Assessing resilience in urban road networks using macroscopic fundamental diagrams

This article proposes and evaluates predictive models for quantifying, assessing, and managing the impact of road or intersection closures on the performance of an urban road network. We measure the network’s performance loss using resilience-based metrics and apply rerouting to mitigate the adverse effects of road closures. The simulations are conducted in the extended Athens city centre. The findings indicate that the optimal area for implementing rerouting strategies varies depending on whether a single or multiple corridors are closed. By integrating this information with other factors, such as the number of closed corridors and traffic flow, we develop a supervised machine learning model to predict performance declines in the network resulting from road closures.