A Resilience Adjustment Method for Real-time Cooperative Optimization of High-speed Trains

The high-speed railway is a complex system composed of human, train and operation environment. Traditional train driving strategy optimization calculated offline based on scheduled timetable does not recover train delays due to external and internal perturbations in daily operations. In this paper, the real-time adjustment of optimal driving strategies for high-speed train group cooperative operation is investigated. Firstly, a distributed information interaction model based on multi-agent and graph theory is established to realize real-time information sharing among all train agents. Then, a set of resilience is denoted as a standard to trigger the integrated real-time optimization algorithm. With respect to the adverse influence of unanticipated perturbations, resilience adjustment is applied to ensure safety, energy-saving, punctuality and passenger comfort for the distributed cooperative operation of high-speed train group in real time. Finally, the effectiveness of proposed methods is verified by simulation using real data from a Chinese high-speed line.