  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.
