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Genetic Algorithm for Biobjective Urban Transit Routing Problem

Author

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  • J. S. C. Chew
  • L. S. Lee
  • H. V. Seow

Abstract

This paper considers solving a biobjective urban transit routing problem with a genetic algorithm approach. The objectives are to minimize the passengers’ and operators’ costs where the quality of the route sets is evaluated by a set of parameters. The proposed algorithm employs an adding-node procedure which helps in converting an infeasible solution to a feasible solution. A simple yet effective route crossover operator is proposed by utilizing a set of feasibility criteria to reduce the possibility of producing an infeasible network. The computational results from Mandl’s benchmark problems are compared with other published results in the literature and the computational experiments show that the proposed algorithm performs better than the previous best published results in most cases.

Suggested Citation

  • J. S. C. Chew & L. S. Lee & H. V. Seow, 2013. "Genetic Algorithm for Biobjective Urban Transit Routing Problem," Journal of Applied Mathematics, Hindawi, vol. 2013, pages 1-15, December.
    • Handle: RePEc:hin:jnljam:698645
    DOI: 10.1155/2013/698645
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    Cited by:

    1. Zajac, Sandra & Huber, Sandra, 2021. "Objectives and methods in multi-objective routing problems: a survey and classification scheme," European Journal of Operational Research, Elsevier, vol. 290(1), pages 1-25.
    2. Ahmed, Leena & Mumford, Christine & Kheiri, Ahmed, 2019. "Solving urban transit route design problem using selection hyper-heuristics," European Journal of Operational Research, Elsevier, vol. 274(2), pages 545-559.
    3. Christina Iliopoulou & Konstantinos Kepaptsoglou & Eleni Vlahogianni, 2019. "Metaheuristics for the transit route network design problem: a review and comparative analysis," Public Transport, Springer, vol. 11(3), pages 487-521, October.
    4. Pierre-Léo Bourbonnais & Catherine Morency & Martin Trépanier & Éric Martel-Poliquin, 2021. "Transit network design using a genetic algorithm with integrated road network and disaggregated O–D demand data," Transportation, Springer, vol. 48(1), pages 95-130, February.
    5. Camporeale, Rosalia & Caggiani, Leonardo & Ottomanelli, Michele, 2019. "Modeling horizontal and vertical equity in the public transport design problem: A case study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 125(C), pages 184-206.
    6. Philipp Heyken Soares, 2021. "Zone-based public transport route optimisation in an urban network," Public Transport, Springer, vol. 13(1), pages 197-231, March.
    7. Xuemei Zhou & Yehan Wang & Xiangfeng Ji & Caitlin Cottrill, 2019. "Coordinated Control Strategy for Multi-Line Bus Bunching in Common Corridors," Sustainability, MDPI, vol. 11(22), pages 1-23, November.
    8. Sunhyung Yoo & Jinwoo Brian Lee & Hoon Han, 2023. "A Reinforcement Learning approach for bus network design and frequency setting optimisation," Public Transport, Springer, vol. 15(2), pages 503-534, June.
    9. Wencheng Huang & Bin Shuai & Eric Antwi, 2019. "A two-stage optimization approach for subscription bus services network design: the China case," Public Transport, Springer, vol. 11(3), pages 589-616, October.

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