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Inter-city bus routing and timetable setting under stochastic demands

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Listed:
  • Yan, Shangyao
  • Chi, Chin-Jen
  • Tang, Ching-Hui

Abstract

Vehicle fleet routing and timetable setting are essential to the enhancement of an inter-city bus carrier's operating cost, profit, level of service and competitiveness in the market. In past research the average passenger demand has usually served as input in the production of the final fleet routes and timetables, meaning that stochastic disturbances arising from variations in daily passenger demand in actual operations are neglected. To incorporate the stochastic disturbances of daily passenger demands that occur in actual operations, in this research, we established a stochastic-demand scheduling model. We applied a simulation technique, coupled with link-based and path-based routing strategies, to develop two heuristic algorithms to solve the model. To evaluate the performance of the proposed model and the two solution algorithms, we developed an evaluation method. The test results, regarding a major Taiwan inter-city bus operation, were good, showing that the model and the solution algorithms could be useful in practice.

Suggested Citation

  • Yan, Shangyao & Chi, Chin-Jen & Tang, Ching-Hui, 2006. "Inter-city bus routing and timetable setting under stochastic demands," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(7), pages 572-586, August.
  • Handle: RePEc:eee:transa:v:40:y:2006:i:7:p:572-586
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    References listed on IDEAS

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    Cited by:

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    3. Erfan Hassannayebi & Seyed Hessameddin Zegordi & Mohammad Reza Amin-Naseri & Masoud Yaghini, 2017. "Train timetabling at rapid rail transit lines: a robust multi-objective stochastic programming approach," Operational Research, Springer, vol. 17(2), pages 435-477, July.
    4. Shangyao Yan & Ching-Hui Tang, 2008. "An Integrated Framework for Intercity Bus Scheduling Under Stochastic Bus Travel Times," Transportation Science, INFORMS, vol. 42(3), pages 318-335, August.
    5. Yingxin Liu & Xinggang Luo & Xu Wei & Yang Yu & Jiafu Tang, 2021. "Robust Optimization Model for Single Line Dynamic Bus Dispatching," Sustainability, MDPI, vol. 14(1), pages 1-18, December.
    6. Jian Li & Lu Zhang & Bu Liu & Ningning Shi & Liang Li & Haodong Yin, 2023. "Travel-Energy-Based Timetable Optimization in Urban Subway Systems," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
    7. Lo, Hong K. & An, Kun & Lin, Wei-hua, 2013. "Ferry service network design under demand uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 59(C), pages 48-70.
    8. Kang, Liujiang & Wu, Jianjun & Sun, Huijun & Zhu, Xiaoning & Gao, Ziyou, 2015. "A case study on the coordination of last trains for the Beijing subway network," Transportation Research Part B: Methodological, Elsevier, vol. 72(C), pages 112-127.
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    10. Shafahi, Yousef & Khani, Alireza, 2010. "A practical model for transfer optimization in a transit network: Model formulations and solutions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(6), pages 377-389, July.
    11. 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.
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