Genetic Algorithm for Biobjective Urban Transit Routing Problem

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.

Efficient transit network design and frequencies setting multi-objective optimization by alternating objective genetic algorithm

“…Inspired on Mumford (2013) and Chew et al (2013), our problem is subject to the following constraints:…”

“…Nikolić and Teodorović (2013) developed a Swarm Intelligence model for the transit network design problem, based on the Bee Colony Optimization metaheuristics aiming to maximize the number of served passengers, to minimize the total in-vehicle time of all served passengers, and to minimize the total number of transfers in the network. Chew et al (2013) proposed an approach based on genetic algorithm to minimize the passengers' and operators' costs. The authors conducted computational experiments on Mandl's (1980) benchmark network, reporting results that outperformed previous best published results in most cases.…”

“…Fan et al (2009) and Chew et al (2013) highlighted this problem and its effect in their proposed method. With respect to demand allocation methods, most of the papers made simplified assumptions and relied on shortest path methods.…”

Planning, operation, and control of bus transport systems: A literature review

“…Numerical results on instances presented by Mandl (1979), show that the proposed heuristic is capable of obtaining more Pareto optimal solutions that using a weighted objective function. Recently, Chew et al (2013) formulate a bi-objective version of the TND that minimizes the user costs and operator costs subject to constraints such as a limited number of lines, limited number of line nodes, and a limited percentage of passengers using several transfers. To solve the proposed approach, a GA was designed and tested on a Benchmark data set.…”

Coordinated Control Strategy for Multi-Line Bus Bunching in Common Corridors

“…Bus station design [17,18] Change the number of bus stations, the layout of bus stations and other characteristics.…”