Cross-cultural differences in driver aggression,aberrant, and positive driver behaviors

The present study investigated differences in driver aggression for self and others within countries and cultural differences between driver aggression, aberrant, and positive driver behaviors across five countries (Estonia, Greece, Kosovo, Russia, and Turkey). It was predicted that drivers from these five countries differ significantly in terms of driver aggression for self and others, aberrant, and positive driver behaviors. In the study, 743 participants completed the questionnaire package, including the Driver Aggression Indicators Scale (DAIS), the short version of the Driver Behavior Questionnaire (DBQ) with items from the Positive Driver Behavior Scale, and the Demographic Information Form. Paired samples T-tests were conducted to examine the differences in driving aggression between self and others in the five countries. The results indicated that, except for Russian drivers, drivers reported that other drivers had higher driver aggression than themselves. To examine the cross-cultural differences, analysis of covariance (ANCOVA) tests were conducted for the two dimensions of the DAIS (hostile aggression and revenge, and aggressive warnings) and the three dimensions of the DBQ with items from the Positive Driver Behavior Scale (errors, violations, and positive driver behaviors). Cross-country item-based comparisons were then made for the DAIS and the DBQ. The ANCOVA results showed significant differences in both item-based and subscale comparisons. Russian drivers were significantly different from other drivers in terms of hostile aggression and the revenge subscales of the DAIS and positive driver behaviors. Turkish drivers were significantly different from other drivers in several items of the DBQ, including errors and violations. The findings suggest that culture-specific strategies might be conducted for traffic-related anger management. Furthermore, differences in errors and violations among the five countries may be due to cultural differences. Positive driver behaviors might be better promoted in countries by drawing on their distinct cultural road safety strategies.     

Factors explaining driver yielding behaviour towards pedestrians at courtesy crossings

Courtesy crossings are pedestrian crossing facilities where drivers are not legally required to stop for pedestrians, but are encouraged to do so by design elements such as stripes, coloured or textured road surfaces, visual narrowings of the carriageway, and ramps. There is little empirical evidence on drivers' behaviour or guidance on how to design these crossings. This paper analysed data for 937 interactions between drivers and pedestrians at 20 crossings across England, comparing driver yielding behaviour at courtesy crossings and at zebras (marked unsignalised crossings, where drivers are legally required to stop); and identifying the design elements associated with yielding behaviour at courtesy crossings. The analysis controlled for crossing stage; characteristics and situation of pedestrians and vehicles; characteristics of the road and site; and time context. Driver yielding behaviour was analysed for each separate traffic lane that pedestrians need to cross. We found that all four design elements considered (stripes, coloured/textured surface, visual narrowing, and ramps) increased the propensity of the first vehicle to stop and of any vehicle to stop. A before-after analysis then showed that adding a new element (stripes) to a courtesy crossing led to an increase in yielding rates from 20% to 97%. Overall, we found evidence supporting the use of multiple design elements in courtesy crossings. We discuss the implications of these findings for transport policy and urban design.     

The impact of auditory continual feedback on take-overs in Level 3 automated vehicles

ObjectiveTo implement auditory continual feedback into the interface design of a Level 3 automated vehicle and to test whether gaze behavior and reaction times of drivers improved in take-over situations.BackgroundWhen required to assume manual control in take-over situations, drivers of Level 3 automated vehicles are less likely than conventional drivers to spot potential hazards, and their reaction time is longer. Therefore, it is crucial that the interface of Level 3 automated vehicles will be designed to improve drivers’ performance in take-over situations.MethodIn two experiments, participants drove a simulated route in a Level 3 automated vehicle for 35 min with one imminent take-over event. Participants’ gaze behavior and performance in an imminent take-over event were monitored under one of three auditory interface designs: (1) Continual feedback. A system that provides verbal driving-related feedback; (2) Persistent feedback. A system that provides verbal driving-related feedback and a persistent beep; and (3) Chatter feedback. A system that provides verbal non-driving-related feedback. Also, there was a control group without feedback.ResultsUnder all three auditory feedback designs, the number of drivers' on-road glances increased compared to no feedback, but none of the designs shortened reaction time to the imminent event.ConclusionIncreasing the number of on-road glances during automated driving does not necessarily improve drivers’ attention to the road and their reaction times during take-overs.ApplicationPossible implications for the effectiveness of auditory continual feedback should be considered when designing interfaces for Level 3 automated vehicles.     

