Traffic Accident Severity Prediction Using a Meta-Model Based on a Majority Vote

Abstract

Road traffic safety is a major concern for road authorities and ordinary citizens. Consequently, accident prediction has become an interesting research topic that tries to provide solutions to predict, in real-time, traffic accidents occurrence and their severity. In this paper, a meta-model to predict, as early as possible, the risk and the severity of traffic accidents is proposed. The meta-model exploits seven predictive algorithms widely used in the literature and relies on a majority voting mechanism to improve predictions. For this purpose, a dataset of more than 45,000 observations is used, and two accident levels are considered. Based on the localization and the time of each accident, non-accident data are generated to create negative observations in the final dataset. Moreover, several features related to traffic flow, weather, and road conditions are collected and used as predictors to build and evaluate the predictive solutions and the meta-model. The experiment results show that the proposed meta-model dominates all other models in terms of F1 score.