Energy Saving Optimization of Train Operation Timetable Based on a Dueling DQN Algorithm
Subway traction energy consumption can be reduced by optimizing subway timetables.To solve the problem of the impact of passenger flow fluctuations and train delays on the actual energy-saving rate,this study proposes a Dueling Deep Q Network(DQN)deep reinforcement learning timetable optimization algorithm combined with a real-time subway power supply current flow calculation model.An interval iterative optimization model based on the spatiotemporal distribution of the dynamic passenger flow was established to suppress the impact of passenger flow variation.The Adaptive Moment Estimation(Adam)and root mean square propagation(RMSProp)methods were applied to predict the Q-network and target Q-network as well as improve the convergence speed of the model.While minimizing passenger transfer,waiting,and total travel times,this model allows for the seamless switching of energy-saving timetables.The test results for Suzhou Line 4 demonstrate the effectiveness of the proposed method.Under the conditions that the arrival time deviation at transfer stations was less than 2 s and the overall operating time of trains remained unchanged,the traction energy saving was 5.27％,and the train kilometer energy consumption decreased by 4.99％.
urban rail transittimetable optimizationtraction energy savingDueling DQNdynamic passenger traffic
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