Optimization of train headway in moving block based on a particle swarm optimization algorithm

Rail transit plays an increasingly important role in the public transportation system, and effectively reducing its headway is of great practical significance. An optimization method is proposed to minimize headway of mainline in moving block by comprehensively considering trip time. A particle swarm optimization (PSO) algorithm is developed to search for optimized points and value of speed limit. The developed model is applied to a particular segment of metro in Shanghai. Simulation results demonstrate that, although there was a mite increase of trip time which was negligible, the method could effectively reduce headway of mainline in moving block.