Key points are not available for this paper at this time.
• Hybrid simulation-graph approach identified optimal weight-metric combinations. • W2 weighting scheme with Total Node Strength achieved 96 % correlation accuracy. • Weighted graphs dramatically outperformed unweighted in vulnerability assessment. • Dynamic traffic-informed weighting captured congestion effects better than static. Understanding transportation network vulnerability and resilience is vital for urban mobility and economic thriving. While graph representations are commonly used for assessment, the choice of graph weights or metrics lacks clear justification based on traffic dynamics. This study introduces a novel approach that combines detailed traffic simulations with graph representations to develop a comprehensive framework for vulnerability and resilience studies. Using a real road network case study, numerous disruption scenarios were simulated to identify the most effective combination of graph weights and metrics reflecting network performance under disruptions. The methodology integrates outputs from a national transport simulation model with graph theory analysis. It evaluates applying four weighting schemes to nine graph metrics and compares their results against traffic simulation outputs. Findings reveal that a weighting scheme incorporating travel time and volume-to-capacity ratio consistently outperforms others across various disruption scenarios. When combined with this scheme, Total Node Strength shows the strongest correlation with simulation outcomes. The results also demonstrate the advantage of weighted graphs over unweighted ones in capturing network performance. Future research may focus on validating these findings across diverse network types and sizes and on exploring dynamic network behaviors to enhance real-world applicability.
Marian et al. (Sat,) studied this question.