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

被引：0
|
作者：
Ran X. [1 ]
Chen J. [1 ]
Chen S. [2 ]
Liu G. [3 ]
Zou Q. [1 ]
机构：
[1] Chongqing Key Laboratory of Intelligent Integrated and Multidimensional Transportation System, Chongqing Jiaotong University, Chongqing
[2] Integrated Transport Research Center of China, Beijing Jiaotong University, Beijing
[3] Beijing National Railway Research & Design Institute of Signal & Communication Group Co Ltd, Beijing
来源：
Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology | 2024年 / 24卷 / 03期
基金：
中国博士后科学基金;
关键词：
flexible train composition mode; NSGA-II; rolling stock circulation; train timetable; urban traffic;
D O I：
10.16097/j.cnki.1009-6744.2024.03.018
中图分类号：
学科分类号：
摘要：
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. © 2024 Science Press. All rights reserved.
引用
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页码：184 / 193
页数：9
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