Ranking critical bridges in wide-area road networks through iterative macroscopic traffic simulation

Bridge failures in case of disruptive events can lead to severe mobility interruptions and losses in regional connectivity. This study proposes a scalable and computationally efficient network modeling methodology to support the prioritization of bridge monitoring and retrofitting under resource constraints by integrating publicly available datasets, hazard information, and simplified incremental macroscopic traffic simulation. Using Alessandria, Italy, as a case study, baseline and disrupted road network scenarios were generated, where flood-exposed bridge edges were iteratively closed one at a time to represent flood-induced disruptions. An origin–destination matrix was estimated to assess traffic redistribution under each disruption scenario. Traffic performance indicators were then used to quantify the impacts of bridge-edge closures on regional mobility, and the analysis was extended to complete physical-bridge closures. The findings reveal that while local bridges may produce high relative increases in travel time, motorway bridges generate disproportionately large absolute impacts due to their high traffic volumes, underscoring their crucial role in maintaining regional connectivity. The principal criticality patterns were preserved when complete physical bridges were closed, while comparison with a topological assessment highlighted the importance of considering traffic demand and route redistribution. A traffic-demand sensitivity analysis further confirmed the robustness of the identified critical bridges under variations in motorway crossing demand.