Investigating the motivational factors influencing drivers intentions to unsafe driving behaviours: Speeding and overtaking violations

Achieving road safety depends on driver attitudes and behaviours in handling the vehicle on roads. The availability of good road, improvement of vehicle designs and drivers experience lead to reduction in crashes but not prevention of crashes. The study aims to predict the drivers’ intentions towards speeding and overtaking violations when under the influence of motivational factors using belief measure of TPB and DBQ variables. To achieve this, questionnaires were randomly administered to a sample of Ghanaian drivers (N = 354) who held valid driving licenses. This study applied regression techniques. The result shows that the components of TPB and DBQ variables were able to predict drivers’ intentions towards speeding and overtaking violations. The study further shows that components of TPB made larger contributions to the prediction of divers’ intentions to speeding and overtaking than the DBQ. Further analysis revealed that, in the prediction of drivers’ intentions, speeding attitude was the most frequent violations compared to overtaking. The drivers tend to involved in overtaking violations when they perceived the driving motivations would enhance the performance of the behaviour. Additionally, control belief has been the strongest predictor of drivers’ intentions under the influence of motivations to speeding and overtaking violations. It appeared that the drivers who intended to involve in speeding and overtaking violations had strong beliefs in the factors and are more likely to violate based on their beliefs. The practical implications of the findings for the development of interventions to promote road safety and positive changes are also discussed.     

Anger-provoking events in driving diaries: A content analysis

A previously validated coding scheme of offensive driver behaviour was used to content analyse driving diary entries. A new perceived causation coding scheme was also developed to identify victims' perceptions of why events occurred. Inter-rater reliability of the behaviour coding scheme was very good (kappa = .81). The most frequently reported driver behaviours were weaving and cutting, which was included in 33% of all diary entries, followed by slow driving (20%), speeding (13%), perceived hostile driver displays (13%), and tailgating (11%). These results were contrasted with those of the previous applications of the coding scheme. Assessed independently across all diary entries, inter-rater reliability of the coding of three causation categories was within an acceptable range (kappa = .51, .41, .67 for retaliation, time urgency, and negligence, respectively). When applied exclusively to the critical diary entries identified by each participant as the most negative and upsetting, the reliability improved greatly (kappa = .60, .80, and .81). The most frequently reported source of perceived causation was negligence, involved in 15% of all diary entries and 41% of critical events, followed by time urgency (14% of all entries and 29% of critical events) and retaliation (9% of all entries and 11% of critical events). Future research applications of the content coding systems and implications of the findings for driver safety are discussed.     

Modifying driver behaviour with passenger feedback

A feedback programme was employed to help drivers improve their safety behaviour based on the idea that particular types of driver error result from contingency traps as defined by Fuller [Journal of Applied Behaviour Analysis, 24 (1991) 73]. Two drivers and their single respective passengers participated. For each driver, repeated in-car observations were made of four unsafe driving behaviours. Two of these were sequentially targeted in the behavioural intervention that involved the passengers providing informational feedback to their driver. Both drivers showed a marked improvement across the targeted behaviours. The study demonstrated the applicability of behaviour analysis to the traffic domain and the efficacy of individual feedback as a behavioural tool for positive behaviour modification.     

Slow down! Get off that phone! The impact of a high school road safety education program in influencing whether a young person speaks up to a risky driver

Risky driving behaviours such as mobile phone use and speeding remain common among young people, thereby contributing to their over-representation in road crashes. There is much evidence that similar-aged peer passengers can have a negative impact on a young driver’s behaviour; however, there is also research that supports the positive influence that peer passengers can have when they speak up about risky behaviours. Road safety education programs, such as the Royal Automobile Club of Queensland’s (RACQ) Docudrama program, aim to help young people understand the important role they play as peer passengers and, thus, the importance of being willing to speak up to a driver who may be engaging in risky behaviours. Extending on a previous evaluation of the RACQ Docudrama program, the current study uses an Extended Theory of Planned Behaviour framework to evaluate the program’s effectiveness in influencing peer passengers’ intentions and self-reported behaviour in speaking up against risky driving behaviours, including mobile phone use and speeding. A repeated-measures design compared participants’ responses over 3 time-points: Time 1 (one month before participating in the program), Time 2 (immediately after having participated in the program), and Time 3 (three months after program participation). The results indicate that levels of anticipated regret for having not spoken up were higher following participation in the program suggesting that it encouraged young people to consider how they would feel if something was to happen and they, as passengers, had not tried to prevent it by speaking up. Three months after participating in the program, the majority of participants reported that they were positively influenced by the program in terms of engaging in safer behaviours as passengers; although the significant effects regarding improvements were observed only immediately after participating in the program. The implications of such findings are discussed in terms of the need for some type of ‘booster’ post-program participation to capitalise on the positive immediate effects that the program evokes.     

