Academic literature on the topic 'Risky Driving'

This paper proposes a measurement of risk (MOR) method to recognize risky driving behavior based on the trajectory data extracted from surveillance videos. Three types of risky driving behavior are studied in this paper, i.e., speed-unstable driving, serpentine driving, and risky car-following driving. The risky driving behavior recognition model contains an MOR-based risk evaluation model and an MOR threshold selection method. An MOR-based risk evaluation model is established for three types of risky driving behavior based on driving features to quantify collision risk. Then, we propose two methods, i.e., the distribution-based method and the boxplot-based method, to determine the threshold value of the MOR to recognize risky driving behavior. Finally, the trajectory data extracted from UAV videos are used to validate the proposed model. The impact of vehicle types is also taken into consideration in the model. The results show that there are significant differences between threshold values for cars and heavy trucks when performing speed-unstable driving and risky car-following driving. In addition, the difference between the proportion of recognized risky driving behavior in the testing dataset compared with that in the training dataset is limited to less than 3.5%. The recognition accuracy of risky driving behavior with the boxplot- and distribution-based methods are, respectively, 91% and 86%, indicating the validation of the proposed model. The proposed model can be widely applied to risky driving behavior recognition in video-based surveillance systems.

This study evaluated the relationship between drivers’ cognitive biases (i.e., optimism bias, illusion of control) and risky driving behaviour. It also investigated the mediational role of risk perception in the relationship between cognitive biases and self-reported risky driving. The sample included 366 drivers (Mage = 39.13, SD = 13.63 years) who completed scales measuring optimism bias, illusion of control, risk perception, and risky driving behaviour, as well as demographic information. The results showed that risky driving behaviour was negatively predicted by optimism bias and positively predicted by the illusion of control. Further, risk perception negatively correlated with risky behaviour and also mediated the relation between both optimism bias and illusion of control with risky driving. The practical implications of these results for traffic safety and future research are discussed.

Evaluating risks when driving is a valuable method by which to make people better understand their driving behavior, and also provides the basis for improving driving performance. In many existing risk evaluation studies, however, most of the time only the occurrence frequency of risky driving events is considered in the time dimension and fixed weights allocation is adopted when constructing a risk evaluation model. In this study, we develop a driving behavior-based relative risk evaluation model using a nonparametric optimization method, in which both the frequency and the severity level of different risky driving behaviors are taken into account, and the concept of relative risk instead of absolute risk is proposed. In the case study, based on the data from a naturalistic driving experiment, various risky driving behaviors are identified, and the proposed model is applied to assess the overall risk related to the distance travelled by an individual driver during a specific driving segment, relative to other drivers on other segments, and it is further compared with an absolute risk evaluation. The results show that the proposed model is superior in avoiding the absolute risk quantification of all kinds of risky driving behaviors, and meanwhile, a prior knowledge on the contribution of different risky driving behaviors to the overall risk is not required. Such a model has a wide range of application scenarios, and is valuable for feedback research relating to safe driving, for a personalized insurance assessment based on drivers’ behavior, and for the safety evaluation of professional drivers such as ride-hailing drivers.

Truck-related accidents account for a substantial portion of traffic accidents. Risky driving behavior is a main cause of traffic accidents. Understanding the risky driving behavior of truck drivers is therefore important in reducing truck-related accidents. This study aimed to propose and validate a research model that integrated a theory of planned behavior, sensation seeking, and risk perception to explain the risky driving behavior of truck drivers. A total of 471 valid data were collected from Chinese truck drivers in this study. Structural equation modeling and mediation analysis were used to examine the influence of factors in the research model on the risky driving behavior of truck drivers. Results showed that sensation seeking and risk perception of truck drivers were influential in shaping their intention to drive riskily with the mediation of attitude toward risky driving. Risk perception and attitude toward risky driving also had a negative influence and positive influence on the intention, respectively. On the basis of the findings, practical recommendations for reducing the risky driving behavior of truck drivers were provided for concerned parties.

