Unfolding the dynamics of driving behavior

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Unfolding the dynamics of driving behavior

a machine learning analysis from Germany and Belgium

Journal Article (2024)

Author(s)

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 - Safety and Security Science)

Christelle Al Haddad (Technische Universität München)

Md Rakibul Alam (Technische Universität München)

Constantinos Antoniou (Technische Universität München)

Eleonora Papadimitriou (TU Delft - Safety and Security Science)

Tom Brijs (Transportation Research Institute (IMOB))

George Yannis (National Technical University of Athens)

Research Group

Safety and Security Science

Machine learning Neural network Driving behavior Long-short-term-memory network (LSTM) On-road field trials

DOI related publication

https://doi.org/10.1186/s12544-024-00655-z Final published version

To reference this document use

https://resolver.tudelft.nl/uuid:0c335015-ff7e-4896-b865-e93a04923bc7

More Info

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Publication Year

2024

Language

English

Research Group

Safety and Security Science

Issue number

1

Volume number

16

Article number

40

Downloads counter

238

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.