Unfolding the dynamics of driving behavior: a machine learning analysis from Germany and Belgium

Abstract The i-DREAMS project focuses on establishing a framework known as the ‘Safety Tolerance Zone (STZ)’ to ensure drivers operate within safe boundaries. This study compares Long-Short-Term-Memory Networks and shallow Neural Networks to assess participants’ safety levels during i-DREAMS on-road trials. Thirty German drivers’ trips and Forty-Three Belgian drivers were analyzed using these methods, revealing factors contributing to risky behavior. Results indicate i-DREAMS interventions significantly enhance driving behavior, with Neural Networks displaying superior performance among the al... Mehr ...

Verfasser: Stella Roussou
Eva Michelaraki
Christos Katrakazas
Amir Pooyan Afghari
Christelle Al Haddad
Md Rakibul Alam
Constantinos Antoniou
Eleonora Papadimitriou
Tom Brijs
George Yannis
Dokumenttyp: Artikel
Erscheinungsdatum: 2024
Reihe/Periodikum: European Transport Research Review, Vol 16, Iss 1, Pp 1-13 (2024)
Verlag/Hrsg.: SpringerOpen
Schlagwörter: On-road field trials / Driving behavior / Long-short-term-memory network (LSTM) / Neural network / Machine learning / Transportation engineering / TA1001-1280 / Transportation and communications / HE1-9990
Sprache: Englisch
Permalink: https://search.fid-benelux.de/Record/base-28971860
Datenquelle: BASE; Originalkatalog
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Link(s) : https://doi.org/10.1186/s12544-024-00655-z