论文标题
铁路操作通过动态模拟和增强学习进行重新安排系统
Railway Operation Rescheduling System via Dynamic Simulation and Reinforcement Learning
论文作者
论文摘要
由于自然灾害的加剧，铁路服务中断的数量一直在增加。此外，社交状况（例如Covid-19-大流行）的突然变化要求铁路公司经常修改交通时间表。因此，预计会自动支持最佳计划。在这项研究中，提出了自动铁路调度系统。该系统利用强化学习和动态模拟器，可以模拟整个线路的铁路交通和乘客流量。提出的系统可以快速生成整个线路的交通时间表，因为优化过程是作为培训事先进行的。使用中断方案对系统进行评估，结果表明该系统可以在几分钟内生成整个行的优化时间表。
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.
