Toward Road Safety Recommender Systems: Formal Concepts and Technical Basics

Abstract

Worldwide, traffic accidents are recognized as one of the leading causes of death. This phenomenon leads to significant daily losses affecting both road users and road authorities. Therefore, the need for effective dynamic road security systems is highly considered. Traffic accident data analysis is one of the promising approaches for improving road safety. By taking into account multiple factors (e.g., infrastructure, weather, driver behavior, etc.), it allows measuring the impact of traffic accidents on road security. However, reformulating this impact into practical road safety decisions remains limited and unstructured. To overcome the mentioned limitations, this paper proposes the first end-to-end recommendation framework for road safety. Our framework introduces a three-layered architecture, designed to handle data analysis and action recommendation tasks. For data analysis, we adopt a baseline of state-of-the-art machine and deep learning algorithms to build different traffic accident prediction models. For the action recommendation task, we developed a new approach involving model predictions, model interpretations, actions definition, and road-action interactions matrix annotation. The proposed framework has been successfully experimented and evaluated using two real-world datasets of historical traffic accidents of France (2006-2017) and Morocco (2010-2014), achieving interesting ROC-AUC scores of 0.93 and 0.96, respectively.