Predicting the severity of road traffic accidents Morocco: a supervised machine learning approach - Touzani, Halima Drissi; Faquir, Sanaa; Yahyaouy, Ali (IAES International Journal of Artificial Intelligence (IJ-AI), 2025)
Early prediction of road accidents fatality and injuries severity is one of the important subjects to road safety emphasizing the critical need to prevent serious consequences to reduce injuries and fatalities. This study uses real road accidents data set in Morocco. It represents the intersection between road safety and data science, aiming to employ machine learning techniques to provide valuable insights in accident’s severity prevention. The purpose of this paper is to study road accidents data in the country and combine results from statistical methods, spatial analysis, and machine learning models to determine which factors will mostly contribute to increase the accident’ severity in the country. A comparison of results obtained was also conducted in this paper using different metrics to evaluate the effectiveness of each method and determine the most important factors that contribute to increase the fatality or injuries severity in the specific context of accidents. The best prediction model was then injected into a proposed algorithm where more intelligent techniques are included to be implemented in a car engine to perform an early detection of severe accidents and therefore preventing crashes from happening.