  The number of railway service disruptions has been increasing owing to
intensification of natural disasters. In addition, abrupt changes in social
situations such as the COVID-19 pandemic require railway companies to modify
the traffic schedule frequently. Therefore, automatic support for optimal
scheduling is anticipated. In this study, an automatic railway scheduling
system is presented. The system leverages reinforcement learning and a dynamic
simulator that can simulate the railway traffic and passenger flow of a whole
line. The proposed system enables rapid generation of the traffic schedule of a
whole line because the optimization process is conducted in advance as the
training. The system is evaluated using an interruption scenario, and the
results demonstrate that the system can generate optimized schedules of the
whole line in a few minutes.
