## 5 Conclusions and Future Work  
This study aims to provide a comprehensive approach to assessing the vulnerability of transportation networks by evaluating both network performance and traffic volume under critical-link attacks, focusing on worst-case scenarios. Previous studies have often been limited by static and deterministic analyses, focusing solely on statistical characteris  
Figure 5: CCDF of traffic volume distribution for undisruptive conditions and first 10% of critical-link attacks. Disruptions marked with asterisks (*) indicate critical-link attacks that caused significant traffic flow declines. tics or the impact of disruptions on traffic flow. In contrast, this research integrates both perspectives--network performance and traffic flow--under worst-case scenarios. Critical road segments, identified based on various topological parameters, were disrupted sequentially, and changes in performance were assessed using the CCDF. To assess the impact on traffic volume, a linear relationship between the AADT and topological parameters derived from natural street transformations was established, with the CCDF function applied to evaluate changes in traffic volume under critical-link attacks. The findings demonstrate that the proposed approach effectively identifies critical components of the transportation network and evaluates the severity of disruptions under each topological perspective. Unlike traditional vulnerability assessment methods, which often rely on extensive empirical data or predefined disruption scenarios, our approach is particularly valuable in data-scarce environments where such information is limited or unavailable. This makes it highly applicable to medium-scale cities in Asia, where transport networks often develop an ad hoc manner with limited systematic planning. By employing a worst-case scenario analysis based on topological parameters, this method enables a broader and more practical vulnerability assessment that does not depend on predefined disruption conditions. Instead, it systematically examines how disruptions to key topological properties impact network performance and traffic flow, offering adaptable insights applicable across diverse urban contexts. This flexibility enhances its relevance for regions where data collection is challenging, providing a robust tool for proactive network resilience planning in developing cities. Using the CCDF for each topological parameter, this study revealed how critical-link attacks affect transportation network performance. The results indicate that, under normal conditions, the distributions have heavy tails, suggesting high probability and criticality. However, during attacks, the tail of the distribution is damaged as the critical links of the network are attacked. Consequently, all the distribution functions under disruption depict a lower tail with reduced probability and criticality for each topological parameter, as the most impacted segments are targeted during each disruption iteration. Disruptions to these key segments can lead to severe consequences for network performance. By employing this approach, we highlight how analyzing critical-link disruptions associated with each topological parameter allows for a detailed understanding of their individual influence on network vulnerability. This insight underscores the need to prioritize the strengthening of these segments to ensure a robust and resilient transportation system. Furthermore, this study explains the impact on traffic flow when critical segments are attacked. It depicts that the critical-link attacks lead to significant declines in traffic volume across all phases related to SC, CC, BC, and EC. In addition, the disruption of specific critical road segments results in a significant jump in traffic volume compared to other attacks on critical segments in the network. This phenomenon occurs either due to a lack of alternative segments to redirect traffic or because certain road failures have less impact on the network.