Intelligent Road Safety Navigation in Sri Lanka: A Review of Machine Learning Techniques and Proposal of a Model for Predicting Accident Hotspots and Severity

Abstract

Both public safety and the stability of the economy are seriously threatened by traffic accidents. Like many other regions, Sri Lanka is faced with the challenge of road accidents, which hinder the path to safer roads. One of the reasons that accidents occur is that people are unaware of common accident locations. The government has already enforced other tactics, like traffic signals and fines, to reduce these incidents, but they have been ineffective. In order to decrease road accidents, people must change their driving patterns. While looking for a solution to that problem, existing studies around the world have proposed predictive machine learning models for accident-prone locations known as hotspots and severity levels. But there were not any existing studies that proposed a solution that was suitable for Sri Lanka. This research seeks to address these challenges by conducting an in-depth review of existing machine learning techniques and proposing the most suitable model approaches for prediction of accident hotspots and severity in Sri Lanka based on the availability of the accident data. One of the main objectives is to identify the correct machine learning techniques. According to studies, 81% used the 'Random Forest' algorithm, which is a supervised machine learning algorithm for the prediction. And Random Forest performed better in approximately 69% of the studies. And this research is not just proposing suitable model approaches for predictions. It provides the foundation to revolutionize road safety through the development of an intelligent road safety mobile navigation application