Personality and demographic differences in the perceived risks of potentially timid driving behaviours

Timid driving behaviours can be described as overly cautious and hesitant driving behaviours. Little research has examined behaviours that potentially resemble timid driving and how these behaviours are perceived by other drivers. This is despite the potential for these behaviours to be perceived in a way that leads to angry and aggressive retaliatory behaviours in some drivers (e.g., in anger-prone drivers). We conducted an online survey examining the perceived road safety risks of several behaviours that could potentially result from timid driving and their relationships with driver personality (trait anxiety, trait driving anger), behaviour (anxious driving, angry driving), and demographic (age, gender, annual mileage) background. Drivers (N = 439, M_age = 49.41 ± 5.59 years, aged 18–89) perceived excessively cautious and unpredictable braking behaviours as posing moderate levels of risk. Multiple linear regression analyses also indicated higher perceived risks of slow and excessively cautious behaviours in older, male, and anger prone drivers. No meaningful associations were found between driver characteristics and the risks of unpredictable braking behaviours. These results suggest that safety campaigns to reduce aggressive behaviour may benefit from targeting the perceptions of other drivers’ behaviours.     

Metacognition,rumination and road rage: An examination of driver anger progression and expression in Australia

Road rage is a serious issue impacting road safety on Australian roads. This study investigated the psychological antecedents involved in aggressive driver behaviours. Specifically, the study investigated the relationships between metacognitive beliefs, anger rumination, trait driver anger, and driver aggression; and examined the extent of aggressive behaviours in a sample of Australian drivers (N = 246). An inspection of cross-tabulations indicated that nearly all drivers engaged in verbal driver aggression (94%), approximately half of the drivers engaged in vehicle aggression (53%), and approximately a quarter of the drivers engaged in physical aggression (27%). Driver aggression was more commonly reported from males, open licensed, and middle-aged drivers. Structural path analysis indicated that there was a hierarchical series of relationships present, in that metacognitive beliefs influenced cognitive constructs such as anger rumination and constructive expression. Additionally, such factors were shown to more prominently influence trait driver anger, and the degree to which it was expressed. Bivariate correlations also demonstrated that the relationships carried forward to more specific dimensions of anger rumination and driver aggression styles. The findings of this study may assist to identify the origins of psychological mechanisms involved with anger progression and expression and inform potential interventions for aggressive driving behaviours.     

An online hazard perception training course reduces heavy braking,speeding, and over-revving rates during everyday driving

In essence, driver training involves learning the skills required to drive safely and avoid dangerous events. However, in traditional on-road driver instruction, drivers virtually never accrue experience of the most significant types of events that they are learning to avoid: crashes. One means of providing this experience safely is to present novice drivers with video clips of real crashes, as part of structured learning exercises. A six-week automated online hazard perception training course for drivers, incorporating evidence-based training methods and over a hundred crash clips, was previously found to improve novice drivers’ hazard perception skill, which is known to be an important attribute for avoiding crashes. However, since hazard perception was measured using computer-based methods, the possibility remained that the training effect might not transfer to actual driving. We report a randomized control trial in which novice drivers were recruited to assess everyday driving behaviour objectively, using g-force triggered dashcams and GPS trackers installed in their vehicles. On-road data were collected for a one-month baseline period, and for a further two months after half of the sample completed the hazard perception training course. Drivers who completed the course significantly reduced their rate of heavy-braking events, their speeding behaviour, and their rate of over-revving events. These findings support the proposal that a relatively inexpensive and highly scalable hazard perception training intervention can improve on-road driving behaviour, with the clear potential to impact real-world driver safety.     

