Study on Train Regulation for Urban Rail Transit Based on A Hybrid Intelligent Algorithm

Abstract

When the metro train delays due to unexpected conditions in the operation of urban rail transit, it is necessary to restore the normal operation as soon as possible by train regulation. Train regulation for urban rail transit is a large-scale, complex combinatorial optimization problem, which is difficult to obtain optimal solution because of the huge search space and numerous constraints. Therefore, this paper establishes the corresponding mathematical model according to the multi-objective and multi-constraint characteristics of the optimization problem. In order to obtain faster convergence rate and better accuracy for train regulation algorithm, this paper presents a hybrid intelligent algorithm, GA-SA, which combines the advantages of genetic algorithm (GA) and simulated annealing algorithm (SA). Experiments show that compared with the traditional methods, GA-SA is suitable for the problem of train regulation in urban rail transit system and it can better ensure the normal operation of the metro train.