Prediction of traffic crash severity in the Maltese islands

Abstract

Road traffic crashes represent a major global concern, impacting public safety, traffic congestion, and eco- nomic productivity. In Malta, the growing number of vehicles combined with a densely built environment, underscores the urgent need for predictive safety interventions. Research indicates that many road crashes exhibit recognisable patterns and are, to some extent, preventable. This work explores different machine learning techniques, to predict the severity of traffic crashes using training crash data in Malta. In this work, classification algorithms are developed to categorise crashes into four distinct severity classes, with promising prediction results. Furthermore, this work identifies high-risk zones and hotspots near critical infrastructures in the Maltese traffic network.