Optimization of pavement maintenance and rehabilitation using pavement management system in Prince George

Pavement Management Systems (PMS) are essential for guiding cost-effective and sustainable road maintenance, particularly in municipalities operating within harsh climates and under financial constraints. This research examines the optimization of pavement maintenance strategies for the City of Prince George, British Columbia, by combining historical condition data, predictive modeling, and decision-support frameworks. The study utilizes pavement distress survey results from 2016, 2017, 2020, and 2023 to assess network-level deterioration, identify critical distress types, and establish performance baselines. To forecast pavement performance, three modeling approaches—Random Forest (RF), Multiple Linear Regression (MLR), and Artificial Neural Networks (ANN)—were applied to predict the Pavement Distress Index (PDI). These models were evaluated using the statistical metrics such as Root Mean Square Error (RMSE), and coefficient of determination (R²). The Random Forest model achieved the highest predictive accuracy (R² = 0.96, RMSE = 0.55), followed closely by the ANN (R² = 0.95, RMSE = 0.48), while the MLR model demonstrated lower predictive capability (R² = 0.81, RMSE = 0.92). Variable importance analysis identified transverse cracking, rutting, and surface roughness as the most influential predictors of deterioration. The findings of this research provide a data-driven framework for proactive pavement maintenance planning in Prince George, enabling the prioritization of high-impact interventions and the optimization of rehabilitation budgets. By extending pavement service life and reducing long-term maintenance costs, the proposed methodology supports the creation of more resilient transportation infrastructure. The framework can be adapted for use in other municipalities facing similar environmental and operational conditions, strengthening the integration of advanced analytics into municipal asset management practices.