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Robust timetable optimization for bus lines subject to resource and regulatory constraints

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  • Gkiotsalitis, K.
  • Alesiani, F.

Abstract

Timetables are typically generated based on passenger demand and travel time expectations. This work incorporates the travel time and passenger demand uncertainty to generate robust timetables that minimize the possible loss at worst-case scenarios. We solve the resulting minimax problem with a genetic algorithm that uses sequential quadratic programming to evaluate the worst-case performance of each population member. Our approach is tested on a bus line in Singapore demonstrating an improvement potential of ≃5% on service regularity and excessive trip travel times.

Suggested Citation

  • Gkiotsalitis, K. & Alesiani, F., 2019. "Robust timetable optimization for bus lines subject to resource and regulatory constraints," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 128(C), pages 30-51.
    • Handle: RePEc:eee:transe:v:128:y:2019:i:c:p:30-51
    DOI: 10.1016/j.tre.2019.05.016
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    Cited by:

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    2. Kayhan Alamatsaz & Frédéric Quesnel & Ursula Eicker, 2024. "Enhancing Electric Shuttle Bus Efficiency: A Case Study on Timetabling and Scheduling Optimization," Energies, MDPI, vol. 17(13), pages 1-26, June.
    3. 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.
    4. Gkiotsalitis, K. & Cats, O. & Liu, T. & Bult, J.M., 2023. "An exact optimization method for coordinating the arrival times of urban rail lines at a common corridor," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 178(C).
    5. Ji, Ying & Du, Jianhui & Han, Xiaoya & Wu, Xiaoqing & Huang, Ripeng & Wang, Shilei & Liu, Zhimin, 2020. "A mixed integer robust programming model for two-echelon inventory routing problem of perishable products," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 548(C).
    6. Gkiotsalitis, K. & Cats, O., 2021. "At-stop control measures in public transport: Literature review and research agenda," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    7. Meloni, Carlo & Pranzo, Marco & Samà, Marcella, 2021. "Risk of delay evaluation in real-time train scheduling with uncertain dwell times," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    8. Kayhan Alamatsaz & Sadam Hussain & Chunyan Lai & Ursula Eicker, 2022. "Electric Bus Scheduling and Timetabling, Fast Charging Infrastructure Planning, and Their Impact on the Grid: A Review," Energies, MDPI, vol. 15(21), pages 1-39, October.
    9. Yiming Bie & Mingjie Hao & Mengzhu Guo, 2021. "Optimal Electric Bus Scheduling Based on the Combination of All-Stop and Short-Turning Strategies," Sustainability, MDPI, vol. 13(4), pages 1-21, February.
    10. Gkiotsalitis, K. & Schmidt, M.E. & van der Hurk, E., 2021. "Subline frequency setting for autonomous minibusses under demand uncertainty," ERIM Report Series Research in Management ERS-2021-008-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    11. Cui, Shaohua & Gao, Kun & Yu, Bin & Ma, Zhenliang & Najafi, Arsalan, 2023. "Joint optimal vehicle and recharging scheduling for mixed bus fleets under limited chargers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 180(C).

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