Bilevel Optimization of Detour Signage Placement under Road Closures

Abstract

Road closures resulting from construction activities significantly disrupt traffic flow and often divert heavy truck traffic onto alternative routes. These detours, if not properly planned, can funnel trucks onto roads not designed for high axle loads, accelerating pavement deterioration and increasing maintenance needs. To address this challenge, this research proposes a comprehensive bilevel modeling framework that integrates vehicle-type-specific traffic flow prediction with detour signage placement optimization. Employing a user equilibrium traffic assignment model solved by paired alternative segments algorithm, the study predicts traffic distributions before and after road closures, identifying road segments vulnerable to increased truck traffic. A genetic algorithm is utilized to solve the bilevel optimization framework to strategically determine optimal detour signage placement. The primary objective is to minimize a fitness function that incorporates both infrastructure considerations and network efficiency measures, prioritizing the restriction of heavy trucks from vulnerable or prohibited road segments while accounting for network total travel times. The model is demonstrated through application to the entire Minneapolis transportation network, illustrating its capability to handle complex, large-scale real-world scenarios effectively. Results show that the optimized signage placement significantly reduces truck usage of vulnerable roads while maintaining efficient traffic operations, offering valuable insights for policymakers and planners aiming to enhance resilience and sustainability in detour management strategies.