Proposal of a driver profile classification in relation to risk level in overtaking maneuvers

Traffic crashes are a worldwide problem, and records have indicated frontal collisions have resulted in the most significant number of fatalities. Such a type of crash is frequently caused by improper overtaking of vehicles, which highlights the interference of human factors. Therefore, investigations on driver's risk perception are necessary. This study proposes a classification of driver's risk level through a decision tree using the Classification and Regression Tree (CART) algorithm from data collected from the overtaking maneuvers in a driving simulator. The model obtained by CART algorithm indicated young male drivers are more likely to take risks in overtaking maneuvers. The results were correlated with governmental records and similar studies. In addition, the results showed the potential of the tool for used as a risk level classifier, as well as the validation of the driving simulator in studies associated with human factor behaviours, accident analysis and investigation.     

A diary study of distracted driving behaviours

The introduction and uptake of technology within road vehicles has readily advanced the capabilities and the functions that the driver of a road vehicle has available to them. While this has benefited the drivers’ productivity and entertainment behind the wheel, it has also heightened the possibility for distraction. Research into driver distraction to date has identified how technologies inside the vehicle may be used ineffectively and can compromise the safety of the road transport system. Yet, the factors that drivers state impact on their decision to engage with distracting technologies are less well known. This paper presents the first diary study into driver distraction. The study asked drivers to record all technological distractions that they engaged with across a 4-week period, as well as interactions that they ignored or choose not to engage with. The diary entries include the technologies drivers interacted with and the conditions surrounding this, as well as external factors that drivers cited to influence their decision to interact. Primarily, factors relating to the task itself were found to be of most importance to the drivers’ decision to engage. Differences were also found in how drivers stated they compensated for any engagement with distracting tasks. This has important consequences for the design and integration of technological devices into the vehicle. The novel application of the method offers insights into the naturalistic conditions surrounding drivers’ involvement with distracting technologies. The method is also reviewed on its applicability to the study of driver distraction.     

Driver behaviour and traffic accident involvement among professional urban bus drivers in China

The present research aimed to investigate specific behaviors of professional urban bus drivers in China with the revised Driver Behavior Questionnaire (DBQ), and to define the relationships among various driving behaviors (errors, positives, inattention errors, violations), background information (age, years of driving experience, mobility, etc.), self-assessment, and traffic accident. To achieve such goals, the present research designed a four-dimensional DBQ with 20 items for professional urban bus drivers in China. The KMO coefficient of the whole scale was 0.835, and Bartlett’s test was statistically significant (p < 0.000), which demonstrated strong validity of the scale and should be suitable for factor analysis. The four loading factors accounted for 58.991%. In addition, the reliability and effectiveness of the present 20-item scales were measured. The coefficient of internal consistency-Cronbach’s alpha coefficient was 0.881 and the Cronbach’s Alpha Based on Standardized Items was 0.911. This showed that driving behavior scale of professional bus drivers in China was of high reliability and validity. The analysis showed that among the four factors, positive driving behaviors were significantly associated with errors, inattention errors and violations, respectively. Errors, inattention errors and violations correlated positively with each other. This verified that the correlation coefficient of each factor was medium and high, which indicated that the scale had good difference validity. The test content of the total scale was also highly consistent with the test content of each factor, which indicated that the revised scale had good standard related validity. According to the accident prediction model, the variables that significantly affected the occurrence of traffic accidents were daily driving time, positive driving behavior, SE2 (Driving safety), SE3 (Aberrant driver behaviors). The results showed that professional bus drivers often working overtime were most likely to have accidents. The probability of traffic accidents decreased by 53% for every unit of positive driving behavior frequency of professional bus drivers. The more they felt that they had the tendency of aberrant driving behavior, the more likely they were to have traffic accidents. To summary, the present research contributed to validating and improving the DBQ for professional urban drivers in China.     

