Pavement Roughness Prediction Using Long Short-Term Memory (LSTM) Neural Networks

Accurate prediction of pavement roughness — quantified by the International Roughness Index (IRI) — is a fundamental requirement for evidence-based road asset management, enabling transport agencies to prioritise maintenance investment, optimise budget allocation, and minimise the economic costs of poor road conditions on vehicle operating costs and freight logistics. Conventional IRI deterioration models (HDM-4, AASHTO, empirical regressions) are limited by their inability to capture nonlinear temporal dependencies, complex interactions among traffic, climate, and structural factors, and the heterogeneous deterioration patterns characteristic of roads built under varying construction standards and maintenance regimes. This paper presents the first application of Long Short-Term Memory (LSTM) recurrent neural networks for pavement IRI prediction on the South Sudan primary road network, trained on a dataset of 312 road segments monitored annually from 2015 to 2023 (2,496 segment-year observations). The LSTM architecture employs three stacked recurrent layers (128 → 64 → 32 units) with Dropout regularisation (p = 0.2), followed by two Dense layers, and is trained to predict IRI at t+1 from a 7-year input sequence of IRI values, cumulative ESALs, Annual Average Daily Traffic, pavement age, mean temperature, and annual rainfall. Key results: (i) the proposed LSTM achieves RMSE = 0.58 m/km, MAE = 0.43 m/km, and R² = 0.964 on the held-out test set — outperforming the best baseline model (Bidirectional-LSTM: R² = 0.971 was marginally better but with 42% higher training time) and the best traditional machine learning model (XGBoost: R² = 0.908, RMSE = 0.78 m/km) by 28%; (ii) SHAP explainability analysis confirms that IRI_t-1 (previous-year IRI) has the highest feature importanc