Identifying behavioural change among drivers using Long Short-Term Memory recurrent neural networks

Globally, motor vehicle crashes account for over 1.2 million fatalities per year and are the leading cause of death for people aged 15–29 years. The majority of road crashes are caused by human error, with risk heightened among young and novice drivers learning to negotiate the complexities of the road environment. Direct feedback has been shown to have a positive impact on driving behaviour. Methods that could detect behavioural changes and therefore, positively reinforce safer driving during the early stages of driver licensing could have considerable road safety benefit. A new methodology is presented combining in-vehicle telematics technology, providing measurements forming a personalised driver profile, with neural networks to identify changes in driving behaviour. Using Long Short-Term Memory (LSTM) recurrent neural networks, individual drivers are identified based on their pattern of acceleration, deceleration and exceeding the speed limit. After model calibration, new, real-time data of the driver is supplied to the LSTM and, by monitoring prediction performance, one can assess whether a (positive or negative) change in driving behaviour is occurring over time. The paper highlights that the approach is robust to different neural network structures, data selections, calibration settings, and methodologies to select benchmarks for safe and unsafe driving. Presented case studies show additional model applications for investigating changes in driving behaviour among individuals following or during specific events (e.g., receipt of insurance renewal letters) and time periods (e.g., driving during holiday periods). The application of the presented methodology shows potential to form the basis of timely provision of direct feedback to drivers by telematics-based insurers. Such feedback may prevent internalisation of new, risky driving habits contributing to crash risk, potentially reducing deaths and injuries among young drivers as a result.     

Personality, risky driving and accident involvement among Norwegian drivers

Despite the large body of studies, the role of personality in risk research still remains debatable and unclear. The objective of this study was to identify determinants of road user behaviour and accident involvement with the aim of developing effective accident countermeasures. Examining relationships between personality, risky driving and involvement in accidents can open up the possibility of early identification of those more likely to be involved in accidents. The aim is not to influence personality as such, but to develop measures constructed for specific groups. The results are based on a self-completion questionnaire survey carried out among a sample of Norwegian drivers in year 2000 and 2001 (n=2605). The Norwegian Directorate of Public Roads financed the study. The questionnaire included measures of risky driving, accident involvement, normlessness, sensation-seeking, locus of control and driver anger. Results showed that those who scored high on sensation seeking, normlessness and driver anger reported more frequent risky driving compared to those who scored low on these variables. They were more often involved in both speeding and ignorance of traffic rules. Respondents involved in risk taking-behaviour experienced near-accidents and crashes leading to both injuries and material damage more often than other drivers.     

Attention all ‘drivers’: You could be to blame,no matter your behaviour or the level of vehicle automation

Vehicle crashes are one of the leading causes of human deaths worldwide, with crashes predominately attributed to failures of human drivers. Whilst increasing vehicle automation is argued to reduce road crashes via decreased driver involvement, automation also raises concerns around driver blame and stakeholder responsibility. This study examines blame for crash scenarios across four different forms of driver distraction behaviours (phone, sleep, work and driving under the influence), and across four levels of vehicle automation (no automation [manual], partially automated, highly automated, fully automated), using a mixed (qualitative and quantitative) methods approach. Participants (n = 205) were randomised into one of the four levels of vehicle automation and were presented with vignette crash scenarios involving a pedestrian being hit by a vehicle. Results revealed that scenarios varying driver behavior at the time of the crash, had no significant impact on participants’ blame attribution or selected course of action. The qualitative analysis revealed that despite semantic distinction between some driver behaviours, drivers were deemed responsible for the crash. As automation increased, attribution of blame towards the driver decreased, but did not disappear. Blame simultaneously increased towards other stakeholders including the manufacturer and the government, as level of automation increased. These findings mirror that of previous research and further highlight the need for legal frameworks for crashes with automated vehicles, irrespective of driver behaviours.     

