A Multiobjective Optimization Method for Integrated Road Asset Management Considering Traffic Dynamics

Abstract

Traditional road asset management often operates in isolation, leading to suboptimal coordination. While integrated asset management enables multiasset maintenance and rehabilitation (M&R) decisions on a unified platform, existing approaches typically do not systematically account for traffic redistribution effects. This study proposes a road M&R planning method based on bilevel multiobjective optimization (MOO) that explicitly integrates user and environmental considerations with economic and performance objectives. The upper level optimizes network-level multiyear agency cost, network condition, user cost, and greenhouse gas (GHG) emissions. The lower level employs a traffic assignment model to address traffic dynamics caused by reduced link capacity during M&R operations. The bilevel MOO model is solved using the Nondominated Sorting Genetic Algorithm III and Self-Regulated Method of Successful Averages to generate Pareto solutions, with an analytic hierarchy process-based weighted-sum method determining the final solution. A five-year case study on a road network in Liaoning Province, China, demonstrates the method’s effectiveness for pavement and bridge M&R planning: 19.11% improvement in network condition, 9.07% reduction in GHG emissions, and 4.64% reduction in user costs, proving the method’s effectiveness for achieving cost-effective and sustainable M&R decisions. Comparative analysis against a static traffic baseline reveals that explicitly modeling traffic redistribution reduces user costs by 99.39% and GHG emissions by 61.17%, demonstrating traffic dynamics alter optimal M&R decisions. The methodology is validated for a regional network with asphalt pavements, reinforced concrete T-beam bridges, and passenger vehicle traffic under deterministic demand; extensions to heterogeneous vehicle types, elastic demand, and other infrastructures represent directions for future studies.