Abstract

The interaction between tires and the road surface is crucial for maintaining vehicle stability and safety. The frictional forces at the tire-road interface, along with the coefficient of friction, affect vehicle dynamics, influencing transportation safety. Rapid advancements in transportation technology, infrastructure demands, and safety regulations pose complex transportation challenges, requiring adaptive solutions. The goal of this study is to provide actionable insights for highway agencies, offering improved friction assessment methodologies that support safer and more effective roadway maintenance and design. This study investigates and verify Locked Wheel Skid Tester (LWST) friction measurement on horizontal curves using Sideway-force Coefficient Routine Investigation Machine (SCRIM), pavement friction performance adjustment factor through a numerical approach incorporating thermomechanical finite element analysis (FEA), and advanced pavement marking assessments. The research aims to enhance friction measurement accuracy and improve roadway safety by integrating predictive modeling and field validation. Machine learning algorithms refine the interpretation of friction data, enabling the development of adjustment factors for various road geometries and speeds. Additionally, optimized pavement marking materials, including angular glass beads and ceramic particles, are explored to enhance long-term durability and friction performance.

Keywords

friction, LWST, SCRIM, pavement marking, finite element analysis, data analysis, machine learning

Report Number

FHWA/IN/JTRP-2025/22

SPR Number

4940

Performing Organization

Joint Transportation Research Program

Publisher Place

West Lafayette, Indiana

Date of Version

2025

DOI

10.5703/1288284317908

Additional Files
SPR-4940 Technical Summary.pdf (4170 kB)
SPR-4940 Technical Summary
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