•Goals for Driver Education (GDE) used to evaluate a novice driver education course.•Perceptions of young drivers and driver educators is compared and contrasted.•Misalignment in perceptions about ...what the driver education course focused on.•Novice drivers perceive training is more related to concrete influences on driving.•Indication that driver educators may tend to combine particular components of GDE.
Evaluation research suggests that professional driver education and training has little effect on reducing the crash involvements of young drivers. Driver education and training programs have been criticised as being unsystematically designed and lacking an empirical or theoretical basis. The Goals for Driver Education (GDE) is a theoretical framework developed to address these criticisms. The GDE defines four hierarchical levels of driving behaviours and influences on driving and three individualised Person-specific factors that should be considered in driver education and training programs. The aim of this study was to compare and contrast, in a methodologically rigorous manner, the perceptions that young drivers (n = 22; Mage = 17.80 years, SD = 6.54 months) and driver educators (n = 10; Mage = 54.5 years, SD = 9.21 years) have of a professional driver education and training course they participated in or facilitated. Eight semi-structured focus groups were conducted and the GDE was used to direct the collection and analysis of the data. Young drivers mainly discussed basic driving skills located on the lower levels of the GDE rather than higher level abstract factors that increase risk for young drivers. Driver educators tended to group particular GDE levels and Person-specific factors together when discussing the driving course and paid limited attention to Goals and contexts of driving. Results suggest that driver educators should provide direct instruction regarding the more abstract social and contextual factors that influence driving to potentially increase the efficacy of driver education and training as a safety countermeasure.
•Goals for Driver Education (GDE) was applied to Parent-supervised practice driving.•Parents are likely to outsource learning activities at the lower GDE levels.•Parents concerned about ‘knowledge ...gaps’ and avoiding conflicts with their child.•Parents would welcome specific parent-focused training on driver supervision.
In many jurisdictions with Graduated Driver Licensing systems, such as those in North America, Australia, and New Zealand, parents play an important role in teaching their child how to drive and facilitating their access to formal driver education. This study explored parents’ views on these processes in a theoretically grounded manner using the Goals for Driver Education (GDE) Framework. The GDE framework groups influences on young driver behaviour into four interconnected hierarchical levels: vehicle manoeuvring (Level 1), mastery of traffic situations (Level 2), goals and contexts for driving (Level 3) and goals for life and skills for living (Level 4). Fourteen parents of novice drivers participated in five focus groups held in urban and regional locations in South East Queensland, Australia. A six-step thematic analysis was used consisting of (1) familiarisation with the data, (2) generation of initial codes, (3) searching for themes, (4) reviewing themes, (5) defining and naming themes and (6) producing the report. Parents indicated that they were more likely to outsource the teaching of skills at Levels 1 and 2 of the GDE to professional driving instructors as they were concerned that they would pass bad habits onto their child or they were unaware of the road rules that their child was required to follow. Parents believed that they were able to more effectively teach skills located on Levels 3 and 4 of the GDE framework because they had a greater knowledge of their child when compared with professional educators. The study findings can be used to develop an intervention that would support parents to more effectively supervise learner drivers.
•A systematic review of 49 articles examining hazard perception methodologies.•There was a high level of heterogeneity in the methodologies.•Temporal responses were a consistent measure of HP whilst ...spatial were inconsistent.•There were considerable inconsistencies in the development of the tests.
Poor hazard perception, or the ability to anticipate potentially dangerous road and traffic situations, has been linked to an increased crash risk. Novice and younger road users are typically poorer at hazard perception than experienced and older road users. Road traffic authorities have recognised the importance of hazard perception skills, with the inclusion of a hazard perception test in most Graduated Driver Licensing (GDL) systems.
This review synthesises studies of hazard perception tests in order to determine best practice methodologies that discriminate between novice/younger and experienced/older road users.
Published studies available on PsychInfo, Scopus and Medline as at April 2018 were included in the review. Studies included a hazard perception test methodology and compared non-clinical populations of road users (car drivers, motorcyclists, bicyclists and pedestrians), based on age and experience, or compared methodologies.
49 studies met the inclusion criteria. There was a high degree of heterogeneity in the studies. However all methodologies – video, static image, simulator and real-world test-drive were able to discriminate road user groups categorised by age and/or experience, on at least one measure of hazard perception.
Whilst there was a high level of heterogeneity of studies, video methodology utilising temporal responses (e.g. press a button when detecting the potential hazard) are a consistent measure of hazard perception across road user groups, whereas spatial measures (e.g. locate potential hazard in the scenario) were inconsistent. Staged footage was found to discriminate as well as unstaged footage, with static images also adding valuable information on hazard perception. There were considerable inconsistencies in the categorising of participants based on age and experience, limited application of theoretical frameworks, and a considerable lack of detail regarding post hoc amendments of hazardous scenarios. This research can guide further developments in hazard perception testing that may improve driver licensing and outcomes for road users.
