An Algorithm for Incident Detection Using Artificial Neural Networks

• Main Authors: Yong-Kul Ki, Woo-Teak Jeong, Hee-Je Kwon, Mi-Ra Kim
• Format: Article
• Language: English
• Published: FRUCT 2019-11-01
• Series: Proceedings of the XXth Conference of Open Innovations Association FRUCT
• Subjects: traffic incident detection; artificial neural networks; travel speed; traffic information center
• Online Access: https://fruct.org/publications/fruct25/files/Ki.pdf
• ISSN: 2305-7254; 2343-0737

Vehicular accidents cause tragic loss of lives and traffic congestion to the transportation system. Therefore, prompt detection of traffic incidents offers tremendous benefits of minimizing congestion and reducing secondary accidents. Most incident management systems use inductive loop detectors for incident detection. Inductive loops are the most commonly used traffic detectors and they collect data such as vehicle speed at a point. However, the implemented algorithms using loop detectors showed mixed success. I think that the changes in average traffic speed in case of traffic incidents have certain patterns that are different from the normal conditions. In this paper, I try to automatically detect traffic incidents using artificial neural networks and traffic condition information of the traffic information center. In the field tests, the new model performed better than existing metho