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，地铁系统作为关键且可持续的公共交通方式，其巨大的能源消耗对可持续发展目标构成挑战。延迟、客流变化等扰动因素会通过影响地铁系统能效进一步加剧这一问题。为解决该挑战，我们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时刻表并优化系统能效。在仿真环境中的实验表明，本方法相较于基线方法具有显著优势：牵引能耗最高降低10.9%，再生制动能量利用率最高提升47.9%。本研究为城市轨道交通节能问题提供了有效解决方案。