Author

E. Theodoraki

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Open access Aug 2026

Improving risky driving behavior classification via conditional GAN-augmented hybrid machine learning models: a two-country naturalistic driving study

A hybrid machine learning pipeline that combines Conditional Generative Adversarial Networks (cGANs) for creating realistic synthetic samples with robust classifiers, including an XGBoost-Random Forest ensemble, a Recurrent Neural Network boosted by AdaBoost, and dedicated GAN-driven models is introduced.

E. Theodoraki, P. Koliou, G. Yannis · European Transport Research... · 0 citations

Open access Aug 2026

Evaluating Risky Driving Behavior Using a Naturalistic Driving Dataset: A Hybrid Modelling Approach

Three hybrid machine learning models are developed and evaluated to classify risky driving behavior into three safety levels using naturalistic driving data from Belgium and the UK, supporting the potential of hybrid deep learning models in improving road safety by accurately detecting risky driving behaviors.

E. Theodoraki, Thodoris Garefalakis, Eva Michelaraki et al. · Infrastructures · 0 citations