Discovering Traffic Anomaly Propagation in Urban Space Using Enhanced Traffic Change Peaks

Discovering traffic anomaly propagation enables a thorough understanding of traffic anomalies and dynamics. Existing methods, such as Outlier-Tree, are not accurate to find out the trend of abnormal traffic for two reasons. First, they discover the propagation pattern based on the detected traffic anomalies. The imperfection of the detection method itself may introduce false anomalies and miss the real anomaly. Second, they develop a propagation tree of anomalies by searching continuous spatial and temporal outlier neighborhoods rather than considering from a global perspective, and thus cannot form a complete propagation tree if a spatial or temporal gap exists. In this paper, we propose a novel discovering traffic anomaly propagation method using the mesh data and enhanced traffic change peaks (en-TCP) to visualize the change of traffic anomalies (e.g., an area where vehicles are gathering or evacuating) and thus accurately capture traffic anomaly propagation. Inspired by image processing techniques, the GPS trajectory dataset in each time bin can be converted to one grid traffic image and be stored in the grid density matrix, in which the grid cell corresponds to the pixel and the density of grid cells corresponds to the Gray level (0∼255) of pixels. An enhanced adaptive filter is developed to generate traffic change graph sequences from grid traffic images in consecutive periods, and clustering en-TCP in a continuous period is to discover the propagation of traffic anomalies. The accuracy and effectiveness of the proposed method have been demonstrated using a real-world GPS trajectory dataset.