Calibration of disturbance parameters in railway operational simulation based on reinforcement learning

In railway operations, delays are used as one of most important factors to quantify and evaluate the quality of the railway services. However, data about stochastic disturbances and the causes of the delays are hard to be collected and measured. The efforts to manually estimate these disturbances are also considerably high. In this paper, a method for the automatic calibration of disturbance parameters, which are used to generate stochastic disturbances in simulation tools, is developed with the support of the reinforcement learning technique. Simulation and application results show that the efforts for calibrating parameters can be significantly reduced with ensured consistency between simulation models and the actual railway operations.