Real-time Traffic Incident Detection Using an Autoencoder Model

Traffic flow data collected by loop detectors have been widely used for traffic incident detection. As traffic flow data have strong spatial-temporal correlations, this study tries to detect traffic incidents using an unsupervised learning approach. In this paper, a novel automatic incident detection (AID) method based on Autoencoder (AE) is proposed to detect the occurrence time and the location of traffic incidents in both freeway and urban networks. AE is an unsupervised machine learning model, which extracts nonlinear features of traffic flow data. A statistic named Squared Prediction Error (SPE) is constructed for incident detection. Meanwhile, the contribution plot technique is applied for incident localization. The experiments are conducted via a microscopic simulation platform Vissim and the test results verify the efficiency and effectiveness of the proposed method.