Document Type
Journal Article
Department/Unit
Department of Mathematics
Language
English
Abstract
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.
Keywords
Outlier, Density-Based, Local Outlier Factor, Supervised Approach, Traffic Data
Publication Date
2016
Source Publication Title
Information Technology in Industry
Volume
4
Issue
1
Start Page
6
End Page
18
Publisher
IT in Industry
Peer Reviewed
1
Copyright
This work is licensed under a Creative Commons Attribution 3.0 License.
Funder
This research is supported by Hong Kong RGC GRF: 12201814, HKBU FRG1/15-16/002 and HKBU FRG2/14-15/054.
Link to Publisher's Edition
http://www.it-in-industry.org/index.php/itii/article/view/53
ISSN (print)
22040595
ISSN (electronic)
22031731
Recommended Citation
Tang, Jialing, and Henry Y. T. Ngan. "Traffic outlier detection by density-based bounded local outlier factors." Information Technology in Industry 4.1 (2016): 6-18.