AI for Road Safety in Nepal: Predicting High-Risk Accident Zones Using Traffic, Weather, and Road Condition Data

Abstract

Road accidents are a leading cause of injury and fatalities in Nepal, especially in regions with heavy traffic, poor road infrastructure, and variable weather conditions. This study leverages machine learning models and statistical analysis on historical traffic, weather, and road condition data to predict high-risk accident zones. Simulated analyses demonstrate the effectiveness of tree-based models in capturing complex interactions among risk factors. The findings provide actionable insights for authorities to prioritize safety interventions, improve traffic management, and enhance public safety, ultimately aiming to reduce accident rates and save lives.