Optimization method of dynamic trajectory for high-speed train group based on resilience adjustment

The dynamic operation process of high-speed train groups was investigated to enhance the autonomy and intelligence of train control, and a distributed information interaction model of high-speed train groups was constructed based on the multi-agent and graph theoretic approaches. A multiobjective optimization model was formulated to optimize the energy saving and punctuality of train groups and ensure the safety and passengers' comfort. The static optimal trajectories of train groups were determined through the differential evolution algorithm modified based on the simulated annealing. On this basis, a resilience-based dynamic interval adjustment mechanism for the train groups supported by the information exchange was specifically established for the moving block system to prevent or eliminate the train delay propagation caused by the stochastic disturbances during the operation. Moreover, an online cooperative optimization algorithm was developed to achieve the dynamic adjustment of the train group trajectories. Finally, simulations were performed based on the actual field data of the Wuhan-Guangzhou High-Speed Railway. Research results show that the proposed online cooperative optimization algorithm can effectively improve the optimal solution searching ability, and avoid excessively frequent updates of the Pareto optimal set. The average algorithm trigger times under different disturbance scenarios decreases by 36.7%. In typical disturbance scenarios, the optimized dynamic adjustment approach decreases the delay degree of the disturbed train from 6.2% to 0, and guarantees the safe and smooth operation of the train group. The optimized approach can save the energy consumption by up to 4.8% compared with the immediate delay recovery approach. Even with more significant disturbance scenarios, the delay degree of the disturbed train decreases from 13.1% to 1.4%, and the global time deviation decreases to 0 with an energy-saving rate of 1.8%. The proposed method can solve the problem that the static trajectory planning is unable to fully adapt to the change in the external dynamic environment, and effectively and timely restore the train operation despite complex disturbances. 7 tabs, 24 figs, 31 refs.