Gender roles, sex and the expression of driving anger

The present study investigated the validity of the 25-item Driving Anger Expression Inventory (DAX) as well as the role of sex and gender-roles in relation to the expression of driving anger in a sample of 378 French drivers (males = 38%, M = 32.9 years old). Confirmatory Factor Analysis supported the four-factor structure of the 25-item DAX (Adaptive/Constructive Expression; Use of the Vehicle to Express Anger; Verbal Aggressive Expression and Personal Physical Aggressive Expression) and two of the three aggressive factors were found to have significant positive relationships with driving anger, while adaptive/constructive expression was negatively related to driving anger. Use of the vehicle to express anger was not significantly related to crash involvement, but was significantly related to all other crash-related conditions (traffic tickets, loss of concentration, loss of control of the vehicle, near crash). The presence of feminine traits, but not sex, was predictive of adaptive/constructive behaviours, while masculine traits predicted more frequent verbal aggressive expression, use of the vehicle to express anger, personal physical aggressive expression and total aggressive expression. This finding may account for the inconsistent relationship found between driving anger and sex in previous research. This research also found that the 25-item DAX is a valid tool to measure the expression of driving anger and that the endorsement of masculine traits are related to more aggressive forms of driving anger expression

Gender roles, sex and the expression of driving anger

The present study investigated the validity of the 25-item Driving Anger Expression Inventory (DAX) as well as the role of sex and gender-roles in relation to the expression of driving anger in a sample of 378 French drivers (males = 38%, M = 32.9 years old). Confirmatory Factor Analysis supported the four-factor structure of the 25-item DAX (Adaptive/Constructive Expression; Use of the Vehicle to Express Anger; Verbal Aggressive Expression and Personal Physical Aggressive Expression) and two of the three aggressive factors were found to have significant positive relationships with driving anger, while adaptive/constructive expression was negatively related to driving anger. Use of the vehicle to express anger was not significantly related to crash involvement, but was significantly related to all other crash-related conditions (traffic tickets, loss of concentration, loss of control of the vehicle, near crash). The presence of feminine traits, but not sex, was predictive of adaptive/constructive behaviours, while masculine traits predicted more frequent verbal aggressive expression, use of the vehicle to express anger, personal physical aggressive expression and total aggressive expression. This finding may account for the inconsistent relationship found between driving anger and sex in previous research. This research also found that the 25-item DAX is a valid tool to measure the expression of driving anger and that the endorsement of masculine traits are related to more aggressive forms of driving anger expression

Developing an objective indicator of fatigue: An alternative mobile version of the Psychomotor Vigilance Task (m-PVT)

Approximately 20% of the working population report symptoms of feeling fatigued at work. The aim of the study was to investigate whether an alternative mobile version of the ‘gold standard’ Psychomotor Vigilance Task (PVT) could be used to provide an objective indicator of fatigue in staff working in applied safety critical settings such as train driving, hospital staffs, emergency services, law enforcements, etc., using different mobile devices. 26 participants mean age 20 years completed a 25-min reaction time study using an alternative mobile version of the Psychomotor Vigilance Task (m-PVT) that was implemented on either an Apple iPhone 6s Plus or a Samsung Galaxy Tab 4. Participants attended two sessions: a morning and an afternoon session held on two consecutive days counterbalanced. It was found that the iPhone 6s Plus generated both mean speed responses (1/RTs) and mean reaction times (RTs) that were comparable to those observed in the literature while the Galaxy Tab 4 generated significantly lower 1/RTs and slower RTs than those found with the iPhone 6s Plus. Furthermore, it was also found that the iPhone 6s Plus was sensitive enough to detect lower mean speed of responses (1/RTs) and significantly slower mean reaction times (RTs) after 10-min on the m-PVT. In contrast, it was also found that the Galaxy Tab 4 generated mean number of lapses that were significant after 5-min on the m-PVT. These findings seem to indicate that the m-PVT could be used to provide an objective indicator of fatigue in staff working in applied safety critical settings such as train driving, hospital staffs, emergency services, law enforcements, etc

Deep Learning for Automatic EEG Feature Extraction: An Application in Drivers’ Mental Workload Classification

In the pursuit of reducing traffic accidents, drivers’ mental workload (MWL) has been considered as one of the vital aspects. To measure MWL in different driving situations Electroencephalography (EEG) of the drivers has been studied intensely. However, in the literature, mostly, manual analytic methods are applied to extract and select features from the EEG signals to quantify drivers’ MWL. Nevertheless, the amount of time and effort required to perform prevailing feature extraction techniques leverage the need for automated feature extraction techniques. This work investigates deep learning (DL) algorithm to extract and select features from the EEG signals during naturalistic driving situations. Here, to compare the DL based and traditional feature extraction techniques, a number of classifiers have been deployed. Results have shown that the highest value of area under the curve of the receiver operating characteristic (AUC-ROC) is 0.94, achieved using the features extracted by convolutional neural network autoencoder (CNN-AE) and support vector machine. Whereas, using the features extracted by the traditional method, the highest value of AUC-ROC is 0.78 with the multi-layer perceptron. Thus, the outcome of this study shows that the automatic feature extraction techniques based on CNN-AE can outperform the manual techniques in terms of classification accuracy

Factors determining speed management during distracted driving (WhatsApp messaging)

Conceptualization: S.O.P., O.O.T., C.O. and R.G.A.; Methology: S.O.P., O.O.T., C.O. and C.S.; Formal Analyis: S.O.P, O.O.T., M.C.L. and C.S.; Investigation: S.O.P; C.O. and R.G.A.; Writing-Original Draft: S.O.P., O.O.T., M.C.L. and C.S.; Writing-Review & Editing: S.O.P., C.O. and R.G.A.; Supervision: C.O. and R.G.A.; Project administration: R.G.A.; Funding acquisition: R.G.A.The objective of this work was to investigate self-regulation behaviours, particularly speed management, under distracted conditions due to WhatsApp use. We also studied the influence of different environments and driver characteristics, introducing visual status as one of them. Seventy-five drivers were evaluated in a simulator study involving two test sessions under baseline and texting conditions. A cluster analysis was used to identify two groups with different visual capacity .Lastly, possible predictors of speed management were studied developing a generalised linear mixed model. Our results show that drivers reduced their speeds in the presence of more demanding driving conditions; while replying to a WhatsApp message, on curved road segments and when parked cars are present. Driving speed also correlated with driver characteristics such as age or dual task experience and human factors such as self-perceived risk. Finally, although there were significant differences in visual capacity between the two groups identified, the model did not identify visual capacity membership as a significant predictor of speed management. This study could provide a better understanding of the mechanisms drivers use when WhatsApp messaging and which environments and driver conditions influence how speed is managed.Ministry of Economy and Competitiveness (Spain)European Union (EU) FIS2017-85058-RMinistry of Science, Innovation and Universities (Spain) FPU15/0557