Towards vulnerability urban road networks: Adaptive topological optimization and network performance analysis

To address the vulnerability of complex transportation networks during sudden events and attacks, this study focuses on the road network of Fucheng District in Mianyang City and proposes an adaptive topological expansion optimization model to enhance the original road network data. Densely populated region adjustments were considered to calculate the composite vulnerability index of nodes. The analysis examined road network composite vulnerability index changes under different intentional attack strategies (e.g., targeting nodes with the highest or lowest composite vulnerability index first) and user response behaviors (comprehensive information availability and limited information acquisition). The results indicate that targeting nodes with the highest vulnerability causes 2.5 times more overall vulnerability than targeting nodes with the lowest vulnerability. Under comprehensive information availability (CIA) conditions, the road network's composite vulnerability index decreases by approximately 0.02 compared to limited information availability (LIA) conditions. The adjustment method accounting for population density distribution effectively identifies and protects critical nodes, enhancing the composite importance index within densely populated regions. This research provides theoretical support and practical tools for improving the robustness and pre-disaster preparedness of transportation networks.