Prediction of Traffic Accidents Severity Based on Machine Learning and Multiclass Classification Model

Abstract

Road traffic accidents are a common and seemingly inevitable problem. While its occurrences rely on many unpredictable factors, this paper shows how to utilize machine learning to predict both the possibility of the accident and its severity. The datasets used were related to road accidents in several countries in a period of a few years. Some of the parameters observed were the weather conditions, sun position, speed limit, and time of the day. To predict the severity of the accident given the circumstances and road conditions, a multiclass classification model is used. Different datasets were combined to cover different situations and scenarios that happen in traffic and taking the severity of accidents in prediction. The dataset values were normalized before the training process and the training set and validated on the validation dataset. The prediction results show the correlation between used weather conditions, daylight time, and traffic accident severity.