Identifying Hidden Patterns of Fatal Pedestrian Traffic Accidents in East Azerbaijan Province of Iran: Application of Categorical Principal Component Analysis (CATPCA)

Background: Identifying patterns and hidden relationships among fatal pedestrian road traffic accident (FPRTA) features can be effective to reduce pedestrian fatalities. This study aimed to detect the patterns of FPRTA in the East Azerbaijan province of Iran. Methods: : The present study is a descriptive-analytic study based on the data of all 1782 FPRTAs that occurred in East Azerbaijan province of Iran during the years 2010 to 2019 collected by the forensic organization. CATPCA (Categorical Principal Component Analysis) was performed to recognize hidden patterns in the data by extracting principal components from the set of 13 features of FPRTA. The importance of each component was assessed by using the variance accounted for (VAF) index. Results: The optimum number of components to fit the CATPCA model was six with a 71.09% explanation of the total variation. The first and most important component with VAF=22.04% contained the demographic and socioeconomic characteristics of the pedestrians. The second-ranked component with VAF=12.96% was about the injury. The third component with VAF=10.56% was about the severity of the accident. The fourth component with VAF=9.07% was somehow related to the knowledge and observance of the traffic rules. The fifth component with VAF=8.63% was about the quality of medical relief and finally, the sixth component with VAF=7.82% was about environmental conditions. Conclusion: CATPCA revealed hidden patterns of FPRTA data in the format of six components. The revealed patterns showed that some interactions between correlated features led to a higher mortality rate.