In the realm of urban transportation, metro systems serve as crucial and sustainable means of public transit. However, their substantial energy consumption poses a challenge to the goal of sustainability. Disturbances such as delays and passenger flow changes can further exacerbate this issue by negatively affecting energy efficiency in metro systems. To tackle this problem, we propose a policy-based reinforcement learning approach that reschedules the metro timetable and optimizes energy efficiency in metro systems under disturbances by adjusting the dwell time and cruise speed of trains. Our experiments conducted in a simulation environment demonstrate the superiority of our method over baseline methods, achieving a traction energy consumption reduction of up to 10.9% and an increase in regenerative braking energy utilization of up to 47.9%. This study provides an effective solution to the energy-saving problem of urban rail transit.

翻译：在城市交通领域，地铁系统作为可持续公共交通的关键载体，其巨大的能源消耗对可持续发展目标构成挑战。延误和客流变化等扰动会进一步加剧这一问题，对地铁系统的能效产生负面影响。为解决该课题，本文提出一种基于策略的强化学习方法，通过调整列车停站时间与巡航速度，在扰动条件下重新调度地铁运行图并优化系统能效。仿真环境中的实验表明，该方法相较于基线方法具有显著优势，可实现牵引能耗降低最高10.9%，再生制动能量利用率提升最高47.9%。本研究为城市轨道交通的节能问题提供了有效解决方案。