Work traffic accidents are an issue both in Spain and all over the world, and specific evidence on commuting accidents is scarce. Even though both industrial safety and welfare have been improved during the last decades, the commuting accidents rate is growing worldwide. Purpose: The aim of this study was to examine and describe the characteristics of commuting traffic crashes of Spanish professional drivers. Materials and methods: For this cross-sectional study, commuting accidents suffered by drivers during the last 12 years were analyzed. Crossed and heatmap-based analyses were performed in order to establish patterns and driver-based differences among commuting crashes. Results: Commuting crashes’ features were found to be associated with demographic and job-related variables of professional drivers. Drivers’ gender, time slots (peak/off-peak hours), together with the specific hour of the event, explained different trends in the accidentś severity and characteristics. Conclusions: The results of this study suggest that commuting accidents involving professional drivers differ in demographic and situational issues from general and on-duty professional driverś traffic crashes. Also, and since in Spain commuting crashes are occupational accidents, more numerous and better actions should be taken in this regard, especially considering the association of professional drivers’ accidents with fatigue and shift-working.

Background: The accumulated evidence has shown how professional drivers are, in psychosocial terms, among the most vulnerable workforces, and how their crashes (some of them preceded by stressful working conditions) constitute both an occupational and public health concern. However, there is a clear lack of validated tools for measuring stress and other key hazardous issues affecting transport workers, and most of the existing ones, frequently generic, do not fully consider the specific features that properly describe the work environment of professional driving. This study assessed the psychometric properties, convergent validity and consistency of two measures used for researching occupational stress among professional drivers: the Siegrist’s ERI (Effort-Reward Imbalance Inventory) and Karasek’s JCQ (Job Content Questionnaire). Methods: We examined the data collected from 726 Spanish professional drivers. Analyses were performed using Structural Equation Models, thus obtaining basic psychometric properties of both measures and an optimized structure for the instruments, in addition to testing their convergent validity. Results: The results suggest that the abbreviated versions of ERI (10 items) and JCQ (20 items) have clear dimensional structures, high factorial weights, internal consistency and an improved fit to the task’s dynamics and hazards, commonly faced by of professional drivers; a short set of items with low psychometrical adjustment was excluded, and the root structure of the questionnaires was kept. Conclusions: This study supports the value and reliability of ERI-10 and JCQ-20 for measuring job stress among professional drivers. Also, there is a high consistency between both measures of stress, even though they belong to different theoretical conceptions of the phenomenon. In practical settings, these instruments can be useful for occupational researchers and practitioners studying stress-related issues from the perspective of human factors.

Although fully automated vehicles (SAE level 5) are expected to acquire a major relevance for transportation dynamics by the next few years, the number of studies addressing their perceived benefits from the perspective of human factors remains substantially limited. This study aimed, firstly, to assess the relationships among drivers’ demographic factors, their assessment of five key features of automated vehicles (i.e., increased connectivity, reduced driving demands, fuel and trip-related efficiency, and safety improvements), and their intention to use them, and secondly, to test the predictive role of the feature’ valuations over usage intention, focusing on gender as a key differentiating factor. For this cross-sectional research, the data gathered from a sample of 856 licensed drivers (49.4% females, 50.6% males; M = 40.05 years), responding to an electronic survey, was analyzed. Demographic, driving-related data, and attitudinal factors were comparatively analyzed through robust tests and a bias-corrected Multi-Group Structural Equation Modeling (MGSEM) approach. Findings from this work suggest that drivers’ assessment of these AV features keep a significant set of multivariate relationships to their usage intention in the future. Additionally, and even though there are some few structural similarities, drivers’ intention to use an AV can be differentially explained according to their gender. So far, this research constitutes a first approximation to the intention of using AVs from a MGSEM gender-based approach, being these results of potential interest for researchers and practitioners from different fields, including automotive design, transport planning and road safety.