城市轨道交通多编组列车时刻表和车底周转综合优化研究  

Joint Optimization of Train Timetabling and Rolling Stock Circulation Planning in Urban Rail Transit Line with Multiple Train Compositions

作　　者：冉昕晨 陈坚[1] 陈绍宽[2] 刘葛辉 邹庆茹 RAN Xinchen;CHEN Jian;CHEN Shaokuan;LIU Gehui;ZOU Qingru(Chongqing Key Laboratory of Intelligent Integrated and Multidimensional Transportation System,Chongqing Jiaotong University,Chongqing 400074,China;Integrated Transport Research Center of China,Beijing Jiaotong University,Beijing 100044,China;Beijing National Railway Research&Design Institute of Signal&Communication Group Co Ltd,Beijing 100070,China)

机构地区：[1]重庆交通大学,智能综合立体交通重庆市重点实验室,重庆400074 [2]北京交通大学,中国综合交通研究中心,北京100044 [3]北京全路通信信号研究设计院集团有限公司,北京100070

出　　处：《交通运输系统工程与信息》2024年第3期184-193,共10页Journal of Transportation Systems Engineering and Information Technology

基　　金：中国博士后科学基金(2023M730430);重庆交通大学科研启动经费(2020023011);重庆市教委科学技术研究项目(KJQN202000704)。

摘　　要：为缓解城市轨道交通高峰客流拥挤,减少平峰运力浪费,本文研究多编组列车混跑场景下的列车时刻表和车底周转综合优化问题。基于动态变化的OD客流需求和多类型的行车资源,以最小化乘客等待时间和列车运行费用为优化目标,以开行列车总数、列车时刻表、编组类型、列车出入段情况和车底接续关系为决策变量,考虑时刻表、车底衔接、车底资源、折返线占用和列车能力等约束,构建城市轨道交通多编组列车时刻表和车底周转综合优化模型。根据开行列车总数可变的特点,设计变长度非支配排序遗传算法Ⅱ求解双目标的帕累托集合。以某地铁线路为例进行案例分析,结果表明:相比于单一编组方案,优化的多编组方案可同时降低26.16%和25.75%的乘客总等待时间和列车运行费用;优化后的列车平均满载率增加了1.3%~9.6%,进一步提高了运输能力和客流需求的匹配度。To address the issues of peak-hour congestions and off-peak underutilization of transportation capacity on an urban rail line,a joint optimization method of train timetabling and rolling stock circulation planning with multiple train compositions is proposed.Based on dynamically changing OD passenger demand and multiple types of line resource,a two-objective optimization model is constructed to minimize the total passenger waiting time and the train operating cost.The total number of operating trains,the timetable,the train types,the entry and exit of trains from depots,and the train succession relationship are taken as decision variables.Timetable-related constraints,rolling stock circulation-related constraints,fleet size constraints,turnaround constraints,and train capacity constraints are considered in this model.Since the total number of trains is not determined,a NSGA-II(Non-dominated Sorting Genetic Algorithm-II)with variable-length chromosomes is designed to solve for the Pareto optimal solution of the two-objective optimization model.A case study conducted on a subway line demonstrates the effectiveness of this modelling and solution approach.The results show that the optimized multi-train composition strategy simultaneously reduces the total passenger waiting time by 26.16%and the train operating costs by 25.75%.Moreover,the optimized average load factor of trains is increased by 1.3%~9.6%,further improving the matching between transportation capacity and passenger flow demand.

关 键 词：城市交通 列车时刻表 NSGA-Ⅱ 灵活编组 车底周转 

分 类 号：U121[交通运输工程]