Road Accident Analysis and Prediction of Accident Severity by Using Machine Learning in Bangladesh

Abstract

In recent years, the road accident has become a global problem and marked as the ninth prominent cause of death in the world. Due to the enormous number of road accidents every year, it has become a major problem in Bangladesh. It is entirely inadmissible and saddening to allow its citizen to kill by road accidents. Consequently, to handle this overwhelmed situation, a precise analysis is required. This research paper has been done to analyze traffic accidents more deeply to determine the intensity of accidents by using machine learning approaches in Bangladesh. We also figure out those significant factors that have a clear effect on road accidents and provide some beneficent suggestions regarding this issue. Analysis has been done, by using Decision Tree, K-Nearest Neighbors (KNN), Naïve Bayes and AdaBoost these four supervised learning techniques, to classify the severity of accidents into Fatal, Grievous, Simple Injury and Motor Collision these four categories. Finally, the best performance is achieved by AdaBoost.