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Modern road infrastructures are complex networks featuring various elements such as roads, bridges, intersections, and roundabouts, with advanced control systems. Roundabouts have gained prominence as a safer alternative to traditional intersections promoting smoother traffic flow and fewer collisions by guiding traffic in one direction, encouraging reduced speed, and minimizing conflict points.This study investigated driver behavior within roundabouts, focusing on gaze behavior, particularly the left-side mirror and window, under mobile phone distraction conditions. In addition, the effects of roundabout specifications (i.e., number of lanes and size of the central island) and the drivers’ characteristics (i.e., driving experience) were examined.In total, 43 participants, aged 19–56 years including 30 males and 13 females, held a valid driving license, drove through a virtual simulated urban road containing four roundabouts, implemented in a static driving simulator, under baseline condition (no distraction) as well as mobile-induced distraction. Driving simulator data were collected and drivers’ gaze direction and fixation on nine areas of interest were captured with an eye tracker. Results: showed that experienced drivers exhibit a more fixation on the left-side mirror and window and were less distracted. Moreover, the road environment, i.e., the number of cars and the roundabout size, significantly influenced the drivers’ attention. As regards the driving performance, the number of infractions increased when the drivers diverted focus from the left side of the car. The outcomes of the present study might help to improve traffic safety at roundabouts.

Abstract Background About 1.35 million people died in traffic accidents around the world in 2018, make this type of accidents the 8th cause of death in the world. Particularly, in Spain, there were 204,596 traffic accidents during 2016 and 2017, out of which 349,810 drivers were injured. The objective of this study was to understand to what extent seat belt non-use and human factors contribute to drivers injury severity. Methodology The results are based on the information and 2016–17 data provided by the Spain national traffic department “Dirección General de Tráfico” (DGT). The discretization model and Bayesian Networks were developed based on important variables from the literature. These variables were classified as; human factor, demographic factor, conditioning factor and seat belt use. Results The results showed that failure to wear the seat belt by drivers are likely to increase the risk of fatal and sever injury significantly. Moreover, distraction and road type road can contribute to the accident severity.

The aim of this study was to quantify the probability of committing a speed infraction by young drivers and to investigate to what extent listening music could affect young drivers’ emotions as well as their driving performances at the wheel. To achieve this aim, employing Bayesian networks, the study analysed different music styles, in which they resulted in sample drivers’ speed infractions. Gender and drivers’ experiences at the wheel were the other factors, which were taken into account when interpreting the study results. Variables taken into account in this study included type of music whilst driving, gender of drivers, and drivers’ driving experiences. These variables further incorporated into the study of other telemetric variables including acceleration, number of revolutions per minute (RPM) of the engine, brake, traffic, and other types of infractions other than speed, which were considered as dependent variables. A driving simulator was used, and different driving simulation studies were carried out with young people aged between 20 and 28 years. Each participant carried out three simulations by listening to different type of music in each journey. The study defined a conceptual model in which the data were analysed and evaluated mathematically through Bayesian networks. A sensitivity analysis was performed to evaluate the influence of music on driving speed. Based on the different variables, the study further analysed the probability of speed infractions committed by drivers and their adequate speed. The range of frequency probabilities varied between 96.32% (which corresponds to experienced male drivers who do not listen to music) and 79.38% (which corresponds to less-experienced female drivers who listen to music), which resulted in their happiness or aggression.

Introduction. Road traffic injuries are now regarded as the eighth leading cause of death globally. For example, in 2016, 102,362 traffic injuries took place in Spain in which 174,679 drivers suffered injuries. These findings necessitated the development of the current study which focuses on the prime factors that cause this type of injuries. The aim of this study, therefore, is to explore the behavioral factors that entail a higher risk of suffering either a serious or a fatal injury for drivers. Methods. The findings are based on information and data provided by “Dirección General de Tráfico” (DGT) in Spain on traffic injuries that occurred in the year 2016. Reviewing a wide range of the literature, the authors identified the most influential variables and created a model using the Bayesian networks. The variables that define the model are grouped into four factors: vehicle factor, road factor, circumstantial factor and human factor. Results. The results suggest that the principal variables that determine a higher probability of serious or fatal injuries in traffic injuries are: lack of using appropriate safety accessories, high-speed violations, distractions as well as errors. Finally, the research shows the severity probability based on reason of displacement (“in itinere,” on business, or in leisure).

Work stress increasingly affects many workers from different countries. Conditions such as high demand, low social support and low job control are considered predictors of increased stress. With data obtained from the V European Working Conditions Survey (EWCS) a Bayesian network model was made. It provides information on the levels of stress in relation to model demand-control-social support (DCS), differentiating into work situations as they are, self-employed, private and public. To deepen understanding of the interrelationships between these variables sensitivity analysis of individual and overall were performed to check the DCS model assumptions. This model applied in the V EWCS identified the variations and similarities between different work situations, proving that having low levels of demand, together with control and high social support, the likelihood of stress decreases.

Human workers are envisioned to work alongside robots and other intelligent factory modules, and fulfill supervision tasks in future smart factories. Technological developments, during the last few years, in the field of smart factory automation have introduced the concept of cyber-physical systems, which further expanded to cyber-physical production systems. In this context, the role of collaborative robots is significant and depends largely on the advanced capabilities of collision detection, impedance control, and learning new tasks based on artificial intelligence. The system components, collaborative robots, and humans need to communicate for collective decision-making. This requires processing of shared information keeping in consideration the available knowledge, reasoning, and flexible systems that are resilient to the real-time dynamic changes on the industry floor as well as within the communication and computer network infrastructure. This article presents an ontology-based approach to solve industrial scenarios for safety applications in cyber-physical production systems. A case study of an industrial scenario is presented to validate the approach in which visual cues are used to detect and react to dynamic changes in real time. Multiple scenarios are tested for simultaneous detection and prioritization to enhance the learning surface of the intelligent production system with the goal to automate safety-based decisions.