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【背景】轨道交通作为城市交通的重要组成部分，其网络结构日益复杂，客运量也在不断攀升。保障轨道交通系统的高效运行对于维护城市的经济、社会稳定具有重要意义，然而在日常运营中频繁发生的随机扰动事件对轨道交通网的稳定性构成严峻挑战，严重干扰城市的运转秩序。【目标】系统解析轨道交通网在扰动事件中的动态响应机制，量化评估轨道交通网面对扰动事件的性能，帮助识别网络中的关键环节，为轨道交通网的资源配置及应急管理提供科学依据。【方法】基于复杂网络和弹性三角形理论，综合考虑轨道交通网拓扑结构、乘客出行合理路径、轨道交通流，提出了一种基于神经网络算法的城市轨道交通网络弹性评估方法。【数据】以北京市轨道交通网络为例，研究随机扰动下轨道交通网弹性的变化。【结论】构建的弹性评估模型能够全面评估轨道交通网在面对随机扰动时的性能维持能力，量化分析网络中关键节点和线路，突破传统静态网络分析方法局限，对保障轨道交通网的安全稳定、提升城市综合交通效能具有重要意义。
Abstract：[Background] As an important component of urban transportation, rail transit has developed rapidly in terms of its network structure to accommodate increasing passenger traffic volume.The efficient operation of rail transportation systems is vital to the sustainable development of the urban economy, society, and environment. However, the frequent random disturbances in daily operations pose a significant challenge to the stability of rail transit networks and severely interfere with urban operations. [Objective]Researchers must analyze the dynamic response mechanism of rail transit networks during disturbances, quantitatively evaluate the performance of rail transit networks during disturbance events, and identify the key network components. This provides a scientific basis for the resource allocation and emergency management of rail transit networks. [Methods]By integrating complex network theory with the resilience triangle framework, we propose a neural-network-based resilience assessment method that incorporates three dimensions, i.e., network topology,rational passenger travel paths, and origin-destination flow patterns. [Data]Using the Beijing rail transit network as a case study, we investigate resilience variations under random disturbances. [Conclusion]The results show that the proposed model can comprehensively evaluate the performance retention of the rail transit network during disturbances, highlight critical nodes and lines, and overcome the limitations of the conventional static network-analysis method. This approach contributes significantly to ensuring network-safety stability and improving urban-transportation efficiency.
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基本信息:
DOI：10.19961/j.cnki.1672-4747.2025.03.018
中图分类号:U29-39
引用信息:
[1]叶森,吕兴,李盛杰等.基于神经网络算法的城市轨道交通网弹性评估[J].交通运输工程与信息学报,2025,23(03):197-212.DOI:10.19961/j.cnki.1672-4747.2025.03.018.
基金信息:
先进轨道交通自主运行全国重点实验室开放课题资助项目（RCS2023K001）; 国家级大学生创新创业训练计划项目（202510004167）