Deep Learning–Based Approach for Short- and Long-Term IRI Forecasting Using Long-Term Pavement Performance Data

Abstract

This study presents a deep learning–based approach for forecasting the international roughness index (IRI) using a long short-term memory (LSTM) network enhanced with an attention mechanism. Leveraging data from the long-term pavement performance program, the proposed LSTM–attention model is applied to both short-term (three-step) and long-term (six-step) prediction horizons and is systematically evaluated under different historical input lengths (lags). The results show that the model can generate accurate multistep IRI forecasts, with a 7-year lag providing the best trade-off between predictive accuracy and data availability for long-term predictions. Stratified analyses further reveal that predictive performance is higher for pavements in good-to-fair ( 0 – 1.5    m / km ) condition than for those in more advanced stages of deterioration, indicating increasing variability in IRI progression as pavements degrade. The study also demonstrates that traditional evaluation metrics such as R 2 can be misleading for deep nonlinear models and highlights Chatterjee’s correlation coefficient ( ξ n ) as a more reliable measure of predictive association in this context.