Using machine learning to compute tire-penetration related properties for enhanced rolling resistance prediction

Abstract

A major contributor to GHG emissions is the transportation sector, particularly pavement transport. The limited understanding of tire-pavement interactions leads to inaccurate predictions of these emissions, particularly from rolling resistance (RR). Traditional methods for predicting RR are constrained by their limited applicability and inability to account for the complex dynamics of tire-pavement interactions, resulting in poor prediction accuracy. These limitations make it challenging for policymakers to make proper decisions, as existing methods are manual and labour-intensive. This study aims to develop an automated system to capture tire-pavement interaction data using the Laser Crack Measurement System (LCMS). To the best of the authors' knowledge, no robust technique currently exists for automatically calculating tire penetration-related information from LCMS data to predict RR. Therefore, this research explores machine learning (ML)-based models to reduce uncertainties in existing approaches and enhance RR predictions using automated LCMS data. It examines the relationships between RR, tire penetration volume, and the characteristics of the Dutch pavement network, comparing the results with those of commonly used RR prediction models. The study introduces an automatic tire penetration calculation approach using LCMS data to assess the impact of tire penetration volume and depth on RR in relation to surface properties. The findings reveal that traditional empirical models show poor correlations between RR and texture indicators, whereas ML-based models significantly improve the accuracy of RR predictions. These results could inform the development of strategies to reduce GHG emissions from pavement transport, supporting global efforts to combat climate change and achieve the goals of the Paris Agreement.