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Transit network design using a genetic algorithm with integrated road network and disaggregated O–D demand data

Author

Listed:
  • Pierre-Léo Bourbonnais

    (Polytechnique Montreal
    Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT))

  • Catherine Morency

    (Polytechnique Montreal
    Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT))

  • Martin Trépanier

    (Polytechnique Montreal
    Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT))

  • Éric Martel-Poliquin

    (Ministère des Transports du Québec (MTQ))

Abstract

Evolutionary algorithms have been used extensively over the past 2 decades to provide solutions to the Transit Network Design Problem and the Transit Network and Frequencies Setting Problem. Genetic algorithms in particular have been used to solve the multi-objective problem of minimizing transit users’ and operational costs. By finding better routes geometry and frequencies, evolutionary algorithms proposed more efficient networks in a timely manner. However, to the knowledge of the authors, no experimentation included precise and complete pedestrian network data for access, egress and transfer routing. Moreover, the accuracy and representativeness of the transit demand data (Origin Destination matrices) are usually generated from fictitious data or survey data with very low coverage and/or representativity. In this paper, experiments conducted with three medium-sized cities in Quebec demonstrate that performing genetic algorithm optimizations using precise local road network data and representative public transit demand data can generate plausible scenarios that are between 10 and 20% more efficient than existing networks, using the same parameters and similar fleet sizes.

Suggested Citation

  • 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.
    • Handle: RePEc:kap:transp:v:48:y:2021:i:1:d:10.1007_s11116-019-10047-1
    DOI: 10.1007/s11116-019-10047-1
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    References listed on IDEAS

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    4. Sönke Beckmann & Sebastian Trojahn & Hartmut Zadek, 2023. "Process Model for the Introduction of Automated Buses," Sustainability, MDPI, vol. 15(19), pages 1-36, September.
    5. Durán-Micco, Javier & Vansteenwegen, Pieter, 2022. "Transit network design considering link capacities," Transport Policy, Elsevier, vol. 127(C), pages 148-157.

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