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

TL;DR

This paper develops a time series transformer model to accurately predict pavement surface deterioration, aiding maintenance planning by capturing nonlinear degradation influenced by environmental and operational factors.

Contribution

It introduces a novel application of transformer-based modeling to forecast pavement skid and texture deterioration over time using field data.

Findings

Transformer model achieved R2 = 0.981 for skid resistance prediction.

Random Forest best for macrotexture with R2 = 0.838.

Surface deterioration is nonlinear and affected by multiple factors.

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.