A complex network analysis approach for estimation and detection of traffic incidents based on independent component analysis

Traffic incidents due to non-recurring congestion frequently occur in urban environments. In this study, we propose the estimation and detection of traffic incidents based on independent component analysis (ICA) and hybrid observer (HO)-generalized likelihood ratio (GLR) techniques. First, we develop the traffic time series to obtain insight into the traffic flow and to detect traffic incidents. Then, we use time series analysis to construct complex networks. Next, we propose the ICA technique to monitor traffic flow. Then, we introduce a piecewise switched linear model based observer to estimate the possible occurrence of traffic incidents. Finally, we propose a new incident detection method that combines HO and GLR techniques. The combined HO-GLR method can produce better incident detection, improve traffic safety, and enhance traffic management systems. We have validated the effectiveness of the proposed method using simulated traffic data generated from the Ayer Rajah Expressway in Singapore and a real-world dataset from the I-880 freeway of California. The performance metrics used to evaluate the performance of the proposed method includes detection rate, false alarm rate, classification rate, mean time to detection and the area under receiving operating characteristics curve. The experimental results show that the proposed method has obtained better performance in all of the criteria when compared with other well-known methods.