Unfolding the dynamics of driving behavior
a machine learning analysis from Germany and Belgium
Stella Roussou (National Technical University of Athens)
Eva Michelaraki (National Technical University of Athens)
Christos Katrakazas (National Technical University of Athens)
Amir Pooyan Afghari (TU Delft - Technology, Policy and Management)
Christelle Al Haddad (Technische Universität München)
Md Rakibul Alam (Technische Universität München)
Constantinos Antoniou (Technische Universität München)
Eleonora Papadimitriou (TU Delft - Technology, Policy and Management)
Tom Brijs (Transportation Research Institute (IMOB))
George Yannis (National Technical University of Athens)
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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 algorithms considered.
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