Spatiotemporal Traffic Anomaly Detection on Urban Road Network Using Tensor Decomposition Method

Abstract

Tensor-based models emerged only recently in modeling and analysis of the spatiotemporal road traffic data. They outperform other data models regarding the property of simultaneously capturing both spatial and temporal components of the observed traffic dataset. In this paper, the nonnegative tensor decomposition method is used to extract traffic patterns in the form of Speed Transition Matrix (STM). The STM is presented as the approach for modeling the large sparse Floating Car Data (FCD). The anomaly of the traffic pattern is estimated using Kullback–Leibler divergence between the observed traffic pattern and the average traffic pattern. Experiments were conducted on the large sparse FCD dataset for the most relevant road segments in the City of Zagreb, which is the capital and largest city in Croatia. Results show that the method was able to detect the most anomalous traffic road segments, and with analysis of the extracted spatial and temporal components, conclusions could be drawn about the causes of the anomalies. Results are validated by using the domain knowledge from the Highway Capacity Manual and achieved a precision score value of more than 90%. Therefore, such valuable traffic information can be used in routing applications and urban traffic planning.