Real-time Adjustment and optimization for Platoon Operation of High-speed Trains

Nowadays attention has been drawn to the problem of insufficient transportation capacity and knock-on delays, especially in the Spring Festival and the Summer travel rush. In this paper, the idea of flocking formation is involved for train adjustment and optimization in a train tracking situation with a moving block system. First, the memetic algorithm is implemented to seek the optimal train trajectory (driving speed curve) with the consideration of the safety, punctuality, energy saving and riding comfort. Then, the tracking interval model and platoon operation model of the high-speed trains are established. Based on the proximal trains?? information of status, speed, and driving phase, a dynamic driving phase adjustment strategy for the tracking train is designed to maintain the tracking distance. Finally, the proposed model and strategy are applied to Chibi North-Changsha South high-speed railway comdor by adopting the real-world operation data and the temporary speed restriction. Simulation results show that, by adopting real-time adjustment and optimization strategy and train platoon model, interactions between the proximal trains can be reduced, thereby reducing the energy consumption by 5.19%, increasing the rail line space utilization of 55s. Furthermore, this has the effect of restricting the knock-on delay to a certain extent.