We investigate the effect of teenage driving on mortality and risky behaviors in the United States using a regression discontinuity design. We estimate that total mortality rises by 5.84 deaths per 100,000 (15 percent) at the minimum legal driving age cutoff, driven by an increase in motor vehicle fatalities of 4.92 deaths per 100,000 (44 percent). We also find that poisoning deaths, which are caused primarily by drug overdoses, rise by 0.31 deaths per 100,000 (29 percent) at the cutoff and that this effect is concentrated among females. Our findings show that teenage driving contributes to sex differences in risky drug use behaviors. (JEL I12, J13, J16, R41)

Personality traits such as impulsivity or sensitivity to rewards and punishments have been associated with risky driving behavior, but it is still unclear how brain anatomy is related to these traits as a function of risky driving. In the present study, we explore the neuroanatomical basis of risky driving behavior and how the level of risk-taking influences the relationship between the traits of impulsivity and sensitivity to rewards and punishments and brain gray matter volume. One hundred forty-four participants with different risk-taking tendencies assessed by real-life driving situations underwent MRI. Personality traits were assessed with self-report measures. We observed that the total gray matter volume varied as a function of risky driving tendencies, with higher risk individuals showing lower gray matter volumes. Similar results were found for volumes of brain areas involved in the reward and cognitive control networks, such as the frontotemporal, parietal, limbic, and cerebellar cortices. We have also shown that sensitivity to reward and punishment and impulsivity are differentially related to gray matter volumes as a function of risky driving tendencies. Highly risky individuals show lower absolute correlations with gray matter volumes than less risk-prone individuals. Taken together, our results show that risky drivers differ in the brain structure of the areas involved in reward processing, cognitive control, and behavioral modulation, which may lead to dysfunctional decision-making and riskier driving behavior.

Human errors cause approximately 90 percent of traffic accidents, and drivers with risky driving behaviors are involved in about 52 percent of severe traffic crashes. Driver education using driving simulators has been used extensively to obtain a quantitative evaluation of driving behaviors without causing drivers to be at risk for physical injuries. However, since many driver education programs that use simulators have limits on realistic interactions with surrounding vehicles, they are limited in reducing risky driving behaviors associated with surrounding vehicles. This study introduces surrogate safety measures (SSMs) into simulator-based training in order to evaluate the potential for crashes and to reduce risky driving behaviors in driving situations that include surrounding vehicles. A preliminary experiment was conducted with 31 drivers to analyze whether the SSMs could identify risky driving behaviors. The results showed that 15 SSMs were statistically significant measures to capture risky driving behaviors. This study used simulator-based training with 21 novice drivers, 16 elderly drivers, and 21 commercial drivers to determine whether a simulator-based training program using the SSMs is effective in reducing risky driving behaviors. The risky driving behaviors by novice drivers were reduced significantly with the exception of erratic lane-changing. In the case of elderly drivers, speeding was the only risky driving behavior that was reduced; the others were not reduced because of their difficulty with manipulating the pedals in the driving simulator and their defensive driving. Risky driving behaviors by commercial drivers were reduced overall. The results of this study indicated that the SSMs can be used to enhance drivers’ safety, to evaluate the safety of traffic management strategies as well as to reduce risky driving behaviors in simulator-based training.

