Modelling of young and old driver injury severity in speeding-related road accidents

This study aimed to identify the best machine learning (ML) models for predicting the injury severity of young and old drivers involved in speeding-related road accidents. The best ML model was identified based on the optimal combination of accuracy, F1 score, and area-under-curve metrics. The feature importance analysis was employed to rate the various significant factors affecting young and old driver injury severity (DIS) based on the best ML models and their impacts on the DIS were compared. Police-reported accident data collected from Itanagar and Shillong during 2011–2020 were used in the present duty. Twelve supervised ML models were implemented using 5-,10-, and 15-fold cross-validations in each train ratio value (0.7 and 0.8). The results revealed that the Extra Trees model was the best ML model for the Itanagar young and old DIS prediction. However, it was not easy to identify the best overall ML model in Shillong. The vehicle type variable was the most important factor in predicting the injury severity of Itanagar and Shillong drivers. These findings would be helpful for the transportation authorities to formulate appropriate policies and to adopt effective measures to reduce the speed tendency behaviour of young and old drivers for road safety.