•A comprehensive driving behavior scale was developed for Chinese urban bus drivers.•Identify the driving behaviors of urban bus drivers in China based on DBQ.•Investigate the relationship between ...driver characteristics and traffic accidents.•Put forward suggestions to prevent traffic accidents of 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.
•Pre-licence driver education is mandatory for new drivers in the Australian Capital Territory.•Young novice driver sensation seeking and optimism bias increased between pre and 9–12 months post the ...education course.•Illusionary invulnerability and differential association decreased over this period.•Results indicate that the program has only limited effect on psychosocial factors.
A pre and post (1, 4, and 9–12 months follow up) survey of psychosocial variables was used to examine the effect of a compulsory pre-licence driver education program for drivers aged 16–20 years, in the Australian Capital Territory. While the final survey was collected by telephone, all other surveys were completed online. Two-way mixed ANOVAs revealed that sensation seeking and optimism bias increased over time while illusionary invulnerability and differential association fell. Participants perceived driving as more risky 9–12 months after completing the course compared to their views prior to the course. These results suggest that the program may have a limited effect on these five psychosocial factors over time. Policy makers may need to carefully consider the reasons for providing driver education, the optimal time during the licensing process to provide this education, and the financial and social costs of doing so.
•A novel connected vehicle driving strategy (CVDS) is developed.•Driver compliance is modelled using the prospect theory.•CVDS is integrated with Intelligent Driver Model, i.e., CVDS-IDM.•CVDS-IDM is ...more behaviourally and numerically sound.
This paper incorporates the driver compliance behaviour into a connected vehicle driving strategy (CVDS) that can be integrated with traditional car-following (CF) models to better describe the connected vehicle CF behaviour. Driver compliance, a key human factor for the success of connected vehicles technology, is modelled using a celebrated theory of decision making under risk – the Prospect theory (PT). The reformulated value and weighting functions of PT are consistent with the driver compliance behaviour and also preserve the integral elements of PT. Furthermore, the connected vehicle trajectory data collected from a carefully designed advanced driving simulator experiment are utilised to calibrate CVDS integrated with Intelligent Driver Model (IDM), i.e., CVDS-IDM. The calibration results reveal that drivers in the connected environment drive safely and efficiently. Moreover, the CVDS-IDM can successfully model and predict the CF dynamics of connected vehicles and is more behaviourally and numerically sound than a traditional CF model.
•Driver acceptance was modelled with TAM, TPB, and UTAUT.•TAM, TPB, and UTAUT were able to predict behavioral intention to use an ADAS.•TAM performed the best in explaining 82% of the variability in ...behavioral intention.
Advanced Driver Assistance Systems (ADAS) are intended to enhance driver performance and improve transportation safety. The potential benefits of these technologies, such as reduction in number of crashes, enhancing driver comfort or convenience, decreasing environmental impact, etc., have been acknowledged by transportation safety researchers and federal transportation agencies. Although these systems afford safety advantages, they may also challenge the traditional role of drivers in operating vehicles. Driver acceptance, therefore, is essential for the implementation of these systems into the transportation system. Recognizing the need for research into the factors affecting driver acceptance, this study assessed the utility of the Technology Acceptance Model (TAM), the Theory of Planned Behavior (TPB), and the Unified Theory of Acceptance and Use of Technology (UTAUT) for modelling driver acceptance in terms of Behavioral Intention to use an ADAS. Each of these models propose a set of factors that influence acceptance of a technology. Data collection was done using two approaches: a driving simulator approach and an online survey approach. In both approaches, participants interacted with either a fatigue monitoring system or an adaptive cruise control system combined with a lane-keeping system. Based on their experience, participants responded to several survey questions to indicate their attitude toward using the ADAS and their perception of its usefulness, usability, etc. A sample of 430 surveys were collected for this study. Results found that all the models (TAM, TPB, and UTAUT) can explain driver acceptance with their proposed sets of factors, each explaining 71% or more of the variability in Behavioral Intention. Among the models, TAM was found to perform the best in modelling driver acceptance followed by TPB. The findings of this study confirm that these models can be applied to ADAS technologies and that they provide a basis for understanding driver acceptance.
•Interaction between age effect and task familiarity of professional drivers was examined.•Safety risk of older drivers is lower despite that they have longer brake reaction time.•Older drivers ...intentionally reduce speed and increase time headway to compensate potential risk.•Compensatory behaviors are more effective in reducing the risk of older professional drivers.•It is necessary to promote the safety, health and wellbeing of older professional drivers.
