华南理工大学学报(自然科学版) ›› 2025, Vol. 53 ›› Issue (12): 61-70.doi: 10.12141/j.issn.1000-565X.250107

• 智慧交通系统 • 上一篇    下一篇

城市轨道交通客流廊道跨线列车开行方案的优化

许奇1, 庞立言2, 薛力恺2, 李洁珲3, 贺鹏4   

  1. 1.北京交通大学 综合交通运输大数据应用技术交通运输行业重点实验室,北京 100044
    2.北京交通大学 交通运输学院,北京 100044
    3.北京市地铁运营有限公司运营三分公司,北京 100044
    4.北京城建设计发展集团股份有限公司,北京 100037
  • 收稿日期:2025-04-15 出版日期:2025-12-25 发布日期:2025-07-18
  • 作者简介:许奇(1982—),男,博士,副教授,主要从事城市轨道交通列车运行组织优化研究。E-mail: xuqi@bjtu.edu.cn
  • 基金资助:
    国家自然科学基金项目(72471024)

Optimization of Cross-Line Train Operation Scheme for Passenger Flow Corridors in Urban Rail Transit

XU Qi1, PANG Liyan2, XUE Likai2, LI Jiehui3, HE Peng4   

  1. 1.Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport,Beijing Jiaotong University,Beijing 100044,China
    2.School of Traffic and Transportation,Beijing Jiaotong University,Beijing 100044,China
    3.The Third Operation Branch Company Affiliated with Beijing Mass Transit Railway Operation Co. ,Ltd. ,Beijing 100044,China
    4.Beijing Urban Construction Design & Development Group,Beijing 100037,China
  • Received:2025-04-15 Online:2025-12-25 Published:2025-07-18
  • About author:许奇(1982—),男,博士,副教授,主要从事城市轨道交通列车运行组织优化研究。E-mail: xuqi@bjtu.edu.cn
  • Supported by:
    the National Natural Science Foundation of China(72471024)

摘要:

城市轨道交通客流廊道区域连接城市核心区和不同城市圈层的主要功能区,是城市空间资源和经济活动集中分布的区域,其运输效率对轨道交通网络能力及社会经济活动具有重要影响。因此,针对城市轨道交通客流廊道区域,基于“一干多支”的客流廊道拓扑结构,提出以企业运营成本、乘客出行成本、满载率不均衡系数为目标的多目标非线性优化模型。模型以发车频率和编组方案为决策变量,并设计NSGA-Ⅱ算法进行求解。以北京市回龙观/天通苑—中关村客流廊道为例,验证模型的有效性,结果表明,该模型可有效匹配职住组团间的多方向出行需求。为验证模型的优化效果,提出改进的基于距离的最优解求解方法,求得最优解,乘客出行总时间、企业运营成本和满载率不均衡系数分别为2 639.17 h、103 716.24元、0.086;相较分线独立运营方案,乘客出行时间降低14.09%,满载率不均衡系数降低43.01%,企业运营成本增加17.22%;同时,在复杂线网中,干线区段的最大通过能力和分支区段的最小服务水平的限制对优化方案的效果造成了较大影响。通过提高干线能力上限,研究能力限制对客流廊道列车开行方案优化的影响,结果显示,在提高线路通过能力的条件下,帕累托解的多样性增加,乘客出行时间显著降低,服务的均衡性提高。研究结果为进一步优化跨线开行方案和实际运营提供了相应的经验指导。

关键词: 城市轨道交通, 客流廊道, 跨线运营, 列车开行方案, 多目标非线性优化

Abstract:

Urban rail transit passenger flow corridor connect core urban areas with key functional zones across different urban spatial layers, representing concentrated areas of urban spatial resources and economic activities. Their transportation efficiency significantly impacts both the capacity of the rail transit network and socio-economic activities. Focusing on these corridors, this study adopts a “trunk line with multiple branches” topological structure to develop a multi-objective nonlinear optimization model that minimizes enterprise operational costs, passenger travel costs, and load factor imbalance. The model uses train frequency and marshaling plans as decision variables as decision variables, solved via an NSGA-Ⅱ algorithm. Using Beijing’s Huilongguan/Tiantongyuan-Zhongguancun corridor as a case study, the model demonstrates effective accommodation of multi-directional travel demand between residential and employment hubs. To evaluate optimization performance, an improved distance-based optimal solution selection method identifies a solution with 2 639.17 h (passenger travel time), 103 716.24 yuan (operational cost), and 0.086 (load factor imbalance). Compared to isolated line operation schemes, this achieves 14.09% reduction in passenger travel time and 43.01% lower load factor imbalance, with a 17.22% increase in operational costs. At the same time, in complex line networks, the restrictions of the maximum carrying capacity of the main line section and the minimum service level of the branch section have a great impact on the effect of the optimization scheme. By increasing the upper limit of the main line capacity, the study investigates the impact of capacity restrictions on the optimization of train operation schemes in passenger flow corridors. The results show that under the condition of improving the line carrying capacity, the diversity of Pareto solutions increases, passenger travel time is significantly reduced, and the balance of service is improved. These findings provide empirical guidance for cross-line operation planning and practical rail transit management.

Key words: urban rail transit, passenger flow corridor, cross-line operation, train operation scheme, multi-objective nonlinear optimization

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