A Machine Learning Model For Road Traffic Accident Factor Classification: The Case Of Kamba Woreda, Gamo Zone, Southern Ethiopia

Abstract

Road traffic accidents are events that occur on roads open to public traffic and result in injury, loss of life, vehicle damage, and substantial economic costs. As the number of vehicles increases worldwide, including in Ethiopia, road traffic accidents have risen accordingly, yet it remains difficult to determine the specific conditions that lead to them. Previous studies have largely focused on classifying accident severity levels or predicting whether an accident will occur, often using a limited set of features. This study addresses a different problem: classifying the contributing factors of road traffic accidents into four categories — human, vehicle, road, and environmental factors — using supervised machine learning. The dataset was obtained from the Gamo Zone Kamba Woreda Traffic Police Office and covers seven years of accident records (2011–2017 E.C.). After preprocessing, the dataset comprised 5,250 instances described by 19 attributes, including the target class.