Internationally, young novice drivers (aged 17-25 years) are often overrepresented in road-related crash injury and fatality statistics. Compared with older, more experienced drivers, prominent contributors to young driver crash-risk is their lower perceived risk, higher engagement in risky driving behaviours (e.g., speeding), and poorer hazard perception skills. This thesis describes seven studies conducted to address three research aims. The first aim was to model and examine three psychological theories of personality, social–cognitive, and social-learning, to propose a new conceptual framework that explores how young novice drivers perceive driving risk, and whether they choose to engage or not in risky driving behaviours. In studies 1-4, using a sample of 643 young novice drivers (490 females) who held an Australian driver’s licence (Provisional-1, Provisional-2, or Open), models of: i) reinforcement sensitivity, ii) protection motivation, and iii) prototype willingness, were examined. From these models, factors of reward sensitivity, coping appraisal, threat appraisal, driver prototypes, and behavioural willingness predicted young driver perecived risk, and reported risky driving engagement. The second aim was to examine whether a developed and piloted brief hazard perception training session can improve Provisional-1 drivers (aged 17-25years) overall hazard perception knowledge, identification, response and handling to road user related driving hazards using a driving simulator. Educational, passive and active training methods incorporating a number of established behavioural change techniques were employed, while elements of process and product evaluation were undertaken. Also examined was whether any training group differences persisted at 2-3 week follow-up. In Study 5 a sample of 23 drivers (n=7 Provisional-1, n=7 Provisional-2, and n=9 Open licence) piloted and validated the training methods and hazard perception outcome measures. In Study 6, a brief training session was implemented with a sample of 52 (18 male) Provisional-1 licence drivers aged 17-25 years. Participants were equally randomised to four training conditions (pamphlet, passive, active, and no-training). Participants who received training significantly outperformed the no-training participants in hazard perception identification, response, and handling of hazards, in the simulator hazard perception test. When assessed again at a follow-up session (n=40), support was found for participants who received training that was higher in interactivity (passive and active training) outperforming the no-training participants and participants who only received an educational pamphlet in the hazard perception tests (static and simulator). The third aim was to evaluate whether the brief hazard perception training session’s key objectives were met using feedback from participants who completed the training session, and to identify whether factors identified from the new conceptual model (research aim 1) were associated with hazard perception performance. Study 7 evaluated the training session using feedback from drivers who participated in study 5 (N=52), and explored initial associations between protection motivation theory and the prototype willingness model for perceived risk, reported risky driving engagement, and hazard perception. This approach sought to expand the literature by examining factors associated with these three prominent young driver crash risk models to better adjust and address such factors in future training programs targeting safety outcomes. While potential correlates of hazard knowledge were examined, only three were found to share significant relationships: coping appraisal, previous traffic violations, and risky driver prototype similarity. From participant feedback, three key areas of learning were identified by participants: increased knowledge, awareness of new hazards, and greater awareness of driving laws and rules. A majority of participants in the training conditions also indicated that after the training session their understanding of driving hazard perception and driving related hazards had improved. Across training conditions, 91.5% (n=43) of participants who completed the evaluation measure indicated that they would recommend the session for other Provisional-1 drivers. Examining potential underlying influences for why young drivers are overrepresented in international injury and death tolls is important for road safety research and practice (e.g., driver-oriented interventions). Highlighted in this thesis are factors from the conceptual models that could be amenable to change in influencing young driver decision-making, perceived risk, and risky driving engagement, in addition to a brief training session that showed evidence of hazard perception improvement. These results may contribute to improved road safety initiatives, preventive strategies and interventions that focus on this vulnerable driver demographic.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Applied Psychology
Griffith Health
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Alcohol intoxication represents one situation an individual might increase their amount of risk taking when driving. This dissertation is comprised of three studies that investigate the mechanisms by which alcohol increases driver risk-taking. Study 1 examined the effect of alcohol on driver risk-taking using a proxemics approach. The study also tested whether alcohol-induced increases in risky driving co-occurred with pronounced impairment in the driver’s skill. The study also examined whether the most disinhibited drivers were also the riskiest. Indeed, alcohol increased driver risk-taking and impaired driving skill. The study also revealed risky driving can be dissociable from impairing effects on driver skill and that poor inhibitory control is selectively related to elevated risky driving. Studies 2 and 3 built on this work by addressing whether the apparent dissociation between behavioral measures of driver risk and skill was mediated by perceptions the drivers held. While maintaining the distinction between driver risk and skill, Study 2 tested the relationship between drivers’ BAC estimations and their tendency to take risks on the roadway. Drivers who estimated their BAC to be lower were the riskiest drivers following both alcohol and placebo. Study 3 addressed whether risky driving could be increased by environmental factors that shape perceptions the driver holds. There is evidence post-licensure training programs might inadvertently generate overconfidence in drivers’ perceived ability to operate a motor vehicle and thus fail to perceive dangers normally associated with risky driving behavior. To test this hypothesis, twenty-four drivers received either advanced skill training or no training in a driving simulator. Drivers who received skill training showed increased risky driving under alcohol whereas those who received no training tended to decrease their risk taking. Trained drivers also self-reported more confidence in their driving ability. Taken together, these studies represent a large step towards the betterment of laboratory-based models of driving behavior. The work highlights the importance of distinguishing between driver risk-taking and driving skill. The studies also identified that drivers’ personal beliefs influence alcohol-induced risky driving; this suggests training programs focused on correcting drivers’ misconceptions might be most efficacious in reducing their risk taking on the roadway.