Who overtakes more? Explanatory analysis of the characteristics of drivers from low/middle and high-income countries on passing frequency

The passing manoeuvre requires a driver to make decisions and take actions which are dependent on his/her behavioural characteristics and driving ability. However, previous works on passing rate models have exclusively considered geometric and traffic-related variables. This study aims at bridging this gap by investigating the influence of driver profile (i.e., age, gender, nationality - Italian or Iranian - aggressive driving scores, driving exposure) on passing frequency. A driving simulation experiment involving 54 drivers (36 Italians, 18 Iranians) was conducted along a 6.67 km segment of a two-lane rural highway with passing manoeuvres permitted along 25% of its length. Controlled factors included traffic flow and speed in the oncoming direction, and speed in the driver direction, with a total of 27 scenarios assigned to drivers based on a 3³ confounded factorial design. A Poisson regression model was used to investigate the significance of independent variables. Age and gender and their interaction term were significant, thus the effects of age and gender on the number of passing manoeuvres are mutually interdependent. Furthermore, drivers who drive less often completed fewer overtaking manoeuvres. Sensitivity analyses were carried out to understand the magnitude of change in passing frequency attributable to a variation in the explanatory variables. The findings suggest that driver characteristics have a significant effect on passing frequency and should be considered when conducting a performance and safety evaluation of two-lane roads.     

Higher order training supporting competence,autonomy, relatedness (HOT-CAR): A model to improve learner drivers' higher order skills

Approaches to teaching young Learners to drive on-road often lack a strong, overarching theoretical framework. This paper proposes a transtheoretical model to guide instruction of higher-order skills – that are associated with reduced crash risk in young drivers – including established teaching techniques for effective instruction. Learnings from Self-Determination Theory (SDT) and the Goals for Driver Education framework (widely identified as best-practice but not effectively translated into practice) were integrated into the Higher Order Training supporting Competence, Autonomy, Relatedness (HOT-CAR) model. The model was empirically informed from naturalistic observation of professional in-vehicle lessons (n = 110) and a survey of young adolescent drivers (n = 1627). The HOT-CAR model is presented as a three-component framework that recognises learning to drive occurs within a broader system beyond the Learner and the instructor. The foundation of the model identifies the nature of the relationship between the Learner and the instructor that underlies the success of all other elements. The core of the model is the higher-order driving instruction approach including teaching strategies informed by SDT’s needs-supportive model. The context for the model incorporates some of the immediate considerations relevant to instruction; for example, the graduated driver licensing system, automated vehicle features, and peer influence. An example of the application of the model is provided to reflect the immediate practicality of the HOT-CAR model to driver training. This contributes to the limited road safety literature providing a practical solution to Learner driver training that has potential to reduce the crash risk of young novice drivers. Importantly, the model has potential to be applied and adapted to education and other training environments where higher-order skills are a safety–critical component.     

What makes a good driver on public roads and race tracks? An interview study

Future vehicles may drive automatically in a human-like manner or contain systems that monitor human driving ability. Algorithms of these systems must have knowledge of criteria of good and safe driving behavior with regard to different driving styles. In the current study, interviews were conducted with 30 drivers, including driving instructors, engineers, and race drivers. The participants were asked to describe good driving on public roads and race tracks, and in some questions were supported with video material. The results were interpreted with the help of Endsley’s model of situation awareness. The interviews showed that there were clear differences between what was considered good driving on the race track and good driving on the public road, where for the former, the driver must touch the limit of the vehicle, whereas, for the latter, the limit should be avoided. However, in both cases, a good driver was characterized by self-confidence, lack of stress, and not being aggressive. Furthermore, it was mentioned that the driver’s posture and viewing behavior are essential components of good driving, which affect the driver’s prediction of events and execution of maneuvers. The implications of our findings for the development of automation technology are discussed. In particular, we see potential in driver posture estimation and argue that automated vehicles excel in perception but may have difficulty making predictions.