In fact, studies adopting a stochastic approach tend to be applied to overly simplified and often unrepresentative transport networks with few elements, whereas vulnerability analysis has been applied to large-scale transport networks [17]. This hinders the practical applications of this approach. Although recent studies have highlighted the potential of network topological parameters in assessing network vulnerability, there remains a lack of sufficient research exploring their applicability in worst-case scenarios and adopting robust performance assessment methods [15; 18]. In this context, a more comprehensive approach is needed to utilize topological parameters alongside their distinct network characteristics to evaluate network vulnerability under worst-case scenarios. These identified limitations are further discussed in Section 2, which introduces the analytical framework of the study. In addition to the above limitations, it is essential to understand how traffic flow changes when critical segments are disrupted to evaluate network performance. Generally, evaluating traffic flow changes under disruptive conditions at the network level presents significant challenges due to the complex interplay of various factors [19]. While user-equilibrium (UE) models can predict traffic flows at the network level, they focus on stable or long-term traffic flows [6]. However, traffic flow is affected by topological changes under disruptive conditions. Thus, assuming steady-state traffic conditions, as in UE models, is unrealistic under sudden disasters [20]. Additionally, the lack of real-time data during such events complicates the assessment of traffic flow changes across the network, limiting the applicability of dynamic UE models. In this context, recent studies have focused on changes in network topological parameters to evaluate network changes caused by disruptions [8]. Although studies have claimed a relationship between the topological parameters and traffic flow distribution [21], limited attention has been paid to assessing the impact of traffic flow under disruptive conditions. Lastly, most vulnerability assessment studies are predominantly focused on larger cities in developed countries, where sophisticated data and advanced methodologies are readily available [22]. Consequently, there is a significant gap in research addressing emerging and medium-scale cities, particularly in Asia, where such studies are critically needed due to rapid urbanization and limited access to data and resources [23]. These cities often develop transport networks in an ad hoc manner, leading to inefficiencies and sustainability challenges. The lack of vulnerability assessments in such contexts hinders effective planning [24]. Therefore, it is essential to explore the specific vulnerabilities of transportation networks in medium-scale Asian cities and to develop a simplified simulation framework tailored to these contexts, providing practical insights to guide effective decision-making and planning processes. Motivated by the aforementioned limitations, this study proposes a method to evaluate transportation network vulnerability under worst-case scenarios. The main contribution of this study is threefold:  
1. The proposed method identifies the network vulnerability under worst-case scenarios by targeting critical components derived from network topological parameters. To systematically characterize network performance changes under critical-link attacks, this study employs the complementary cumulative distribution function (CCDF) to analyze the empirical distribution of each topological parameter. The CCDF, which has been rarely utilized in previous studies, provides a novel approach for assessing transportation network performance. 2. This study proposes a straightforward modeling framework that uses topological parameters to assess the impact of critical-link attacks on traffic flow changes through multiple linear regression.