# Analysis of Cascading Failures and Recovery in Freeway Network Under the Impact of Incidents
###### Abstract  
In the past few decades, extensive research has been conducted on the modeling of cascading failures and their recovery processes in freeway networks. In practice, the restoration of functionality and structure in complex networks that suffer large-scale cascading failures may involve a series of repair operations. In this paper, we first propose a cascading failure model for freeway networks, which considers load redistribution by taking travelers' choice behavior into account. Specifically, we use the Stochastic User Equilibrium (SUE) as a method for redistribution in the model. Next, we propose a recovery strategy focused on critical edges, with their importance ranked through the integration of the network's topological features and traffic characteristics. This ranking then serves as the foundation for the edge-recovery process. This model considers the operational mechanisms of complex freeway networks. In the experiment, we used the freeway network in Hunan Province as a case study to validate the effectiveness of our model. Traffic volume data were collected from toll stations on the freeway network, and the topological structure of the network was combined with these data to construct a complex weighted freeway network. The evolution of network cascading failures was analyzed under various scenarios of attacks caused by traffic incidents. Subsequently, the failed network was recovered, and the results indicate that the proposed recovery strategy demonstrates better performance compared to other traditional methods. This research provides theoretical and methodological support for the management of freeway networks. f 12 May 202512 June  
effectively capture the load variations and capacity constraints of nodes and edges within a transportation network [11]. However, previous research on load distribution methods has emphasized the characteristics of nodes, often overlooking the attributes of edges, such as capacity and length [12]. Moreover, travelers make decisions based on the game behavior of independent path choices, determining user equilibrium and system optimum [10]. Therefore, when redistributing loads, it is essential to account for route choice variations stemming from travelers' random perceptions and risk preferences. Based on the above research, this paper aims to improve the cascading failure model applied to traffic networks to accurately characterize the role of edges in load distribution and to consider travelers' route choice behavior. Although significant efforts have been devoted to the prevention and mitigation of cascading failures in traffic networks, it is not possible to completely eliminate this catastrophic process [13]. To address this issue, various models have been developed to simulate the recovery process following cascading failures. After a cascading failure, the remaining network typically contains multiple failed components. Given the extensive coverage of real-world transportation networks and resource limitations, it may be necessary to repair and reintegrate network components individually [14]. The recovery of a single node or edge may cause disturbances to other components, potentially leading to new cascading failures. In this situation, modeling the recovery process as a series of interdependent processes can better illustrate the dynamic nature of system recovery [13]. Recent advancements in transport resilience research have been significantly driven by the integration of intelligent infrastructure, machine learning methodologies, and adaptive systems [15; 16; 17]. Intelligent infrastructure enables real-time monitoring and dynamic responses to network disruptions, while machine learning approaches provide powerful tools for analyzing large-scale transportation data, predicting network vulnerabilities, and optimizing recovery strategies [15; 16]. Furthermore, adaptive systems have been developed to dynamically adjust recovery processes in response to changing network conditions and traveler behaviors [17].