Traffic Outlier Detection by Density-Based Bounded Local Outlier Factors

Outlier detection (OD) is widely used in many fields, such as finance, information and medicine, in cleaning up datasets and keeping the useful information. In a traffic system, it alerts the transport department and drivers with abnormal traffic situations such as congestion and traffic accident. This paper presents a density-based bounded LOF (BLOF) method for large-scale traffic video data in Hong Kong. A dimension reduction by principal component analysis (PCA) was accomplished on the spatial-temporal traffic signals. Previously, a density-based local outlier factor (LOF) method on a two-dimensional (2D) PCA-proceeded spatial plane was performed. In this paper, a three-dimensional (3D) PCA-proceeded spatial space for the classical density-based OD is firstly compared with the results from the 2D counterpart. In our experiments, the classical density-based LOF OD has been applied to the 3D PCA-proceeded data domain, which is new in literature, and compared to the previous 2D domain. The average DSRs has increased about 2% in the PM sessions: 91% (2D) and 93% (3D). Also, comparing the classical density-based LOF and the new BLOF OD methods, the average DSRs in the supervised approach has increased from 94% (LOF) to 96% (BLOF) for the AM sessions and from 93% (LOF) to 95% (BLOF) for the PM sessions.