关键词: 城市交通, 网络脆弱性评估, 关键节点识别, 城市轨道交通网络, 复杂网络理论, 社区结构

Abstract: To scientifically assess the vulnerability of urban rail transit networks and ensure the safe, stable and efficient operation of urban transportation systems, this paper proposes a network vulnerability assessment framework that integrates the structure of traffic communities and the actual distribution of passenger flow. It makes up for the deficiency of existing methods that fail to consider the characteristics of community structure. Firstly, an improved Louvain algorithm considering enhanced transfer station influence (Transit-Enhanced Louvain, TEL) is constructed to partition the rail transit network. By introducing a transfer edge weight adjustment function, the compactness of community partitioning is dynamically optimized. Secondly, based on the community partitioning results, node community indicators centered on inter-community importance, intra-community importance, and station community importance are designed. These indicators are integrated with node passenger flow intensity to construct the Cumulative Comprehensive Importance (CCI), thereby achieving accurate identification of key nodes. Finally, empirical verification is conducted using the real dataset from the urban rail transit network in Beijing. The performance variation trend of urban rail transit network under deliberate attacks is evaluated from three aspects: network efficiency, relative connected subgraph, and passenger flow detour ratio. The results show that when the influence enhancement coefficient of transfer station is 1.4, the maximum modularity obtained by the TEL algorithm reaches 0.822 3, which is better than other baseline models. For the deliberate attack on station sequences based on the CCI indicator, the failure of the top 10% stations leads to a 78.1% decrease in the overall network efficiency, an 83.3% decrease in the relative connected subgraph, and an 86% increase in the passenger flow detour ratio. The network failure efficiency is significantly higher than that of traditional methods, verifying the effectiveness of proposed framework in this study. This study thus provides a scientifically grounded decision-making basis for identifying pivotal nodes and enhancing the resilience of urban rail transit systems.

Key words: urban transportation, network vulnerability assessment, key nodes identification, urban rail transit network, complex network theory, community structure