Spatiotemporal variability of pavement material costs: Implications for maintenance strategies and economic-environmental sustainability

Traditional pavement management often fails to account for the spatiotemporal variability of material and energy costs, compromising both budgetary accuracy and long-term sustainability. To bridge the gap, this study introduces a dynamic optimization framework that integrates spatiotemporal cost predictions into multi-year maintenance planning. Using provincial-level monthly cost data from China, five time series models were developed and validated for key pavement materials and energy, with prediction uncertainty quantified via residual bootstrapping. An exhaustive search over a five-year decision period within a 10-year life-cycle analysis then identifies strategies that minimize total life-cycle cost, encompassing agency cost, user cost, and greenhouse gas (GHG) emissions. Results show significant spatiotemporal variability in historical costs, which was effectively captured by province-specific models, achieving in-sample and out-of-sample mean absolute percentage error (MAPE) both within 10%. Maintenance optimization under dynamic cost scenarios alters optimal strategies across provinces, affecting treatment timing, frequency, and intensity due to shifting trade-offs among agency, user, and environmental costs. A life-cycle impact analysis reveals substantial economic and environmental variability. At the national average, total life-cycle costs under dynamic forecasts range from 16.9% below to 9.0% above static baselines, with agency costs exhibiting even greater fluctuation. While mean dynamic forecasts for user costs and GHG emissions align closely with static results, their percentile projections show large deviations, highlighting a high sensitivity to extreme cost pathways. The findings of this study are expected to substantially enhance the realism and accuracy of pavement management strategies under spatiotemporally dynamic economic and environmental conditions.