Nicholas Holyoak

A Comparison between Genetic Algorithms and Simulated Annealing for Minimizing Transfer Waiting Time in Transit Systems

—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.

Application of Simulated Annealing in Transit Schedule Synchronization

transport servicesis of vital importance in transit planning, as it has direct impacts on the waiting time imposed on transferring passengers. This task, which is widely recognized as schedule synchronization, is highly complicated by nature since it typically leads to a complex combinatorial optimization problem. This study aims to investigate the capability of simulated annealing algorithm in coping with this problem. A new mathematical programming model is presented for the purpose of minimizing the total transfer waiting time in transit networks. Then, a simulated annealing algorithm is developed and applied to a small-size transit network in order to test the algorithm applicability. The numerical results showed the capability of the algorithm in tackling the transit schedule synchronization problem.