Using Principal Component Analysis to Solve a Class Imbalance Problem in Traffic Incident Detection

High imbalances occur in real-world situations when a detection system needs to identify the rare but important event of a traffic incident. Traffic incident detection can be treated as a task of learning classifiers from imbalanced or skewed datasets. Using principal component analysis (PCA), a one-class classifier for incident detection is constructed from the major and minor principal components of normal instances. Experiments are conducted with a real traffic dataset collected from the A12 highway in The Netherlands. The parameters setting, including the significance level, the percentage of the total variation explained, and the upper bound of the eigenvalues for the minor components, is discussed. The test results demonstrate that this method achieves better performance than partial least squares regression. The method is shown to be promising for traffic incident detection.