Risk-Based Network-Level Pavement Treatment Optimization for Greenhouse Gas Emission Reduction

Transportation-related emissions are a major contributor to global greenhouse gas (GHG) emissions and are closely linked to pavement performance. Effective pavement treatment practices are crucial for sustaining pavement performance and mitigating associated GHG emissions. Although the treatment optimization for individual segments is well-studied, extending these strategies network-wide is challenging due to segment heterogeneity, budget constraints, and evolving performance across the network. This study introduces a novel risk-based framework for optimizing pavement treatments at the network level, aimed at reducing transportation-related GHG emissions within budgetary constraints. The framework is distinct in its integration of risk analysis, particularly Monte Carlo (MC) simulations and scenario analysis, to address uncertainties inherent in segment contexts and future treatment actions. This approach notably enhances the predictability of long-term impacts of treatments and themanagement of future uncertainties. The framework's effectiveness was demonstrated through 50-year case studies on Colorado interstates. The integration of scenario analysis was pivotal in determining the optimal treatments for both individual segments and the entire network, leading to a notable reduction of 10% in network GHG emissions. This underscores the importance of considering long-term treatment impacts in promoting sustainable and resilient network management. Additionally, the application of MC simulations slightly raises the overall network GHG estimate but significantly reduces its variability, thereby enhancing the robustness of pavement network management.