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.

ISSN (print)

22040595

ISSN (electronic)

22031731

Included in

Mathematics Commons

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