A Traffic Anomaly Detection Method Based on Gravity Theory and LOF

In order to solve the problem that the traditional method cannot detect the anomaly well, we propose a new traffic anomaly detection method based on the theory of gravity and local outlier factor (LOF) in this paper. We improve the density peak clustering method based on the theory of gravity firstly. A new concept of potential energy is proposed, and a new potential energy–distance decision graph is used for clustering and anomaly detection. Considering the local characteristics of the sample points, we propose the concept of potential energy gradient with reference to LOF for further anomaly detection to improve the accuracy of detection. The simulation results show that the proposed method can detect more types of outliers and get more accurate results. The improved anomaly detection method has good anomaly detection performance.