QIU, MEI (Purdue University), Reindl, William (Purdue University), Chen, Yaobin (Purdue University), Chien, Stanley (Purdue University Indianapolis), Shellhamer, Nathan (Indiana Department of Transportation), McCoy, Dan (Indiana Department of Transportation), Hu, Shu (Purdue University)

Lane-Wise Highway Anomaly Detection

Scheduled for presentation during the Regular Session "S32a-AI-Driven Traffic Monitoring, Safety, and Anomaly Detection" (FR-LM-T32), Friday, November 21, 2025, 12:10−12:30, Southport 2

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 October 18, 2025

Abstract

This paper proposes a scalable and interpretable framework for lane-wise highway traffic anomaly detection, leveraging multi-modal time series data extracted from surveillance cameras. Unlike traditional sensor-dependent methods, our approach uses AI-powered vision models to extract lane-specific features—including vehicle count, occupancy, and truck percentage—without relying on costly hardware or complex road modeling. We introduce a novel dataset containing 73,139 lane-wise samples, annotated with four classes of expert-validated anomalies: three traffic-related anomalies (lane blockage and recovery, foreign object intrusion, and sustained congestion) and one sensor-related anomaly (camera angle shift). Our multi-branch detection system integrates deep learning, rule-based logic, and machine learning to improve robustness and precision. Extensive experiments demonstrate that our framework outperforms state-of-the-art methods in precision, recall, and F1-score, providing a cost-effective and scalable solution for real-world intelligent transportation systems. Our dataset and code can be found here: https://github.itap.purdue.edu/TASI/Lane-wise_traffic_AD.gi t