An Intelligent Risk Aware Navigation Framework for Accident Hotspot Prediction, Real-Time Traffic Analysis, and Safety-Oriented Route Planning

Abstract

Road traffic accidents remain a major public safety issue, particularly in urban regions where increasing traffic density and complex road environments contribute to higher accident risk. Existing navigation systems primarily optimize routes based on travel time or distance, without considering accident risk, which can expose drivers to unsafe road segments. This study proposes an intelligent risk-aware navigation framework that integrates accident hotspot prediction with real-time traffic analysis for safety-oriented route planning. A supervised machine learning model was trained using historical accident records obtained from Sri Lanka Police data to estimate accident risk levels across road segments. These risk scores are combined with real-time traffic information retrieved from mapping services to evaluate alternative routes based on both safety and travel efficiency. Experimental results show that the proposed model achieves an accuracy of 0.93 in predicting accident risk levels. Furthermore, the system is able to recommend routes that reduce exposure to accident-prone areas while maintaining acceptable travel time. A mobile prototype was developed to visualize accident hotspots and provide safer route commendations. The results demonstrate that integrating predictive accident analytics with real-time traffic information can significantly enhance navigation systems by enabling safety-aware decision-making and improving overall road safety