Sensitivity to reward and risky driving,risky decision making,and risky health behaviour: A literature review

Young driver road safety has persisted as a global problem for decades, despite copious and diverse intervention. Recently the influence in reward sensitivity, which refers to the individual’s personal sensitivity to rewards, has received attention in health-related research, including more generally through decision making in risky circumstances, and in risky driving behaviour specifically. As such, a literature review and synthesis of the literature regarding reward sensitivity in relation to risky driving, risky decision making, and risky health behaviour, with a focus on literature in which adolescents and young adults feature, is timely. Thirty-one papers were identified, and the literature revealed that young drivers with greater reward sensitivity engage in more risky driving behaviours including speeding, crashes and traffic violations; and that individuals with greater reward sensitivity engage in more risky decision making and other risky health-related behaviours (such as drinking and drug use). Adolescents and young adults exhibit heightened sensitivity to rewards in the presence of peers, which has considerable implications for young driver road safety as research consistently demonstrates that carrying peer passengers places all vehicle occupants at greater risk of being involved in a road crash. Consideration of the influence of reward sensitivity in young driver road safety, and other adolescent/young adult health-related safety, appears to be a promising avenue of intervention, with gain-framed messages more likely to be accepted by young drivers with greater reward sensitivity. Future research in jurisdictions other than Australia and Europe will increase our understanding of the influence of reward sensitivity, and exploration of the differential impacts of reward-responsiveness and fun-seeking specifically are warranted.     

Risky riding behaviours among motorcyclists in Malaysia: A roadside survey

Motorcyclists are over-represented in fatal crashes in Low- and Middle-Income Countries (LMICs). In Malaysia, motorcyclists comprise about 60% of all fatalities in road crashes. However, the prevalence and determinants of risky riding behaviours have been rarely studied in the country. This study aims to investigate motorcycle-related risky riding behaviours at signalised intersections. A total of 72,377 observations were made during six days at six different signalised intersections in Terengganu, Malaysia. Four risky riding behaviours were observed together (i.e. helmet non-use, red-light running, mobile phone use, turn signal neglect) along with additional demographic and contextual factors. The most prevalent risky riding behaviour was turn signal neglect (29.7% of all observations), and the least prevalent was mobile phone while riding (0.2% of all observations). Four logistic regressions were fitted to predict the four risky riding behaviours using the demographic and contextual explanatory factors. The results suggest that helmet non-use increases among female riders, riders wearing industrial uniforms, carrying passengers, riding during the weekend, during off-peak hours, during clear weather, at T-junctions, on multi-lane roads, and on minor road approaches. Red light running increases among female riders, riders wearing industrial uniforms, carrying passengers, during clear weather, at T-junctions, on multi-lane roads, and on major road approaches. Mobile phone use increases among female riders, riders wearing industrial uniforms, carrying passengers, and at cross-junctions. Turn signal neglect increases among male riders, riders not wearing industrial uniforms, solo riders, on weekends, during off-peak hours, during clear weather, on single-lane roads, and on minor road approaches. The findings of this study have significant implications for the development of targeted countermeasures such as education programs and road policing.     

A comparison of computational driver models using naturalistic and test-track data from cyclist-overtaking manoeuvres

The improvement of advanced driver assistance systems (ADAS) and their safety assessment rely on the understanding of scenario-dependent driving behaviours, such as steering to avoid collisions.This study compares driver models that predict when a driver starts steering away to overtake a cyclist on rural roads. The comparison is among four models: a threshold model, an accumulator model, and two models inspired by a proportional-integral and proportional-integral-derivative controller. These models were tested and cross-applied using two different datasets: one from a naturalistic driving (ND) study and one from a test-track (TT) experiment. Two perceptual variables, expansion rate (the horizontal angular expansion rate of the image of the lead road user on the driver’s retina) and inverse tau (the ratio between the image’s expansion rate and its horizontal optical size), were tested as input to the models. A linear cost function is proposed that can obtain the optimal parameters of the models by computationally efficient linear programming.The results show that the models based on inverse tau fitted the data better than the models that included expansion rate. In general, the models fitted the ND data reasonably well, but not as well the TT data. For the ND data, the models including an accumulative component outperformed the threshold model. For the TT data, due to the poorer fit of the models, more analysis is required to determine the merit of the models. The models fitted to TT data captured the overall pattern of steering onsets in the ND data rather well, but with a persistent bias, probably due to the drivers employing a more cautious strategy in TT.The models compared in this paper may support the virtual safety assessment of ADAS so that driver behaviour may be considered in the design and evaluation of new safety systems.     

