A Resilience Optimization Method for Highway Networks Considering Community Structure

This study proposes a two-stage resilience optimization method for highway networks that accounts for community structure. Addressing the limitations of prior research, which primarily optimized highway networks by merely adding connections, this approach incorporates both edge addition and capacity expansion as optimization strategies. A dual-objective model is developed to minimize the average travel time across the network while maximizing network performance. This model enables precise node and edge selection for resilience optimization within a provincial highway network, supported by a simulated annealing algorithm. Using the Guangdong Province highway network as a case study, the method is evaluated through three sets of computational experiments under varying constraints. Sensitivity analysis is conducted on network performance metrics under multiple scenarios with different constraints on the number of added edges and capacity expansions. Results indicate that the marginal benefit of resilience improvement diminishes as the number of added edges increases. Following the implementation of the model-recommended optimization scheme, the average travel time of the network decreased by 22.02%, and resilience improved by 29.4%.