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

Autor:	Stella Roussou, Eva Michelaraki, Christos Katrakazas, Amir Pooyan Afghari, Christelle Al Haddad, Md Rakibul Alam, Constantinos Antoniou, Eleonora Papadimitriou, Tom Brijs, George Yannis
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	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
Zdroj:	European Transport Research Review, Vol 16, Iss 1, Pp 1-13 (2024)
Druh dokumentu:	article
ISSN:	1866-8887
DOI:	10.1186/s12544-024-00655-z
Popis:	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.
Databáze:	Directory of Open Access Journals
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