Leveraging Machine Learning for Road Accident Analysis

Abstract

Road accidents result in high human and economic costs globally. This paper examines how advanced machine learning techniques can support enhanced analysis of road accident data to uncover patterns and insights to guide traffic safety interventions. Novel machine learning methods proposed include hybrid neural network architectures optimized using nature-inspired algorithms and interpretable rule-based tree ensemble techniques. Our investigation commences with the training and evaluation of each model on a diverse dataset comprising various road-related features. The performance metrics, including accuracy predictive capabilities. The results reveal nuanced strengths and weaknesses in each approach.