基于Dueling DQN算法的列车运行图节能优化研究
Energy Saving Optimization of Train Operation Timetable Based on a Dueling DQN Algorithm
刘飞 1唐方慧 1刘琳婷 1胡文斌 2哈进兵 2钱程2
作者信息
- 1. 苏州市轨道交通集团有限公司运营管理中心,江苏苏州 215101
- 2. 南京理工大学,南京 210014
- 折叠
摘要
通过优化地铁时刻表可有效降低地铁牵引能耗.为解决客流波动和车辆延误对实际节能率影响的问题,提出列车牵引和供电系统实时潮流计算分析模型和基于Dueling DeepQNetwork(Dueling DQN)深度强化学习算法相结合的运行图节能优化方法,建立基于区间动态客流概率统计的时刻表迭代优化模型,降低动态客流变化对节能率的影响.对预测Q网络和目标Q网络分别选取自适应时刻估计和均方根反向传播方法,提高模型收敛快速性,同时以时刻表优化前、后总运行时间不变、乘客换乘时间和等待时间最小为优化目标,实现节能时刻表无感切换.以苏州轨道交通4号线为例验证方法的有效性,节能对比试验结果表明:在到达换乘站时刻偏差不超过2 s和列车全周转运行时间不变的前提下,列车牵引节能率达5.27％,车公里能耗下降4.99％.
Abstract
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％.
关键词
城市轨道交通/时刻表优化/牵引节能/Dueling/DQN/动态客流Key words
urban rail transit/timetable optimization/traction energy saving/Dueling DQN/dynamic passenger traffic引用本文复制引用
出版年
2024
NETL
NSTL
万方数据