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A practical model for transfer optimization in a transit network: Model formulations and solutions

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  • Shafahi, Yousef
  • Khani, Alireza
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    Abstract

    This paper studies the transit network scheduling problem and aims to minimize the waiting time at transfer stations. First, the problem is formulated as a mixed integer programming model that gives the departure times of vehicles in lines so that passengers can transfer between lines at transfer stations with minimum waiting times. Then, the model is expanded to a second model by considering the extra stopping time of vehicles at transfer stations as a new variable set. By calculating the optimal values for these variables, transfers can be better performed. The sizes of the models, compared with the existing models, are small enough that the models can be solved for small- and medium-sized networks using regular MIP solvers, such as CPLEX. Moreover, a genetic algorithm approach is represented to more easily solve larger networks. A simple network is used to describe the models, and a medium-sized, real-life network is used to compare the proposed models with another existing model in the literature. The results demonstrate significant improvement. Finally, a large-scale, real-life network is used as a case study to evaluate the proposed models and the genetic algorithm approach.

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    Bibliographic Info

    Article provided by Elsevier in its journal Transportation Research Part A: Policy and Practice.

    Volume (Year): 44 (2010)
    Issue (Month): 6 (July)
    Pages: 377-389

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    Handle: RePEc:eee:transa:v:44:y:2010:i:6:p:377-389

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    Related research

    Keywords: Public transportation Transit scheduling Transfer coordination Genetic algorithm;

    References

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    1. Haghani, Ali & Shafahi, Yousef, 2002. "Bus maintenance systems and maintenance scheduling: model formulations and solutions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(5), pages 453-482, June.
    2. 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.
    3. Castelli, Lorenzo & Pesenti, Raffaele & Ukovich, Walter, 2004. "Scheduling multimodal transportation systems," European Journal of Operational Research, Elsevier, vol. 155(3), pages 603-615, June.
    4. Yan, Shangyao & Chen, Hao-Lei, 2002. "A scheduling model and a solution algorithm for inter-city bus carriers," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(9), pages 805-825, November.
    5. Haghani, Ali & Banihashemi, Mohamadreza & Chiang, Kun-Hung, 2003. "A comparative analysis of bus transit vehicle scheduling models," Transportation Research Part B: Methodological, Elsevier, vol. 37(4), pages 301-322, May.
    6. Zhao, Fang & Zeng, Xiaogang, 2008. "Optimization of transit route network, vehicle headways and timetables for large-scale transit networks," European Journal of Operational Research, Elsevier, vol. 186(2), pages 841-855, April.
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    Cited by:
    1. Guo, Zhan & Wilson, Nigel H.M., 2011. "Assessing the cost of transfer inconvenience in public transport systems: A case study of the London Underground," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(2), pages 91-104, February.

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