Time Series Transformer-Based Modeling of Pavement Skid and Texture Deterioration
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912522754326528 |
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| author | Gao, Lu Din, Zia Kim, Kinam Senouci, Ahmed |
| author_facet | Gao, Lu Din, Zia Kim, Kinam Senouci, Ahmed |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_01842 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Time Series Transformer-Based Modeling of Pavement Skid and Texture Deterioration Gao, Lu Din, Zia Kim, Kinam Senouci, Ahmed Applications 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. |
| title | Time Series Transformer-Based Modeling of Pavement Skid and Texture Deterioration |
| topic | Applications |
| url | https://arxiv.org/abs/2507.01842 |