首页|面向客流聚集风险防控的城轨列车实时调度模型与算法

面向客流聚集风险防控的城轨列车实时调度模型与算法

扫码查看
原文链接
  • 万方数据
  • 维普
"后疫情时代"下,我国城市轨道交通客流量快速反弹并进一步持续攀升.在此背景下,以缓解车站拥挤度为目标,研究面向客流聚集风险防控的列车实时调度问题具有重要的现实意义.在线路运营受到异常事件干扰条件下,结合列车跳停策略和运行图协同调整,以最小化线路车站拥挤度为目标函数,以车厢满载率为模型约束,建立列车实时调度混合整数线性规划模型.为提高模型求解效率,提出可变邻域搜索算法,首先基于线性规划松弛原理设计模型初始解的启发式计算规则,之后基于可变邻域搜索算法寻找初始解邻域内的近似最优解作为列车实时调度问题的最终解.使用北京地铁亦庄线实际数据进行了仿真实验,仿真结果表明:以标准"站站停"策略生成的运行图调整方案作为评价基准,采用可变邻域搜索算法计算得到的列车实时调度策略可降低线路拥挤度约67.56%,减少约38.28%的线路最大断面客流量,计算时间在1 min左右,可满足列车实时调度的需求,验证了本文提出的列车实时调度模型与求解算法能有效降低车站拥挤度、均衡线路客流的断面分布,对突发大客流带来的车站乘客聚集问题具有较好的调整效果.
Real-time train rescheduling of urban rail transit to optimize safety risks of over-congestions
In the post-pandemic era,the passenger volume of urban rail transit has rebounded rapidly and continues to increase.To address the problem of unexpectedly heavy passenger flows caused by train delays in urban rail transit,we propose a mathematical model and algorithm for real-time train rescheduling with station and carriage-congestion control.Based on the condition that the line opera-tion is disrupted by abnormal disturbances,we propose a mixed-integer linear programming model,that minimizes line(station)congestion and considers carriage congestion as a model constraint for the real-time train rescheduling problem to jointly optimize the train stop-skipping strategy and train timetable.To improve the computational efficiency,we introduce a variable neighborhood search(VNS)algorithm to solve our model.First,based on the relaxation principle of linear program-ming,we design a heuristic rule to generate the initial solution of the model.Subsequently,we use the VNS algorithm to iteratively search for the near-optimal solution,which is also the final solution of our mathematical model,in the neighborhood of the initial solution.A numerical experiment based on the operation data of the Yizhuang line of the Beijing Metro is performed.The simulation results indicate that,compared with the benchmark,i.e.,the train timetable under the standard non-skip strat-egy(the train stops at every station),the final solution of our model reduces the line congestion and maximum section passenger flow by 67.56%and 38.28%,respectively,within approximately 1 min of computing time,thus satisfying the real-time requirement.The experiment results verify that our model and proposed algorithm can reduce station congestion and balance the section distribution of passenger flow;hence,they are advantageous for solving the problem of abrupt heavy passenger flows caused by train delays to control the spread of epidemics.

urban rail transitreal-time train reschedulingpassenger flow optimizationtrain stop-skippingmixed-integer linear programmingvariable neighborhood search

陈星、阴佳腾、高原、蒲凡、杨立兴

展开 >

南昌轨道交通集团有限公司,南昌 330220

北京交通大学,系统科学学院,北京 100044

北京交通大学,先进轨道交通自主运行全国重点实验室,北京 100044

北京理工大学,管理与经济学院,北京 100081

展开 >

城市轨道交通 列车实时调度 客流优化 列车跳停 混合整数线性规划 可变邻域搜索

国家自然科学基金优秀青年科学基金国家自然科学基金基础科学中心项目

7232202272288101

2024

10.19961/j.cnki.1672-4747.2023.11.005

交通运输工程与信息学报
西南交通大学

交通运输工程与信息学报

CSTPCD
影响因子：0.446
ISSN：1672-4747
年,卷(期)：2024.22(2)
  • 8