Detection and Classification of Traffic Anomalies Using Microscopic Traffic Variables

This paper proposes a novel anomaly detection and classification algorithm that combines the spatiotemporal changes in the variability of microscopic traffic variables, namely, relative speed, intervehicle time gap, and lane changing. When applied to real-world scenarios, the proposed algorithm can use the variances of statistics of microscopic traffic variables to detect and classify traffic anomalies. Based on a simulation environment, it is shown that, with minimum prior knowledge and partial availability of microscopic traffic information from as few as 20% of the vehicle population, the proposed algorithm can still achieve 100% detection rates and very low false alarm rates, which outperforms previous algorithms monitoring loop detectors that are ideally placed at locations where anomalies originate.