College of Transportation Engineering, Chang'an University, Xi'an, Shaanxi, China.
College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China.
PLoS One. 2022 Mar 14;17(3):e0265260. doi: 10.1371/journal.pone.0265260. eCollection 2022.
Freeway networks are vulnerable to natural disasters and man-made disruptions. The closure of one or more toll stations of the network often causes a sharp decrease in freeway performance. Therefore, measuring the probability and consequences of vulnerability to identify critical parts in the network is crucial for road emergency management. Most existing techniques only measure the consequences of node closure and rarely consider the probability of node closure owing to the lack of an extensive historical database; moreover, they ignore highways outside the study area, which can lead to errors in topological analysis and traffic distribution. Furthermore, the negative effects produced by the operation of freeway tunnels in vulnerability assessment have been neglected. In this study, a framework for freeway vulnerability assessment that considers both the probability and consequences of vulnerability is proposed, based on the perspective of network cascade failure analysis. The cascade failure analysis is conducted using an improved coupled map lattice model, developed by considering the negative effects of tunnels and optimizing the rules of local traffic redistribution. The perturbation threshold and propagation time step of network cascade failure are captured to reflect the probabilities and consequences of vulnerability. A nodal vulnerability index is established based on risk assessment, and a hierarchical clustering method is used to identify the vulnerability classification of critical nodes. The freeway network of Fuzhou in China is utilized to demonstrate the effectiveness of the proposed approach. Specifically, the toll stations in the study area are classified into five clusters of vulnerability: extremely high, high, medium, low, and extremely low. Approximately 31% of the toll stations were classified as the high or extremely high cluster, and three extremely vulnerable freeway sections requiring different precautions were identified. The proposed network vulnerability analysis method provides a new perspective to examine the vulnerability of freeway networks.
高速公路网络易受到自然灾害和人为干扰的影响。网络中一个或多个收费站的关闭通常会导致高速公路性能的急剧下降。因此，测量脆弱性的概率和后果，以识别网络中的关键部分，对道路应急管理至关重要。大多数现有技术仅测量节点关闭的后果，由于缺乏广泛的历史数据库，很少考虑节点关闭的概率；此外，它们忽略了研究区域以外的高速公路，这可能导致拓扑分析和交通分配的错误。此外，脆弱性评估中忽略了高速公路隧道运行产生的负面影响。在这项研究中，提出了一种考虑脆弱性概率和后果的高速公路脆弱性评估框架，该框架基于网络级联失效分析的角度。使用改进的耦合映射格子模型进行级联失效分析，该模型考虑了隧道的负面影响，并优化了局部交通再分配规则。捕获网络级联失效的扰动阈值和传播时间步长，以反映脆弱性的概率和后果。基于风险评估建立节点脆弱性指数，并使用层次聚类方法识别关键节点的脆弱性分类。利用中国福州的高速公路网络验证了所提出方法的有效性。具体来说，研究区域内的收费站被分为五个脆弱性等级：极高、高、中、低和极低。约 31%的收费站被归类为高或极高等级，识别出了三个需要不同预防措施的非常脆弱的高速公路路段。所提出的网络脆弱性分析方法为检查高速公路网络的脆弱性提供了一个新视角。
