A density-based outlier detection (OD) method is presented by measuring the local outlier factor (LOF) on a projected principal component analysis (PCA) domain from real world spatialtemporal (ST) traffic signals. Its aim is to detect traffic data outliers which are errors in data
and traffic anomalies in real situations such as accidents, congestions and low volume. Since the ST traffic signals have a high degree of similarities, they are first projected to two-dimensional (2D) (x,y)-coordinates by the PCA to reduce its dimension as well as to remove noise, while keeping
the anomaly information of the signals. Based on the designed LOF algorithm, a semi-supervised approach is employed to label any embedded outliers. It reaches an average detection success rate of 93.5%.