Addressing climatic uncertainty through fleet optimization for robust winter road maintenance policy design

Abstract

Effective winter road maintenance (WRM) is essential in regions with heavy snowfall, but climate change has increased the unpredictability and severity of winter weather, complicating WRM planning and policy. This study uses the Taguchi method to optimize WRM strategies, focusing on fleet configurations and maintenance practices. By integrating advanced technologies and data-driven decision-making, the research aims to provide policymakers with evidence-based recommendations to enhance WRM robustness and efficiency. The findings offer actionable insights for improving road safety, minimizing economic disruptions, and optimizing resource allocation under climatic uncertainty, ultimately supporting more effective and sustainable WRM operations.