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

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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.

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Notes

  1. Simulations were conducted to optimize the crossover probability. Using the results from these simulations with a probability of crossover between 0.1 to 1.0, the performance of the algorithm was similar and did not significantly differ from a statistical point of view.

  2. Freeways and highways with no pedestrian access were removed in scenarios 2, 3 and 4.

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Acknowledgements

The authors would like to thank the Ministère des Transports du Québec (MTQ) for the grant that made it possible to fulfill this research. Thank you to the transit network agencies of Sherbrooke, Saguenay and Trois-Rivières for their support and for providing GTFS data and fleet information, and the cities involved for providing traffic signals data. A special thanks to Pierre Tremblay, who was director of transportation systems modelling at the MTQ at the time the research was conducted, as well as Julien Faucher for his help coding the Transition platform, and all the students and research associates that participated in the development of the Transition transit network simulation platform.

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Authors and Affiliations

  1. Department of Civil, Geological and Mining Engineering, Polytechnique Montreal, Montreal, Canada

    Pierre-Léo Bourbonnais & Catherine Morency

  2. Department of Mathematics and Industrial Engineering, Polytechnique Montreal, Montreal, Canada

    Martin Trépanier

  3. Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), Montreal, Canada

    Pierre-Léo Bourbonnais, Catherine Morency & Martin Trépanier

  4. Ministère des Transports du Québec (MTQ), Montreal, Canada

    Éric Martel-Poliquin

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  1. Pierre-Léo Bourbonnais
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  2. Catherine Morency
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Correspondence to Pierre-Léo Bourbonnais.

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Bourbonnais, PL., Morency, C., Trépanier, M. et al. Transit network design using a genetic algorithm with integrated road network and disaggregated O–D demand data. Transportation 48, 95–130 (2021). https://doi.org/10.1007/s11116-019-10047-1

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