The overall aim of the study was to examine relapse among participants in the DWI Prevention Programme and those who get prison sentence after driving when influenced by alcohol and to examine the participants’ attitudes towards drunk – driving, risk behavior and traffic safety. A direct evaluation of the sentence and penal accomplishment is also examined. The sample of the survey study (see article 1) was 44 from the DWI – sample and 44 from the prison – sample that completed a questionnaire answering about their attitudes towards drunk – driving, risk behavior and traffic safety. The results presented in article 2 are based on transcripts of criminal convicts that participated in the DWI Prevention Programme during the period of 1998 – 2002 in the Salten District ( n = 68) and a sample of convicts to an unconditional sentence for drunk - driving in the same time periode (n = 112). The 1st analysis revealed that the DWI – sample had more ideal attitudes towards drunk – driving, risk behavior and traffic safety. There were also significant differences in how they evaluated their sentence and penal accomplishment. The DWI - sample were generally more satisfied with the penal accomplishment, the way they was treated and how the relationships around them were. They were also more satisfied with the contents of the penal accomplishment. Multivariate analysis, Kaplan – Meier and Cox regression was used in the 2nd analysis calculating if there were significant differences between the samples, survival time and to investigate effects of several variables upon the time a specified event takes to happen. In this study the relapse time was shorter for men than for women and the youngest age – groups had a shorter relapse time than the oldest age – groups. The Kaplan – Meier plot revealed that the prison – group have a shorter relapse time compared to the DWI – group. Based on the results of the two articles we can conclude that the DWI Prevention Programme had a very good effect on the participants compared to those who get traditional prison – sentence. The participants in the programme had the most ideal attitudes and the longest survival time after participating. When it comes to survival time among gender and age, women and the older age – groups had the longest survival time.

The Dula Dangerous Driving Index (DDDI) was created to measure drivers' self-reported propensity to drive dangerously (Dula & Ballard, in press). In the early stages of development, the DDDI and each of its subscales (Dangerous Driving Total, Aggressive Driving, Negative Emotional Driving, and Risky Driving) were found to have strong internal reliability (alphas from .83 to .92), and there was evidence of construct validity. In Study One, the alpha coefficient of .91 for the DDDI Total scale indicated excellent internal reliability for the measure and good internal reliability was demonstrated for its subscales with coefficient alphas equal to .81 for the DDDI Risky Driving subscale, .79 for the DDDI Negative Emotional subscale, and the DDDI Aggressive Driving subscale. Additionally, convergent and divergent validity was shown for the DDDI, but evidence was weaker for the validity of the separate subscales. Factor analysis demonstrated that the DDDI seemed to measure a unitary construct. In Study Two, coefficients of stability were generated from a four-week test-retest procedure, which were .76 for the DDDI Risky Driving subscale, .68 for the DDDI Negative Emotional subscale, .55 for the DDDI Aggressive Driving subscale, and .73 for the DDDI Total. In Study Three, the percentage of variance accounted for in criterion variables by different models ranged from 13.6% to 47.7%, where the DDDI Negative Emotional and DDDI Total scales frequently accounted for large portions of variance. In Study Four, the percent of variance accounted for in criterion variables by different models ranged from 22.0% to 65.6%, where some of the DDDI scales were regularly found to account for significant variance. Thus, it was concluded that the DDDI is a measure with high levels of internal reliability and reasonable stability across time, and that face, construct, and predictive validity was demonstrated. However, the evidence in support of the present division of subscales was weak, though present. Therefore, should further data fail to produce more substantial evidence for the validity of the DDDI subscales, a singular dangerous driving measure would be warranted, and the number of items should be shortened as guided by results from factorial analysis.
Ph. D.

