Parameters of DKZ32.

In recent decades, automatic train operation (ATO) systems have been gradually adopted by many metro systems, primarily due to their cost-effectiveness and practicality. However, a critical examination reveals computational constraints, adaptability to unforeseen conditions and multi-objective balancing that our research aims to address. In this paper, expert knowledge is combined with deep reinforcement learning algorithm (Proximal Policy Optimization, PPO) and two enhanced intelligent train operation algorithms (EITO) are proposed. The first algorithm, EITOE, is based on an expert system containing expert rules and a heuristic expert inference method. On the basis of EITOE, we propose EITOP algorithm using the PPO algorithm to optimize multiple objectives by designing reinforcement learning strategies, rewards, and value functions. We also develop the double minimal-time distribution (DMTD) calculation method in the EITO implementation to achieve longer coasting distances and further optimize the energy consumption. Compared with previous works, EITO enables the control of continuous train operation without reference to offline speed profiles and optimizes several key performance indicators online. Finally, we conducted comparative tests of the manual driving, intelligent driving algorithm (ITOR, STON), and the algorithms proposed in this paper, EITO, using real line data from the Yizhuang Line of Beijing Metro (YLBS). The test results show that the EITO outperform the current intelligent driving algorithms and manual driving in terms of energy consumption and passengers’ comfort. In addition, we further validated the robustness of EITO by selecting some complex lines with speed limits, gradients and different running times for testing on the YLBS. Overall, the EITOP algorithm has the best performance.

近几十年来，自动列车运行（Automatic Train Operation，ATO）系统凭借经济性与实用性双重优势，逐步为全球诸多地铁系统所采用。但经严谨分析可见，该类系统仍存在计算约束、对突发工况适应性不足以及多目标平衡难点，本研究正是针对上述问题开展相关工作。本文将专家知识与深度强化学习算法（Proximal Policy Optimization，PPO）相结合，提出两种增强型智能列车运行（Enhanced Intelligent Train Operation，EITO）算法。其中首款算法EITOE基于集成专家规则与启发式专家推理方法的专家系统构建。在此基础上，本文进一步提出EITOP算法，通过设计强化学习策略、奖励函数与价值函数，依托PPO算法实现多目标优化。同时，本文在EITO算法的实现流程中开发了双最小时间分配（Double Minimal-Time Distribution，DMTD）计算方法，以实现更长的列车惰行距离，进一步优化能耗表现。与既往研究相比，EITO系列算法无需依赖离线速度曲线即可实现列车连续运行控制，并可在线优化多项关键性能指标。最后，本文基于北京地铁亦庄线（Yizhuang Line of Beijing Metro，YLBS）的真实线路数据，开展了人工驾驶、智能驾驶算法（ITOR、STON）与本文提出的EITO系列算法的对比测试。测试结果显示，在能耗与乘客舒适度维度上，EITO算法的表现均优于现有智能驾驶算法与人工驾驶模式。此外，本文选取北京地铁亦庄线中部分包含限速、坡道且运行时长各异的复杂线路，进一步验证了EITO算法的鲁棒性。整体而言，EITOP算法的综合性能最优。