Time Series Transformer-Based Modeling of Pavement Skid and Texture Deterioration

Lu Gao; Zia Din; Kinam Kim; Ahmed Senouci

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arXiv · 2507.01842

Time Series Transformer-Based Modeling of Pavement Skid and Texture Deterioration

Lu Gao·Zia Din·Kinam Kim·Ahmed Senouci

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Abstract

This study investigates the deterioration of skid resistance and surface macrotexture following preventive maintenance using micro-milling techniques. Field data were collected from 31 asphalt pavement sections located across four climatic zones in Texas. The data encompasses a variety of surface types, milling depths, operational speeds, and drum configurations. A standardized data collection protocol was followed, with measurements taken before milling, immediately after treatment, and at 3, 6, 12, and 18 months post-treatment. Skid number and Mean Profile Depth (MPD) were used to evaluate surface friction and texture characteristics. The dataset was reformatted into a time-series structure with 930 observations, including contextual variables such as climatic zone, treatment parameters, and baseline surface condition. A comparative modeling framework was applied to predict the deterioration trends of both skid resistance and macrotexture over time. Eight regression models, including linear, tree-based, and ensemble methods, were evaluated alongside a time series transformer model. Results show that the transformer model achieved the highest prediction accuracy for skid resistance (R2 = 0.981), while Random Forest performing best for macrotexture prediction (R2 = 0.838). The findings indicate that the degradation of surface characteristics after preventive maintenance is nonlinear and influenced by a combination of environmental and operational factors. This study demonstrates the effectiveness of data-driven modeling in supporting transportation agencies with pavement performance forecasting and maintenance planning.

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Lu Gao, Zia Din, Kinam Kim, Ahmed Senouci. 2025-08-06. Time Series Transformer-Based Modeling of Pavement Skid and Texture Deterioration. https://arxiv.org/abs/2507.01842

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