Road trauma is recognized as a major public health problem worldwide (particularly for younger drivers), and risky driving has been identified as an important contributor to road crashes. It is often assumed that similar factors influence all risky driving behaviours, although direct and systematic examination of the differences between risky driving behaviours in terms of precipitating factors is lacking. The present thesis sought to undertake a systematic investigation of relevant factors in the prediction of four key risky driving behaviours (speeding, drink-driving, driving while fatigued, and not wearing seat belts). Four versions of a Risky Driving Questionnaire were developed to assess beliefs, personality factors and behavioural intentions, in relation to each of the four behaviours. Four versions of the Implicit Association Test were developed to assess attitudes toward each of the four behaviours, without reliance on self-report (in terms of the relative strength of pairs of associations). Data were collected from a student sample (N=215: Study 1), as well as urban (N=587) and rural (N=422) general population samples (Study 2), and regression models were examined for each of the four behaviours, with interaction terms to assess moderations involving perceived risk. Mediations involving gender were also assessed. Results indicate that different risky driving behaviours are predicted by different factors. For example, in the urban sample, speeding was predicted by driver anger and illusory invulnerability, drink driving was predicted by peer influence, driving while fatigued was predicted by the perceived benefits of not driving while fatigued, and not wearing seat belts was predicted by the (sensation seeking x illusory invulnerability) interaction. Results also suggest that different predictors of risky driving behaviours are relevant for different driver populations. For example, speeding was predicted by authority rebellion in the urban sample, and by sensation seeking in the rural sample. Observed moderations of perceived risk suggest that relationships between perceived risk and risky driving may differ for males versus females, and for low versus high sensation seekers. Findings suggest that future road safety interventions should be based on research of the determinants of individual risky driving behaviours, and in specific driver populations.

Introduction: This study moves to examine the prevalence of risky driving behaviors and deficiency of communication pertinent to topics related to safe driving among adolescents in central Appalachia. Even though plenty of research displays the consequences associated with driving, drivers continue to take part in risky behaviors such as texting while driving, riding in a vehicle without wearing a seatbelt, and riding in a vehicle with someone who has been drinking. Methods: Participants of the study included three high schools in Southwest Virginia consisting of 385 11th and 12th grade students. Students were administered a paper-pencil survey either during homeroom or last period with questions taken from the Youth Risk Behavior Surveillance Survey. Results: The results of the study indicate the frequent occurrence of young drivers engaging in risky driving behaviors associated with texting while driving and not wearing a seatbelt as both passenger and driver in a vehicle. Additionally, the results of the study indicate that there is a lack of healthcare provider communication related to risks associated with driving. This information is crucial because the data demonstrates the missed opportunity to provide better education to adolescents on how they can prevent harm to their lives or the lives of other citizens while driving. Conclusion: After analyzing these results, it becomes evident that more education about safe driving behaviors is crucial for benefiting the young drivers of this region. Because road injury is the leading cause of death among adolescents, it is paramount to provide educational resources to young drivers to decrease the impact of injuries and deaths related to risky driving behaviors. There resides a missed opportunity to educate adolescents about behaviors that may risk their lives or those of their peers and loved ones. In addition, researchers can conduct further studies to examine effective safe driving education programs to decrease the risk behaviors commonly engaged in by adolescent drivers.

The present study aimed the adaptation of the Driving Log, a questionnaire that assesses aggressive and risky driving behaviors in a day by day basis, with 395 Spanish participants. Confirmatory factor analysis showed that the questionnaire fitted properly in two correlated factors, labeled as Risky Driving and Aggressive Driving. Subsequent analyses showed that the number of drives is significantly associated to Risky Driving, while the number of occasions in which anger is experimented correlated with Risky Driving as well as Aggressive Driving. Other findings suggest that men behave in a more risky and aggressive mannerthan women. Young people follow this same tendency in comparison to their elders.
El presente estudio tuvo como objetivo la adaptación del Driving Log, un cuestionario que valora los comportamientos agresivos y arriesgados al volante, en una muestra española de 395 personas. El análisis factorial confirmatorio mostró que el cuestionario ajustaba satisfactoriamente en dos factores, etiquetados como Conducción Arriesgada y Conducción Agresiva. Los análisis posteriores mostraron que el número de trayectos realizados se asoció significativamente a la Conducción Arriesgada, mientras que el número de veces en que se experimentó ira lo hizo tanto con la Conducción Arriesgada como con la Conducción Agresiva. Igualmente, se vio que los hombres se comportaban de forma más arriesgada y agresiva que las mujeres, y que los jóvenes lo hacían en mayor grado que los mayores.

