  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
Na\"ive 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 outlier detection (OD) 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.
