Spatiotemporal particle swarm optimization for future cost allocation in large-scale transportation infrastructure maintenance

Abstract

Transportation infrastructure is vital for sustaining communities and fostering economic development. Urbanization and climate change have led to the rapid deterioration of road transport systems, posing significant challenges for future sustainability. Current transportation infrastructure maintenance planning often prioritizes immediate needs and short-term deterioration indicators, which can overlook long-term changes and future funding constraints. Long-term road maintenance planning is challenged by the large number of decision variables and the complex temporal and spatial dependencies that govern pavement deterioration. Most existing optimization models overlook spatial relationships among road segments, resulting in low computational efficiency, especially for large-scale networks. To address this gap, this study proposes a Spatiotemporal Particle Swarm Optimization for Cost Allocation (SPOCA) model that integrates spatial clustering and heuristic optimization for large-scale decision-making. An age-filtered spatial clustering process first groups roads with similar ages and proximity to preserve spatial structure and reduce problem dimensionality, while a spatial relationship term embedded in the optimization captures correlations among neighboring clusters to improve coordinated decision-making. A case study of Western Australia demonstrates that the SPOCA model reduces computational time by 38% compared with the non-spatial model, while maintaining comparable accuracy and significantly improving network-level pavement quality. The SPOCA model provides a scalable and practical tool to support policymakers in developing efficient and sustainable infrastructure maintenance strategies.