  This paper presents a formal method for characterizing the potential outliers
from the data signature projection of traffic data set using Non-Metric
Multidimensional Scaling (nMDS) visualization. Previous work had only relied on
visual inspection and the subjective nature of this technique may derive false
and invalid potential outliers. The identification of correct potential
outliers had already been an open problem proposed in literature. This is due
to the fact that they pinpoint areas and time frames where traffic
incidents/accidents occur along the North Luzon Expressway (NLEX) in Luzon. In
this paper, potential outliers are classified into (1) absolute potential
outliers; (2) valid potential outliers; and (3) ambiguous potential outliers
through the use of confidence bands and confidence ellipse. A method is also
described to validate cluster membership of identified ambiguous potential
outliers. Using the 2006 NLEX Balintawak Northbound (BLK-NB) data set, we were
able to identify two absolute potential outliers, nine valid potential
outliers, and five ambiguous potential outliers. In a literature where Vector
Fusion was used, 10 potential outliers were identified. Given the results for
the nMDS visualization using the confidence bands and confidence ellipses, all
of these 10 potential outliers were also found and 8 new potential outliers
were also found.
