Published Paper Details:

Road accidents remain a significant public health concern worldwide, leading to loss of lives, injuries, and economic losses. To address this issue, researchers have increasingly turned to machine learning techniques for analyzing road accident data to understand contributing factors, predict accident occurrences, and propose preventive measures. This paper provides a comprehensive review of the state-of-the-art machine learning-based approaches for road accident analysis. We systematically categorize and summarize the various machine learning methods employed in accident detection, severity prediction, causality analysis, and risk assessment. Additionally, we discuss the challenges associated with these approaches, including data availability, feature selection, model interpretability, and scalability. By highlighting the recent advancements, limitations, and future directions in road accident analysis using machine learning, this review aims to provide insights for researchers, policymakers, and practitioners working in the field of road safety.