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.

翻译：由于自然灾害加剧,铁路服务中断的次数不断增加,此外,COVID-19大流行等社会状况的突然变化要求铁路公司经常修改交通进度表,因此,预计会自动支持最佳日程安排。在本研究中,提出了铁路自动日程安排系统。该系统利用强化学习和动态模拟器模拟铁路交通和全线客运流动。拟议的系统能够迅速生成整个线路的交通进度表,因为优化过程是作为培训提前进行的。该系统是利用中断情况来评估的,结果显示该系统可以在几分钟内产生整个线路的最佳日程安排。