It has been a controversial issue for the effect of ageing population on driving safety. Apparently, drivers’ physiological and cognitive performances deteriorate with age. However, older drivers may compensate for the elevated risk by adjusting their behaviors, known as compensatory strategy. Despite the extensive research on this topic, the compensatory strategy of older professional drivers is not well understood since many studies focused on the differences in compensatory behavior between older and young drivers. Professional drivers tend to be more skillful and able to cope with the unfavorable driving environments, thus presenting a higher capability to mitigate the risk. This study attempts to examine the compensatory behavior and its safety effect amongst older professional drivers, as compared to those of older non-professional drivers, using the driving simulator approach. In the driving simulator experiment, participants were asked to follow a leading vehicle for one hour, and two sudden brake events were presented. 41 (mid-aged and older) drivers completed the driving tests. Each participant was required to complete a car-following test, either under high or low traffic flow conditions. Performance indicators include driving capability (i.e. lateral control, longitudinal control, and brake reaction time) and compensatory behavior (i.e. average speed, and time headway). Additionally, two modified traffic conflict measures: time exposed time-to-collision (TET) and time integrated time-to-collision (TIT) are applied to indicate the traffic conflict risk. The random parameter Tobit models were estimated to measure the association between conflict risk and driver attributes, and random intercept models were used to assess other driving performance indicators. Results show that despite the impaired lateral control performance and longer brake reaction time of older drivers, the likelihood of severe traffic conflict of older drivers is lower than that of mid-aged drivers. Furthermore, though both older professional and older non-professional drivers adopted longer time headway, the reduction in the risk of severe traffic conflict is more profound among the older professional drivers. Such findings suggest that older professional drivers are more capable of mitigating the possible collision risk by adopting the compensatory strategy, as compared to older non-professional drivers. This justifies the existence of compound effect by the compensatory strategy of older driver and better driving skills of professional driver. This research provides useful insights into driver training and management strategies for employers, as older drivers would become a major cohort in the transportation industry.
•The USEA Model predicts user behavioral intention to use of level-1 automation better than level-5 automation.•Perceived usefulness was found to be the strongest predictor of behavioral intention to ...use in both level of technologies.•Perceived safety is significant predictor of behavioral intention to use, which impacts perceived usefulness.•Perceived anxiety has limited impact than in the acceptance model of both level technologies.
The increase in the number of older adult drivers in developed countries has raised safety concerns due to the decline in their sensory, motor, perceptual, and cognitive abilities which can limit their driving capabilities. Their driving safety could be enhanced by the use of modern Automated Driver Assistance Systems (ADASs) and might totally resolved by full driving automation. However, the acceptance of these technologies by older adult drivers is not yet well understood. Thus, this study investigated older adult drivers’ intention to use six ADASs and full driving automation through two questionnaires with 115 and 132 participants respectively in Rhode Island, USA. A four-dimensional model referred to as the USEA model was used for exploring older adult drivers’ technology acceptance. The USEA model included perceived usefulness, perceived safety, perceived ease of use, and perceived anxiety. Path Analysis was applied to evaluate the proposed model. The results of this study identified the important factors in older adult drivers’ intention to use ADASs and full driving automation, which could assist stakeholders in improving technologies for use by older drivers.
•This paper proposed a modified general model and specific model of driving safety field. This modified model is described briefly, which can be applied to cases that are more general in nature.•A ...pre-collision warning algorithm, which used a new index, namely RDSI, to evaluate the driving risk level. The effectiveness is verified by field experiments.•The results indicate that this algorithm shows better efficiency in multi-vehicle scenarios than TTCi, which indicates that the proposed algorithm can be applied to multi-vehicle driving scenarios.
The concept and contents of driving safety field theory were presented in our previous study. On this basis, this study focus on driving safety field theory modeling and application. First, a general model is presented, which considered the driver-vehicle-road interactions. The model include the following three parts: (i) driver behaviors, which are determined by driver characteristics, such as physical-psychological, cognition, driving skill, and traffic violations; (ii) vehicle characteristics, which are determined by velocity vectors and virtual masses of vehicles; (iii) road conditions, which are determined by virtual mass of on road non-moving objects, types of traffic signs, road adhesion coefficient, road slope, road curvature, and visibility. In order to establish concrete functional forms, the specific model is presented. This specific model provides a method for virtual mass calculation and describes the field strength and field force in detail. After that, a driving safety indicator namely DSI is defined. Finally, a vehicle collision warning algorithm based on driving safety field model is presented. This algorithm used a new index namely RDSI to evaluate the driving risk level. The effectiveness of this collision warning algorithm is verified by field experiments.
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