Assessment of road network vulnerability using multilayer perceptron surrogates with automated closure propagation

Road networks face increasing disruptions, yet vulnerability assessment methods either oversimplify traffic dynamics or require extensive computational simulations. This research introduces a novel approach integrating traffic simulation, graph theory, and machine learning for efficient and accurate vulnerability assessment. Analysis across numerous disruption scenarios showed that static weighting is inadequate for capturing traffic redistribution effects. In contrast, dynamic weighting aligns strongly with simulation results but was computationally infeasible. To overcome this limitation, a specialized multilayer perceptron artificial neural network (ANN) model was developed with a dual-pathway architecture and a novel automated closure propagation algorithm, separating static network attributes from spatial relationships. This surrogate model generates predictions significantly faster than traffic simulations, enabling comprehensive vulnerability analyses, previously deemed impractical. Testing across diverse disruption scales demonstrated surrogate effectiveness and limitations. This research presents a transferable and scalable methodology for constructing simulation-informed ANN surrogate models, providing practical deployment guidance for informed resilient transportation network planning.