基于鲁棒性的换乘模型下多制式列车运行图协同优化研究

Research on Collaborative Optimization of Multi-Standard Train Operation Chart under Robustness Transfer Model

摘要: 在高密度行车条件下，城市轨道交通系统极易受运行干扰影响，尤其在衔接高速铁路的换乘枢纽站，瞬时大规模换乘客流常导致列车停站时间延长并引发晚点传播，本文提出一种计划层面的协同优化方法，通过在列车运行图编制阶段动态调整各区间缓冲时间，以提升运行图鲁棒性。模型充分考虑列车载客能力约束，基于历史客流数据，结合候车乘客与到站列车之间的动态交互关系，精确刻画实际停站时间，建立以最小化城市轨道交通列车运行图扰动时间和乘客换乘等待时间为目标的多制式换乘列车运行图协同优化模型。运用改进的遗传算法对该优化模型进行求解；仿真实验结果表明，模型与算法能够有效平衡运行稳定性与换乘服务水平，显著提升网络在面对外部客流冲击时的韧性与可靠性。

Abstract: Under the condition of high-density driving, the urban rail transit system is very susceptible to operation interference, especially at the transfer hub station connecting the high-speed railway, the instantaneous large-scale transfer of passengers often leads to the prolongation of train stoppage time and causes delay propagation. This paper proposes a planning-level collaborative optimization method, which dynamically adjusts the buffer time of each section during the train timetable compilation stage to enhance the robustness of the timetable. The model fully considers train passenger capacity constraints and is based on historical passenger flow data and the dynamic interaction between waiting passengers and arriving trains, the model accurately depicts the actual stopping time, and establishes a multi-standard transfer train operation chart collaborative optimization model with the goal of minimizing the disturbance time of urban rail transit train operation chart and passenger transfer waiting time. The improved genetic algorithm is used to solve the optimization model. Simulation results show that the model and algorithm can effectively balance the operation stability and transfer service level, and significantly improve the resilience and reliability of the network in the face of external passenger flow impacts.