Javed, Danial (Southeast University, Nanjing China), Chen, Leilei (Intelligent Transport System Research Center, Southeast Universi), Muhammad, Talha (Southeast University, Nanjing, China)

A Comprehensive Case Study Framework for Rutting Behavior Identification and Prediction.

Scheduled for presentation during the Video Session "On-Demand Video Presentations" (VP-VP), Saturday, November 22, 2025, 08:00−18:00, On-Demand Platform

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on April 2, 2026

Abstract

This paper presents an intelligent prediction system for rutting in flexible pavements, which suggest rutting features extracted from images and historical data to classify rutting types and forecast its behavior. It identifies various factors contributing to rutting, such as vehicle loading, environmental conditions, material properties, and summarizes different models for predicting rutting, including mechanical, empirical, and machine learning-based approaches.

The proposed system extracts key features like rut depth, width, load distribution, pavement structure, and environmental conditions to classify rutting into categories like wearing, instability, and structural rutting. Based on these classifications, the system will select appropriate prediction models for each type. The feasibility of this system is theoretical demonstrated through the classification of rutting images, with the MEPDG model recommended for structural rutting predictions as a case study.