDEAS

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    1. Daganzo, Carlos F., 1984. "Checkpoint dial-a-ride systems," Transportation Research Part B: Methodological, Elsevier, vol. 18(4-5), pages 315-327.
    2. 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.
    3. Dakic, Igor & Yang, Kaidi & Menendez, Monica & Chow, Joseph Y.J., 2021. "On the design of an optimal flexible bus dispatching system with modular bus units: Using the three-dimensional macroscopic fundamental diagram," Transportation Research Part B: Methodological, Elsevier, vol. 148(C), pages 38-59.
    4. (Edward) Kim, Myungseob & Levy, Joshua & Schonfeld, Paul, 2019. "Optimal zone sizes and headways for flexible-route bus services," Transportation Research Part B: Methodological, Elsevier, vol. 130(C), pages 67-81.
    5. Guedes, Pablo C. & Borenstein, Denis, 2018. "Real-time multi-depot vehicle type rescheduling problem," Transportation Research Part B: Methodological, Elsevier, vol. 108(C), pages 217-234.
    6. Yan, Xiang & Zhao, Xilei & Han, Yuan & Hentenryck, Pascal Van & Dillahunt, Tawanna, 2021. "Mobility-on-demand versus fixed-route transit systems: An evaluation of traveler preferences in low-income communities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 481-495.
    7. Liu, Tao & Ceder, Avishai (Avi), 2015. "Analysis of a new public-transport-service concept: Customized bus in China," Transport Policy, Elsevier, vol. 39(C), pages 63-76.
    8. Nie, Qingyun & Zhang, Lihui & Tong, Zihao & Hubacek, Klaus, 2022. "Strategies for applying carbon trading to the new energy vehicle market in China: An improved evolutionary game analysis for the bus industry," Energy, Elsevier, vol. 259(C).
    9. Sadrani, Mohammad & Tirachini, Alejandro & Antoniou, Constantinos, 2022. "Vehicle dispatching plan for minimizing passenger waiting time in a corridor with buses of different sizes: Model formulation and solution approaches," European Journal of Operational Research, Elsevier, vol. 299(1), pages 263-282.
    10. Perumal, Shyam S.G. & Lusby, Richard M. & Larsen, Jesper, 2022. "Electric bus planning & scheduling: A review of related problems and methodologies," European Journal of Operational Research, Elsevier, vol. 301(2), pages 395-413.
    11. Neeraj Saxena & Taha Rashidi & David Rey, 2020. "Determining the Market Uptake of Demand Responsive Transport Enabled Public Transport Service," Sustainability, MDPI, vol. 12(12), pages 1-18, June.
    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. Jin Zhang & Wenquan Li & Guoqing Wang & Jingcai Yu, 2021. "Feasibility Study of Transferring Shared Bicycle Users with Commuting Demand to Flex-Route Transit—A Case Study of Nanjing City, China," Sustainability, MDPI, vol. 13(11), pages 1-21, May.
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