Train trajectory optimization with dynamic headway

The trajectory optimization with dynamic headway for high-speed trains plays an important role in railway operations with the increasing passenger flow. Safety, punctuality, energy saving and comfort are some of the most crucial objectives that are considered in train tracking process in this paper. Primarily, the multi-objective optimization model of tracking train trajectory planning is built under Moving Block System (MBS). Additionally, particle swarm optimization algorithm based on simulated annealing algorithm (PSO-SA) is implemented in order to find the optimal results efficiently. Considering the fact that trains cannot track the offline trajectory accurately as a consequence of the uncertain factors such as the characteristics of electrical motor, routes parameters and the environmental change, the trajectory adjustment mechanism with dynamic headway based on continuous neural network optimization algorithm (CNNO) is designed to improve the performance of the following train's operation. Finally, a simulation model is applied based on the real operational data from Chibi-Yueyang high speed railway line of China to demonstrate the effectiveness of our proposed approach.