High-Speed Train Platoon Dynamic Interval Optimization Based on Resilience Adjustment Strategy

Abstract

Resilience adjustment refers to the generation of a control strategy by evaluating the interaction between related factors. Tracking intervals of the high-speed train platoon change dynamically, which directly influences the operation safety and efficiency, and constrains the train operation trajectories. In China, the tracking interval is getting shorter. To ensure safety and improve efficiency, we research a dynamic interval resilience adjustment strategy based on the moving block system. Firstly, the optimal offline operation strategy is obtained by solving the multi-objective optimization model with the improved gravitational search algorithm (I-GSA). The resilience adjustment mechanism is developed to evaluate the tracking interval and choose the appropriate driving strategy to adjust operation states based on the resilience tracking interval model. Then, we study the relation between operation strategy and departure interval, and a seeker optimization algorithm (SOA) is used to obtain the optimal departure intervals and driving strategies. Simulations are conducted based on the sections between Chibi North station and Changsha South station in Wuhan-Guangzhou high-speed railway. The results indicate that the total operation time decreased by 191s and the operation safety can be ensured at any time.