A novel traffic accident detection method with comprehensive traffic flow features extraction

With the rapidly increasing of automobiles, traffic accidents are gradually becoming more frequent. This creates a great need for effective traffic anomaly detection algorithms. Existing methods shed light on directly inferring the abnormalities from traffic flow, which is short in features extraction and representation of traffic flows. In this paper, we propose three new traffic flow features, namely the road congestion, the traffic intensity, and the traffic state instability, for more comprehensive traffic status representation and anomaly detection. Residual analysis, quadratic discrimination, multi-resolution wavelet analysis are integrated for the extraction of the aforementioned features, which will be applied for the downstream tasks of traffic anomaly detection. Experimental results reveal that accident identification based on the proposed features is more effective than the raw traffic flow, which is supposed to provide an alternative approach for further applications and studies.

Automated summary

With the rapid increase in the number of automobiles, traffic accidents are becoming more frequent. This paper proposes three new traffic flow features and extracts them using residual analysis, quadratic discrimination, and multi-resolution wavelet analysis for traffic anomaly detection. Experimental results show that accident identification based on the proposed features is more effective, providing an alternative approach for further applications and studies.