Highlights In this paper, we study the transit routing problem and present a metaheuristics framework for solving it. We introduce two different versions of Genetic Algorithm (GA) for the problem. Our algorithms allow us to concentrate on minimizing the travel time and the number of transfers simultaneously. We show the effectiveness of our schemes, by comparing our results with previously published results on a benchmark instance. We further test our algorithms on 3 real networks based on bus route network maps for real cities.
Abstract The transit network design problem is concerned with the finding of a set of routes with corresponding schedules for a public transport system. This problem belongs to the class of NP-Hard problem because of the vast search space and multiple constraints whose optimal solution is really difficult to find out. The paper develops a Population based model for the transit network design problem. While designing the transit network, we give preference to maximize the number of satisfied passengers, to minimize the total number of transfers, and to minimize the total travel time of all served passengers. Our approach to the transit network design problem is based on the Genetic Algorithm (GA) optimization. The Genetic Algorithm is similar to evolution strategy which iterates through fitness assessment, selection and breeding, and population reassembly. In this paper, we will show two different experimental results performed on known benchmark problems. We clearly show that results obtained by Genetic Algorithm with increasing population is better than so far best technique which is really difficult for future researchers to beat.
Transit network design by genetic algorithm with elitism
2014-05-02
16 pages
Article (Journal)
Electronic Resource
English
Transit network design by genetic algorithm with elitism
Online Contents | 2014
|Bus Transit Route Network Design Using Genetic Algorithm
ASCE | 2007
|Bus Transit Route Network Design Using Genetic Algorithm
British Library Conference Proceedings | 2007
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