Please use this identifier to cite or link to this item:
Title: Outlier detection in large-scale traffic data by Naïve Bayes method and Gaussian mixture model method
Authors: Lam, P
Wang, L 
Ngan, HYT
Yung, NHC
Yeh, AGO
Issue Date: 2017
Publisher: Society for Imaging Science and Technology
Source: IS and T International Symposium on Electronic Imaging Science and Technology, 2017, p. 73-78 How to cite?
Abstract: It is meaningful to detect outliers in traffic data for traffic management. However, this is a massive task for people from large-scale database to distinguish outliers. In this paper, we present two methods: Kernel Smoothing Naïve Bayes (NB) method and Gaussian Mixture Model (GMM) method to automatically detect any hardware errors as well as abnormal traffic events in traffic data collected at a four-arm junction in Hong Kong. Traffic data was recorded in a video format, and converted to spatial-temporal (ST) traffic signals by statistics. The ST signals are then projected to a two-dimensional (2D) (x, y)-coordinate plane by Principal Component Analysis (PCA) for dimension reduction. We assume that inlier data are normal distributed. As such, the NB and GMM methods are successfully applied in OD (Outlier Detection) for traffic data. The kernel smooth NB method assumes the existence of kernel distributions in traffic data and uses Bayes' Theorem to perform OD. In contrast, the GMM method believes the traffic data is formed by the mixture of Gaussian distributions and exploits confidence region for OD. This paper would address the modeling of each method and evaluate their respective performances. Experimental results show that the NB algorithm with Triangle kernel and GMM method achieve up to 93.78% and 94.50% accuracies, respectively.
DOI: 10.2352/ISSN.2470-1173.2017.9.IRIACV-272
Appears in Collections:Conference Paper

View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

Citations as of Sep 18, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.