The prevention of severe injuries during crashes has become one of the leading issues in traffic management and transportation safety. Identifying the impact factors that affect traffic injury severity is critical for reducing the occurrence of severe injuries. In this study, the Fatality Analysis Reporting System data are selected as the dataset for the analysis. An algorithm named improved Markov Blanket was proposed to extract the significant and common factors that affect crash injury severity from 29 variables related to driver characteristics, vehicle characteristics, accidents types, road condition, and environment characteristics. The Pearson correlation coefficient test is applied to verify the significant correlation between the selected factors and traffic injury severity. Two widely used classification algorithms (Bayesian networks and C4.5 decision tree) were employed to evaluate the performance of the proposed feature selection algorithm. The calculation result of the correlation coefficient, accuracy of classification, and classification error rate indicated that the improved Markov Blanket not only could extract the significant impact factors but could also improve the accuracy of classification. Meanwhile, the relationship between five selected factors (atmospheric condition, time of crash, alcohol test result, crash type, and driver's distraction) and traffic injury severity was also analyzed in this study. The results indicated that crashes occurred in bad weather condition (e.g. fog or worse), in night time, in drunk driving, in crash type of single driver, and in distracted driving, which are associated with more severe injuries.
Keywords: Bayesian networks; C4.5 decision tree; Feature extraction; improved Markov Blanket; traffic injury severity.