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

Roussou, Stella ; Michelaraki, Eva ; Katrakazas, Christos ; Afghari, Amir Pooyan ; Al Haddad, Christelle ; Alam, Md Rakibul ; Antoniou, Constantinos ; Papadimitriou, Eleonora ; Brijs, Tom ; Yannis, George

European Transport Research Review, 2024-12, Vol.16 (1), p.40, Article 40 [Revista revisada por pares]

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Unfolding the dynamics of driving behavior: a machine learning analysis from Germany and Belgium
Autor: Roussou, Stella ; Michelaraki, Eva ; Katrakazas, Christos ; Afghari, Amir Pooyan ; Al Haddad, Christelle ; Alam, Md Rakibul ; Antoniou, Constantinos ; Papadimitriou, Eleonora ; Brijs, Tom ; Yannis, George
Materias: Algorithms ; Automotive Engineering ; Civil Engineering ; Engineering ; Health aspects ; Machine learning ; Neural networks ; Original Paper ; Regional/Spatial Science ; TRA 2024 Dublin - Transport Transitions (Highlights of the 2024 Transport Research Arena conference) ; Traffic safety ; Transportation ; Transportation planning
Es parte de: European Transport Research Review, 2024-12, Vol.16 (1), p.40, Article 40
Descripción: 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.
Editor: Cham: Springer International Publishing
Idioma: Inglés

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