为精准识别对城市道路网络运行效率具有重要影响的关键路段, 该文通过融合路网拓扑结构与交通时变特征构建基于图注意力网络的关键路段识别模型(graph attention network for key segment identification model, GAT-KSI)。首先, 基于交通时变特征, 利用图注意力网络(graph attention network, GAT)得到量化邻居路段间运行状态影响强度的特征影响力分数; 其次, 结合邻居路段拓扑结构信息, 得到量化邻居路段间交通流潜在传播能力的结构影响力分数; 然后, 融合特征影响力分数与结构影响力分数测算路网中邻居路段间“特征-结构”融合影响力。在此基础上, 利用邻居路段间融合影响力改进PageRank模型实现关键路段排序。最后, 基于深圳市路网数据构建路段级联失效“负载-容量”模型, 仿真模拟高峰与平峰2种典型交通情境下关键路段拥堵失效过程; 通过网络效率指标评估不同方法识别的关键路段拥堵失效对路网运行效率的冲击程度进而反映各方法的关键路段识别能力。结果显示, 在高峰和平峰时段, GAT-KSI模型识别结果中排名前20的路段拥堵失效时, 城市道路网络效率分别下降55.9%和50.0%; 相比之下, 度排序、介数中心性排序、传统PageRank模型、引力模型、图卷积网络模型(graph convolutional network, GCN)、图采样聚合模型(graph sample and aggregate, GraphSAGE)以及图注意力网络变体(graph attention network v2, GATv2)等基线方法在各自识别结果中排名前20的路段拥堵失效时, 网络效率下降幅度介于20.0%~43.9%, 表明GAT-KSI模型在不同交通时段均能识别出对城市道路网络运行效率具有重要影响的关键路段, 可为城市交通流量调控与应急响应等提供决策支持。

Objective: Accurate identification of road sections that critically impact the efficiency of urban road network operation is an important prerequisite for optimizing traffic resource allocation and improving road network resilience. Traditional identification methods often rely on topological attributes and ignore the time-varying characteristics of traffic states, which makes it difficult to capture key sections across different periods. Therefore, this study proposes a key link identification model that integrates structural and feature information, aiming to provide a more accurate basis for decision-making in urban traffic control and emergency management. Methods: In this study, a key road segment recognition model based on a graph attention network (GAT-KSI) is constructed. First, given the time-varying nature of traffic, GAT is used to obtain a feature influence score that quantifies the influence intensity of the running state between neighboring road sections. Second, by combining topological information from neighboring road sections, a structural influence score quantifying the potential for traffic flow to propagate between them is obtained. Then, the feature influence score and the structural influence score are combined to measure the feature-structure fusion influence between neighboring road sections in the road network. On this basis, the PageRank model is improved by using a fusion influence score between neighboring road sections to rank key road sections. Finally, based on road network data from Shenzhen, China, a load-capacity model of link cascading failure is constructed, and the congestion failure process of key links under two typical traffic situations, peak and flat peak, is simulated. The network efficiency index is used to evaluate the impact of congestion failures in key sections identified by different methods on the operation efficiency of the road network, thereby reflecting the ability of each method to identify these sections. Results: The experimental results show that the first 20 key sections identified by the GAT-KSI model can cause the road network efficiency to decrease by 55.9% during the peak period and 50.0% during the flat peak period when congestion failures occur. By contrast, the first 20 critical links identified by baseline methods such as degree ranking, betweenness centrality ranking, the traditional PageRank model, the gravity model, graph convolutional network, graph sample and aggregate, and GAT v2 only resulted in a decrease in road network efficiency of 20.0% to 43.9%. This result verifies that the GAT-KSI model can more accurately identify key road sections that have a significant impact on the operation of the road network under different traffic conditions, demonstrating stronger scene adaptability and recognition stability. Conclusions: The GAT-KSI model proposed in this study achieves accurate identification of key road sections by integrating road network topology and traffic time-varying characteristics. The model not only considers the dynamic propagation characteristics of traffic states but also accounts for the potential impact mechanisms of the road network structure, and it shows superior recognition performance across different traffic scenarios. The model can provide effective technical support for key node identification, congestion prevention, and emergency response in urban traffic management, offering important theoretical value and practical benefits.