Modeling driver injury severity using machine learning algorithms

Author:

Sorum Neero Gumsar1ORCID,Pal Dibyendu1ORCID

Affiliation:

1. Department of Civil Engineering, North Eastern Regional Institute of Science & Technology, Nirjuli 791109, Arunachal Pradesh, India

Abstract

This study planned to predict and analyze the driver injury severity (DIS) using 12 machine learning (ML) algorithms. Police reports of single- and two-vehicle accidents that occurred during 2011–2020 in the two cities of India (Itanagar and Imphal) were used in this study. The best-performing model to predict the DIS for Itanagar was Gradient Boosting Trees (GBT). “Causes of Accident” variable had shown maximum impact on the DIS. In the case of Imphal, it was the GBT, Extra Trees, and Random Forest models across all k-fold cross-validation for train ratios 0.70, 0.80, and 0.90, respectively. “Causes of Accident” and “Vehicle Type” had shown maximum impact on the DIS. These results reveal that the ML models can be applied in hilly areas to predict and identify the important factors that affect DIS. Transportation authorities can analyze road accident data using these models while implementing various road safety measures.

Publisher

Canadian Science Publishing

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