  The real-time crash likelihood prediction has been an important research
topic. Various classifiers, such as support vector machine (SVM) and tree-based
boosting algorithms, have been proposed in traffic safety studies. However, few
research focuses on the missing data imputation in real-time crash likelihood
prediction, although missing values are commonly observed due to breakdown of
sensors or external interference. Besides, classifying imbalanced data is also
a difficult problem in real-time crash likelihood prediction, since it is hard
to distinguish crash-prone cases from non-crash cases which compose the
majority of the observed samples. In this paper, principal component analysis
(PCA) based approaches, including LS-PCA, PPCA, and VBPCA, are employed for
imputing missing values, while two kinds of solutions are developed to solve
the problem in imbalanced data. The results show that PPCA and VBPCA not only
outperform LS-PCA and other imputation methods (including mean imputation and
k-means clustering imputation), in terms of the root mean square error (RMSE),
but also help the classifiers achieve better predictive performance. The two
solutions, i.e., cost-sensitive learning and synthetic minority oversampling
technique (SMOTE), help improve the sensitivity by adjusting the classifiers to
pay more attention to the minority class.
