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This paper presents a formal method for characterizing the potential outliersfrom the data signature projection of traffic data set using Non-MetricMultidimensional Scaling (nMDS) visualization. Previous work had only relied onvisual inspection and the subjective nature of this technique may derive falseand invalid potential outliers. The identification of correct potentialoutliers had already been an open problem proposed in literature. This is dueto the fact that they pinpoint areas and time frames where trafficincidents/accidents occur along the North Luzon Expressway (NLEX) in Luzon. Inthis paper, potential outliers are classified into (1) absolute potentialoutliers; (2) valid potential outliers; and (3) ambiguous potential outliersthrough the use of confidence bands and confidence ellipse. A method is alsodescribed to validate cluster membership of identified ambiguous potentialoutliers. Using the 2006 NLEX Balintawak Northbound (BLK-NB) data set, we wereable to identify two absolute potential outliers, nine valid potentialoutliers, and five ambiguous potential outliers. In a literature where VectorFusion was used, 10 potential outliers were identified. Given the results forthe nMDS visualization using the confidence bands and confidence ellipses, allof these 10 potential outliers were also found and 8 new potential outlierswere also found.

Characterizing Classes of Potential Outliers through Traffic Data Set Data Signature 2D nMDS Projection