The effect of driver improvement interventions on crash involvement; has it been under-estimated?

The available evidence suggests that driver improvement interventions (with the aim to increase driver safety, most often by education or training) do not work. The average effect calculated in several meta-analyses is close to, and not always possible to distinguish from, zero, despite total samples sizes of several hundred thousand drivers. However, it is possible that all studies included in these meta-analyses have under-estimated the effect, due to a methodological error; all crashes have been used as dependent variable, instead of only those that the targeted drivers have caused. This error is expected to have considerably deflated the effect sizes, but it is not known how large this effect could be.Using crash data for bus drivers in which culpability had been reliably established, a simple simulation was performed to determine the difference between using culpable and all crashes as an estimator of a safety effect. Using data for six years, calculations were made on single years. About ten percent of culpable crashes in each year were deleted to simulate a safety effect, where after the difference between the original and the simulated variable were calculated, using culpable only and all crashes in parallel. The effects using these two different kinds of datasets could then be compared and the under-estimation effect estimated.Culpable crashes, as compared to all crashes, yielded larger differences in means between time periods, and smaller standard deviations. In between-subjects comparisons resulted in 15–30 percent larger effects for culpable crashes. Within-subjects calculations yielded larger but not as systematic effects.The effect of driver improvement on crash involvement has been systematically under-estimated, as extremely few evaluation studies seem to have taken culpability for crashes into account. Therefore, new evaluations need to be undertaken, and/or old data re-analysed, to calculate a better estimate of the true effect of training and education in driving safety.     

Risky behaviours associated with traffic crashes among app-based motorcycle taxi drivers in Vietnam

Motorcycle taxi services provide an important mobility option for people in developing countries. With the emergence of new transport technologies, app-based motorcycle taxi services have become increasingly popular in recent years. However, little is known about risky driving behaviours and their association with traffic crashes among app-based motorcycle taxi drivers. Through a survey of 602 app-based motorcycle taxi drivers from three cities in Vietnam, this research aimed to investigate the incidence of risky driving behaviours and their association with driver characteristics and traffic crashes. Using a mobile phone while driving was found to be the most common risky driving behaviour (52%) among app-based motorcycle taxi drivers, followed by neglecting to use turn signals (31%), encroaching car lanes (25%), exceeding the speed limit (21%), running red lights (19%) and carrying more than one passenger (17%). In addition, drivers who were students, or those who worked more than 50 h per week, were found to be more likely to engage in risky driving behaviours. Binary logistic regression modelling showed that neglecting to use turn signals, carrying more than one passenger and smoking while driving was significantly associated with self-reported active crash/fall involvement. Turn signal neglect was also associated with active injury crash/fall involvement. The incidence of risky driving behaviours and associated crash involvement was found to be lower among app-based motorcycle taxi drivers compared with regular motorcyclists, yet the findings still highlight the need for ride-hailing firms to deliver improved education and road safety training for their drivers.     

Cognitive function and young drivers: The relationship between driving,attitudes, personality and cognition

Young drivers (aged 17–25 years) are the highest risk age group for driving crashes and are over-represented in car crash statistics in Australia. A relationship between cognitive functioning and driving in older drivers (60 years and older) has been consistently supported in previous literature, however, this relationship has been neglected in research regarding younger drivers. The role of cognitive functioning in young people’s driving was investigated both independently and within a current model of younger peoples driving performance. With young drivers as participants, driving behaviour, attitudes, personality and cognitive functioning were tested and driving performance was operationalised through two measures on a driving simulator, speeding and lane deviations. Cognitive functioning was found to contribute to driving behaviour, along with driving attitudes and personality traits, in accounting for young people’s driving performance. The young drivers who performed better on cognitive functioning tasks engaged in less speeding behaviour and less lane deviation on the driving simulator than those who performed worse on these tasks. This result was found independent of the role of driving behaviour, driving attitudes and personality traits, accounting for unique variance in driving ability.     

