Analyzing Transportation Network Vulnerability to Critical-Link Attacks Through Topology Changes and Traffic Volume Assessment  
[PERSON]  
1 Department of Environmental and Social Infrastructure, Graduate School of Engineering, Nagaoka University of Technology, Nagaoka 940-2188, Niigata, Japan; [EMAIL_ADDRESS] (T.K. ); [EMAIL_ADDRESS] (K.S.) 1 Department of Environmental and Social Infrastructure, Graduate School of Engineering, Nagaoka University of Technology, Nagaoka 940-2188, Niigata, Japan; [EMAIL_ADDRESS] (T.K. ); [EMAIL_ADDRESS] (T.K.) 1 Department of Environmental and Socialassessed the vulnerability of transportation networks from different perspectives and evaluation methods. According to reviews by [PERSON] and [PERSON] [5], most recent studies have focused on enumerating, generating, and simulating disruptive scenarios. These studies evaluated network vulnerability utilizing different performance measures, such as origin-destination (O-D) connectivity [6], travel time [7], and network accessibility [8]. While the above studies highlight the importance of understanding network vulnerabilities and their impacts, they primarily focus on assessing the vulnerability of transportation networks to static and deterministic disruptions [3]. These methods are typically scenario-specific, relying on pre-disaster occurrences or stochastic modeling approaches that evaluate road segment failure probabilities based on predefined disruption conditions and extensive data from past hazard events [8; 9]. For instance, [PERSON] et al. [10] analyzed landslide-induced disruptions in Scotland's road network, identifying 152 vulnerable segments and providing insights for mitigation. [PERSON] et al. [11] used Monte Carlo simulations to evaluate Japan's Tokai region's road vulnerabilities to multi-hazard events. [PERSON] et al. [12] applied an accessibility-based approach to prioritize critical infrastructure in flood-prone Hillsborough County, Florida. [PERSON] et al. [13] analyzed urban flooding impacts on Colombo's transport network using network centrality measures. While these studies provide valuable insights, they rely on predefined hazard scenarios and extensive data inputs. Therefore, such approaches are limited in their applicability to unforeseen disruption scenarios or cases where data are sparse. There is a critical need for a holistic method to assess transportation network vulnerability regardless of specific disruption scenarios. This method should require minimal data while providing actionable insights into network vulnerabilities. In this context, recent studies have suggested assessing vulnerability by targeting critical links, distinguishing worst-case scenarios to evaluate network vulnerability under the most severe conditions. This approach helps identify critical links of the network that could cause the most severe disruptions to overall performance [14; 15]. Doing so provides a robust framework for proactive decision making and targeted investment in network resilience, ensuring that the system is better equipped to handle unexpected disruptions. By targeting critical links, the worst-case scenario approach focuses on analyzing the most severe and unfavorable conditions that could impact a system or situation. By evaluating these extreme scenarios, analysts gain valuable insights into the potential magnitude of losses that may occur during unexpected disruptions. This understanding enables the development of targeted strategies to mitigate risks and minimize adverse impacts, ensuring greater preparedness and resilience in the face of unforeseen challenges [16]. However, even though the aforementioned worst-case approach network vulnerability studies are well suited for assessing network vulnerability, there are certain limitations that require further study. For instance, these studies often adopt a stochastic modeling approach that can be computationally intensive for assessing network vulnerability in larger transportation networks [11].