Vulnerability Analysis of a Road Network for Liquid Chemicals Transportation Considering Intentional Attack

While tank vehicles are in the process of transporting liquid chemicals, the transportation task may be delayed or terminated due to disruptions in the transportation network. To reduce transportation losses, it may be necessary to evaluate network performance to cope with emergencies in the transportation network. This study investigates the vulnerability of the road network for liquid chemical transportation (RNLCT) under both intentional and random attacks. We selected six network indices, including average degree, average path length, average betweenness of node and edge, average clustering coefficient, and network efficiency to analyze the topological structure of the network. A reverse greedy algorithm is applied to identify 40 key nodes for simulated intentional attacks. The results show that network efficiency drops by 57.3% under targeted attacks, compared to 37.8% under random attacks. Moreover, the proposed method outperforms the distance laplacian centrality, which leads to a 43.3% reduction, demonstrating its effectiveness in identifying key nodes and enhancing network robustness.