Traffic incident classification at intersections based on image sequences by HMM/SVM classifiers
Multimedia Tools and Applications
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This research aims at model development for incident detection and travel time estimation using a neuro-fuzzy algorithm. Traffic incidents such as accidents, weather and construction, are a major cause of congestion. Thus incident detection and optimal travel time estimation is required for improving general traffic conditions. Until recently, two approaches related to the above were the aim of many studies. One idea is to estimate travel time using data fusion from many sources while another is to estimate optical path through travel time data. As a first step, in this paper we develop an initial model for incident detection using a neuro-fuzzy algorithm. In our experiments we find that our proposed model has a incident detection rate (DR) of over 83% and a false alarm rate (FAR) under 24%. The test results also suggest that the proposed model enhances accuracy of incident detection in an arterial road and we expect the proposed model to contribute to formal traffic policy.