A paper titled “Modelling self-reported driver perspectives and fatigued driving via deep learning” authored by Alexandros Zoupos, Apostolos Ziakopoulos and George Yannis is published in Traffic Safety Research.  A binary logistic regression model was trained to provide causal insights on which variables affect the likelihood that a driver engaged in driving while fatigued, whereas a Deep Neural Network (DNN) was subsequently trained on the data, slightly outperforming the binary logistic model. Results demonstrate that drivers reporting driving under the influence of drugs, fatigue, or alcohol, as well as speeding, safety, and texting while driving or drivers who were more acceptable of fatigued driving were more likely to have recently driven while fatigued, and from the results of this paper it was concluded that declared fatigued driving behavior can be predicted from questionnaire data, providing new insights to fatigue detection.