How does a driver feel behind the wheel? An exploratory study of drivers' emotions and the effect of their sociodemographic background.

Recent driving behaviour theories with motivational approaches have paid considerable attention to the cognitive characteristics of the driver, especially emotions. The role of drivers' emotions in driving behaviour has been the subject of extensive research, but an important question remains: how do drivers feel in actual driving situations? In this study, we developeda questionnaire comprised of 20 different emotions, conducted an online survey, and collected the responses from 232 participants. Exploratory factor analysis (EFA) indicated a 4-factor structure. In addition, a path diagram and a confirmatory factor analysis (CFA) were constructed, and the factors were semantically named "Unease," "Fear," "Pride," and "Joy," representing two positive and two negative emotions, respectively. Four multiple linear regression (MLR) analyses revealed a statistically significant but low-magnitudeeffect of sociodemographic variables on emotions. The correlations between the emotional factors indicate that two negative emotions and one positive emotion (pride) are highly positively correlated, whereas "joy" is the only emotion negatively associated with negative emotions and has a low correlation with pride. These results imply that sociodemographic variables may only serve as a foundation for forming a driver's contingent emotions, which are later specified by other factors, such as the driving context. Moreover, "joy" may be the only emotional factor that promotes regulation-congruent and favourable driving behaviour; the other three factors may result in various unfavourable driving styles. These findings may be used to determine the needs and specific characteristics of every driver based on their feelings and help design various mitigation measures addressing unfavourable behaviours tailored for different groups of drivers.

A model to identify high crash road segments with the dynamic segmentation method.

Currently, high social and economic costs in addition to physical and mental consequences put road safety among most important issues. This paper aims at presenting a novel approach, capable of identifying the location as well as the length of high crash road segments. It focuses on the location of accidents occurred along the road and their effective regions. In other words, due to applicability and budget limitations in improving safety of road segments, it is not possible to recognize all high crash road segments. Therefore, it is of utmost importance to identify high crash road segments and their real length to be able to prioritize the safety improvement in roads. In this paper, after evaluating deficiencies of the current road segmentation models, different kinds of errors caused by these methods are addressed. One of the main deficiencies of these models is that they can not identify the length of high crash road segments. In this paper, identifying the length of high crash road segments (corresponding to the arrangement of accidents along the road) is achieved by converting accident data to the road response signal of through traffic with a dynamic model based on the wavelet theory. The significant advantage of the presented method is multi-scale segmentation. In other words, this model identifies high crash road segments with different lengths and also it can recognize small segments within long segments. Applying the presented model into a real case for identifying 10-20 percent of high crash road segment showed an improvement of 25-38 percent in relative to the existing methods.