A Study on Maximizing Traffic Reachability in Contingency Scenarios Based on Genetic Algorithms

Abstract

This paper presents a study on maximizing traffic demand accessibility in transportation networks under contingency scenarios using genetic algorithms. Transportation demand accessibility is defined as the proportion of allocated traffic that can reach its destination, considering potential disruptions in the network. The study begins by formulating a model for calculating transportation demand accessibility between different origin-destination pairs, taking into account the possibility of unforeseen events on any road segment. By applying genetic algorithms, the study aims to optimize the allocation of traffic flows in a way that maximizes the expected reachability across the network, even when multiple road sections experience contingencies. Several scenarios are considered, including the introduction of capacity constraints on road segments and varying traffic demands. The results demonstrate that the proposed method can effectively optimize traffic flow allocation, enhancing the robustness and efficiency of transportation networks under uncertain conditions. This research provides valuable insights for urban planners and traffic managers seeking to improve network resilience in the face of unexpected disruptions.