Abstract—Reducing the waiting time imposed on thepassengerstransferring between transit lines has always been a concern for public transport schedulers, as it is a complicated problem by nature. Typically, network-wide minimization of transfer waiting time is a highly complex optimization problem, particularly in the case of dealing with huge transit networks. This problem is unlikely to be solved by exact optimization techniques. This study aims to investigate the capability of two powerful metaheuristic algorithms, genetic algorithms and simulate annealing, in coping with the transfer optimization problem. Amathematical model is presented in this study for minimizing the total transfer waiting time in transit systems. Based on this model, a genetic algorithm and a simulated annealing algorithm are developed and applied to a transit network comprising numerous transfer points. The comparative analysis of the results revealed the ability of the both algorithms in reducing the transfer waiting time although the genetic algorithm could return better results in relatively shorter computation times.
Index Terms—Transit, public transport, simulated annealing, genetic algorithms.
Vahid Poorjafari and Wen Long Yue are with the School of Natural and Built Environments, University of South Australia, Australia (e-mail:cxqvy002@mymail.unisa.edu.au, wen.yue@unisa.edu.au).
Nicholas Holyoak is with the School of Computer Science, Engineering and Mathematics, Flinders University, Australia (e-mail: nicholas.holyoak@flinders.edu.au).
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Cite: Vahid Poorjafari, Wen Long Yue, and Nicholas Holyoak, "A Comparison between Genetic Algorithms and Simulated Annealing for Minimizing Transfer Waiting Time in Transit Systems," International Journal of Engineering and Technology vol. 8, no. 3, pp. 216-221, 2016.