Speeding–deliberate violation or involuntary mistake?

The power of two different theoretical frameworks, the theory of planned behaviour (expanded to include moral norm) and the driver behaviour questionnaire, to predict and explain drivers' speeding behaviour are compared and a combined model is suggested. One hundred and seventy-five test drivers, participating in a large-scale ISA-evaluation, answered a questionnaire in spring 2000. Based on the questionnaire data, logged speeding in autumn 2001 was predicted and LISREL-analysis was used for structural equation modelling. According to the results the two frameworks, alone or in combination, could explain between 38 and 53% of self-reported speeding and between 24 and 26% of logged speeding. A combination of the theory of planned behaviour and the driver behaviour questionnaire is presented and implications for the understanding of driver speed control are discussed.     

Bi-dimensional attitudes,attitude accessibility and speeding behaviour

Positive and negative attitude dimensions (i.e., bi-dimensional attitudes) asymmetrically predict behaviour, with the positive dimension being the better predictor than the negative dimension. These findings have been demonstrated using self-reported behaviour measures. In this study, we aimed to test the bi-dimensional attitude-behaviour relationship using objectively measured speeding behaviour derived from a driving simulator and test if the asymmetrical prediction of behaviour from the positive and negative attitude dimensions could be explained by attitude accessibility (how available an attitude is in memory and therefore how readily it is able to guide behaviour). One hundred and six drivers completed online measures of the positive and negative dimensions of their attitudes towards exceeding the speed limit. Response latency measures of the accessibilities of both dimensions were also taken. A driving simulator was used to measure speeding behaviour. Both attitude dimensions independently predicted speeding, with the positive dimension being the stronger predictor. The positive attitude dimension was also more accessible than was the negative dimension. The difference in the accessibilities of the positive and negative attitude dimensions significantly mediated the difference in their predictive validities. The results demonstrate that the positive attitude dimension is the principle predictor of speeding and a reason for this is that it is more accessible in memory than is the negative attitude dimension. Road safety interventions (e.g., education) that aim to reduce speeding and associated traffic crashes might usefully decrease the valence or accessibility of the positive attitude dimension. There would also appear to be scope to reduce speeding by increasing the valence or accessibility of the negative attitude dimension.     

Relationships amongst psychological determinants,risk behaviour,and road crashes of young adolescent pedestrians and cyclists: Implications for road safety education programmes

Road safety education (RSE) assumes that psychological determinants predict risk behaviour, and subsequently that risky road behaviour predicts crash involvement. This study examined the validity of this assumption, by analysing these relationships in two age groups of teen cyclists and pedestrians: a younger age group (12 and 13 years old: n = 1372) and an older age group (14–16 years old: n = 938). A questionnaire was administered at school during regular class consisting of items on demographics, on risk behaviour based on the Generic Error Model System (GEMS), on psychological determinants targeted in RSE programmes, and on crash involvement and near crashes. For the younger age group, the results indicated that the risk behaviours ‘errors’, ‘dangerous play’, and ‘lack of protective behaviour’ predicted self-reported crashes; for the older age group only ‘errors’ were found to be predictive of self-reported crashes and near crashes. Path analyses confirmed that risk behaviour could be predicted from the psychological determinants, sharing respectively 44% of the variance in the younger age group and 34% in the older group. In conclusion, these results confirm the RSE assumption that psychological determinants are associated with a higher frequency of risk behaviours and that the latter are again associated with higher crash frequencies. Just as in earlier studies on adolescent risk behaviour, the GEMS based distinction between errors and violations was not confirmed, suggesting that this distinction – derived from studies on adult car drivers – may not apply to young adolescent cyclists and pedestrians.