Transit network design using a genetic algorithm with integrated road network and disaggregated O–D demand data
Document Type
Journal Article
Publication Date
2021
Subject Area
place - north america, operations - frequency, economics - operating costs, ridership - demand, planning - network design, planning - surveys
Keywords
Transit network design, Genetic algorithm, Public transportation, Multi-objective optimization, Origin–destination, Evolutionary algorithm
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.
Rights
Permission to publish the abstract has been given by SpringerLink, copyright remains with them.
Recommended Citation
Bourbonnais, PL., Morency, C., Trépanier, M., & Martel-Poliquin, É. (2021). Transit network design using a genetic algorithm with integrated road network and disaggregated O–D demand data. Transportation, Vol. 48, pp. 95–130.
