Modeling of injury severity of distracted driving accident using statistical and machine learning models

Distracted Driving (DD) is one of the global causes of high mortality and fatality in road traffic accidents. The increase in the number of distracted driving accidents (DDAs) is one of the concerns among transportation communities. The present study aimed to examine the individual and interacted effects of the influential factors on the injury severity of the DDAs using the Binary Logistic Regression (BLR) method, and at the same, to select the best machine learning (ML) model in predicting the injury severity of the DDA. The selection of the best ML model was based on the optimum combination of accuracy, F1 score, and area under curve metrics. Ten years of DDA data (2011−2020) provided by the police department of Imphal, India, was used in the present study. The BLR model-without-interaction results revealed that out of twenty categorical variables, nine categorical variables (below 18, 18−24, 25−40, above 40 years age group, two-wheeler, heavy motor vehicle, 12AM-6AM, 6PM-12AM, and hit-object collision) were statistically significant to the injury severity of the DDAs. In interaction model results, there were 11, 1, and 1 significant combinations among categorical variables in two-way, three-way, and four-way interaction models, respectively. The ML model results showed that overall, the XGBoost model was reported as the best-performing model in the first hyperparameter set, and the Single Layer Perceptron model in the second set. These results may be useful for transportation policymakers while implementing any countermeasures to improve road safety in hilly areas.