Drivers play a significant role in causing serious accidents, which underscores the need for further investigating the human element in order to improve road safety. Given the predominance of the information processing approach in driver’s behavior research field, an important psychological construct, Mental Workload (MWL), has been introduced to study the behavior of drivers. The objective of this paper is to investigate the impact of increased MWL on driver behavior and specifically the changes in driver’s Reaction Time (RT) under increased MWL. The experiment conducted in the driving simulator of the Hellenic Institute of Transport which is part of the Centre for Research and Technology Hellas, with the participation of 56 subjects from all age groups. For the simulation of the increased MWL conditions during driving, a secondary task was employed. To this end, the MIT AgeLab Delayed Digit Recall Task in the 1-back version was adapted for the needs of the present research. The driving scenario included 4 unexpected events, which further increase driver’s MWL. Driving performance was observed and relative parameters were measured as RT on the unexpected events, accidents occurred, and maneuvers performed. Appropriate statistical analysis was performed to examine the difference in the drivers’ RT in the unexpected events. Results demonstrated that higher MWL increased drivers’ RT in the majority of the participants. Furthermore, results also indicated a number of participants that probably employed adaptive control behaviors to counterbalance the increased MWL. Overall, variance on MWL proved to play an important role on driver performance, and thus further research on its consequences on driving performance, and the factors that influence its variance during driving, is imperative.

The objective of this paper is the investigation of the parameters that influence the crash occurrence in the case of an unexpected incident. It draws inspiration from the Safety System Approach, which is built on the position that serious injury and death in transport networks cannot be ethically acceptable. To this end, the research takes into consideration parameters regarding both the road environment and the human factor. A driving simulator experiment takes place with the participation of 56 drivers, and the employment of the appropriate equipment (driving simulator, physiological parameters sensors and software, camera, synchronization software). It investigates the effect of different road and environment parameters (road category and complexity of the environment, visibility conditions), parameters of the driver (age, gender, mental workload), physiological parameters of the driver (heart rate, skin conductance, skin temperature) and the reaction time of the driver to the unexpected incidents, as well as the way of reaction (brake, maneuver, both, none). Furthermore, parameters of the vehicle are taken into consideration (speed, acceleration, headway). Α mixed logistic regression model is being developed for examining the relationship between the under study explanatory variables and the crash occurrence in unexpected incidents.