Real-Time Freeway Traffic Anomalous Event Detection System via Radar Detector Sensors
Undetected anomalous incidents on the freeway, such as crashes, can cause increased congestion and secondary crashes. To address this problem, we recently released a real freeway traffic anomalous event detection dataset. However, our evaluation was limited to a static week of traffic data. This demo presents a live anomaly detection dashboard, processing sensor data with a 1-minute delay. It has interactive components to adjust the anomaly detection performance, providing a realistic evaluation of graph anomaly detection methods for long-term analysis and method improvements.
Authors
- Daniel B. Work (ORCID: https://orcid.org/0000-0003-0565-2158)
- Marcos Quiñones-Grueiro (ORCID: https://orcid.org/0000-0001-5391-6774)
- Tyler Derr (ORCID: https://orcid.org/0000-0002-0080-5998)
- Austin Coursey (ORCID: https://orcid.org/0000-0003-1774-6442)
- Gautam Biswas (ORCID: https://orcid.org/0000-0002-2752-3878)
- William Barbour (ORCID: https://orcid.org/0000-0002-1368-3192)
- Zhiyao Zhang (ORCID: https://orcid.org/0000-0002-8848-3865)
- Junyi Ji (ORCID: https://orcid.org/0000-0003-1085-1573)
Institutions
- Vanderbilt University (US)
- Integrated Software (United States) (US)
Publication Details
- Published
- 2025-05-06
- DOI
- https://doi.org/10.1145/3716550.3725156
- Primary Topic
- Network Security and Intrusion Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00