Machine Learning for Severity Classification of Accidents Involving Powered Two Wheelers
Natalia Selini Hadjidimitriou, Marco Lippi, Mauro Dell’Amico and Alexander Skiera
Road traffic safety is one of the major challenges for the future of smart cities and transportation networks. Despite severalsolutions exist to reduce the number of fatalities and severe accidents happening daily in our roads, this reduction is smaller thanexpected and new methods and intelligent systems are needed. The emergency Call is an initiative of the European Commissionaimed at providing rapid assistance to motorists thanks to the implementation of a unique emergency number. In this work, we study the problem of classifying the severity of accidents involving Powered Two Wheelers, by exploiting machine learning systems based on features that could be reasonably collected at the moment of the accident. An extended study on the set of features allows to identify the most important factors that allow to distinguish accident severity. The system we develop achieves over 90% of precision and recall on a large, publicly available corpus, using only a set of twelve features.
Data