Prediction of Crash Injury Severity in Florida's Interstate-95
This work addresses injury severity prediction for traffic safety in a specific region, but it is incremental as it applies existing methods to new data without major innovations.
The study tackled predicting driver injury severity in traffic crashes on Florida's Interstate-95 from 2016 to 2021, using classification methods like logistic regression and Adaboost, with Adaboost achieving the best performance in recall and AUC metrics.
Drivers can sustain serious injuries in traffic accidents. In this study, traffic crashes on Florida's Interstate-95 from 2016 to 2021 were gathered, and several classification methods were used to estimate the severity of driver injuries. In the feature selection method, logistic regression was applied. To compare model performances, various model assessment matrices such as accuracy, recall, and area under curve (AUC) were developed. The Adaboost algorithm outperformed the others in terms of recall and AUC. SHAP values were also generated to explain the classification model's results. This analytical study can be used to examine factors that contribute to the severity of driver injuries in crashes.