A Machine Learning Model for Road Accident Prediction and Prevention

Travelling by road has remained a long time means of movemrnt from one place to the other as humans navigate their daily lives. However, this also comes with a level of high risks, as there are so many challenges attached to this means of movement. This paper aims to develop a machine learning model that predicts and prevents road accidents. It basically does its prediction by taking statistics of log using the following parameters such as road name, weather condition, speed, road condition, traffic density, time, and date. The model was trained using 30 number of estimators and 32 random state while testing was done using the following performance metrics such as Accuracy 0.698%, Precision 0.678%, Recall 0.714% and F1 Score 0.696% for efficiency. The Confusion Matrix from 1 to 3 shows alternative routes to ply upon prediction of risks in other to prevent the already predicted danger whilst depicting true and predicted values. The prediction results shows that the accident probability is 0.7%, with a high risk level and also an advice to avoid particular route and consider alternative route usuage. The above model has been tested and proves efficient for predicting and also preventing road accidents and will ensure safety of human lives on the road during usuage.