Road traffic safety is one of the major challenges for the future of smart cities and transportation networks. Despite several solutions exist to reduce the number of fatalities and severe accidents happening daily in our roads, this reduction is smaller than expected and new methods and intelligent systems are needed. The emergency Call is an initiative of the European Commission aimed 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.

Machine Learning for Severity Classification of Accidents Involving Powered Two Wheelers / Hadjidimitriou, N. S.; Dell'Amico, M.; Lippi, M.; Skiera, A.. - In: IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS. - ISSN 1524-9050. - 21:10(2020), pp. 4308-4317. [10.1109/TITS.2019.2939624]

Machine Learning for Severity Classification of Accidents Involving Powered Two Wheelers

N. S. Hadjidimitriou;M. Dell'Amico;M. Lippi;
2020

Abstract

Road traffic safety is one of the major challenges for the future of smart cities and transportation networks. Despite several solutions exist to reduce the number of fatalities and severe accidents happening daily in our roads, this reduction is smaller than expected and new methods and intelligent systems are needed. The emergency Call is an initiative of the European Commission aimed 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.
2020
17-set-2019
21
10
4308
4317
Machine Learning for Severity Classification of Accidents Involving Powered Two Wheelers / Hadjidimitriou, N. S.; Dell'Amico, M.; Lippi, M.; Skiera, A.. - In: IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS. - ISSN 1524-9050. - 21:10(2020), pp. 4308-4317. [10.1109/TITS.2019.2939624]
Hadjidimitriou, N. S.; Dell'Amico, M.; Lippi, M.; Skiera, A.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1215221
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