Resilient Transportation Network Design under Uncertain Link Capacity Using a Trilevel Optimization Model

This study addresses uncertainty in a transportation network by proposing a trilevel optimization model, which improves resiliency against uncertain disruptions. The goal is to minimize the total travel time by designing a resilient transportation network under uncertain disruptions and deterministic origin-destination demands. The trilevel optimization model has three levels. The lower level determines the network flow, and the middle level assesses the network's resiliency by identifying the worst-case scenario disruptions that could lead to maximal travel time. The upper-level takes the system perspective to expand the existing transportation network to enhance resiliency. We also propose a formulation for the network flow problem to significantly reduce the number of variables and constraints. Two algorithms have been developed to solve the trilevel model. The results of solving the highway network in Iowa show that the trilevel optimization model improves the total travel time by an average of 41%.

Automated summary

This study proposes a trilevel optimization model to address uncertainty in a transportation network, aiming to improve resiliency against uncertain disruptions and minimize total travel time. The model utilizes two algorithms to significantly reduce the number of variables and constraints, and the results show an average improvement of 41% in total travel time in the case of the Iowa highway network.