Despite the tremendous efforts made in recent years towards improving overall health status of adolescents, road traffic crashes remain a global problem worldwide among teen drivers. It is well established that the first few months of independent driving are the most dangerous. Indeed, crash risk among adolescent drivers is particularly high during the early months of independent driving, after which it starts to rapidly decrease over a period of over a period of years. Hypotheses for this decline have focused on Some researchers have hypothesized accumulation of driving experience, maturation, and increasing self-regulation. However, the mechanisms by which they interact to decrease teen crash risk in few months are not well understood. Additionally, safety researchers are engaged in a longstanding quest to fundamentally improve teen driving. To that end, increasing number of studies have been striving for solutions. Understanding the processes underlying patterns in teen crash risk and catalyze effective teen driving interventions can benefit from techniques for modelling complexity. The goal of this project was to develop a model that provides initial insights into the mechanisms underlying adolescent risky driving patterns over time. The purpose of the modeling is to investigate how much faster the early improvement of teen risky driving could be with interventions. This study utilized naturalistic driving data derived from a clinical trial study. A sample of newly-licensed teen drivers and at least one of their parents was recruited from high schools in Iowa and randomly assigned to one out of three groups: control group, feedback group, and feedback plus parent communication group. Each participant's vehicle was equipped with an event triggered video recording system to gather data on near-crashes and crashes as well as their proxies denoted risky driving events. The video recording system was installed in the vehicles of the control group only for data collection purposes. For the feedback intervention group, teen drivers received an immediate feedback via blinking of LED lights on the in-vehicle video system when a driving error occurred. In addition, each teen and their parent in this feedback group received a weekly report card that summarized the types of driving errors made by the teen and provided video clips of those errors. The feedback plus parent communication group was exposed to the feedback intervention described above plus communication strategies for discussing safe driving with teens. The video recording system was also used to collect data on mileage, driver behaviors (eg. traffic violations, cell phone use), and traffic conditions (eg. snow, rain). The first aim of this study thoroughly investigated heterogeneity in driving outcomes within the population of teen drivers. Results showed two distinct risky driving trajectories, including one inverted U-shaped pattern (initial increase in risky driving followed by a steady decrease) and one relatively constant pattern over time. Risk-taking behavior trajectories were found to follow the same patterns as risky driving. The study also identified two groups of teens with respect to amount of driving: one group has a linear increase in the amount of driving and the second group has an upward U-shaped pattern. Teens classified in the high risk-taking behavior group are more likely to be in the high risky driving group whereas the teens classified in the low risk-taking behavior are more likely to be in the low risky driving group. Results showed that males are more likely to be in high risky driving and high risk-taking behavior groups compared to females. The second aim of this project was to develop a dynamic model of teen risky driving and use this framework as a guide to leverage an understanding of the dynamic process underlying patterns in teen risky driving over time. The analysis suggests that the natural risky driving behavior (absent intervention) is slow improvement followed by faster improvement, and finally a plateau: that is, S-shaped decline in errors. The results showed that a model that includes cumulative miles driven and recent risky driving events as stock variables and their feedback is capable of explaining the dynamics of teen risky driving over time. The analysis suggests the existence of a reinforcing loop and two negative feedbacks. The reinforcing loop arises from a decline in recent events leading to a faster increase in driving; this leads to a faster accumulation of driving and thus a greater decrease in driving error rate; the decrease in driving error rate leads to a further decline in recent events via a slow replenishing of the stock “recent events”. The first negative feedback is from recent events to amount of driving. By this feedback mechanism, more recent events (or memories of events) lead to less driving, and thus slow accumulation of driving experience (cumulative miles driven). The second feedback in the model is from recent events to event rate. A greater number of recent events (or memories of events) leads to a decrease in event rate perhaps via corrective actions taken by the teen driver. Thus, more recent events (or memories of events) lead to a decrease in event rate, but slow accumulation of experience via less driving. The results highlight that variations of individualized trends in driving event rate and monthly driving are more likely due to significant variations in the stock “cumulative miles driven” and the stock “recent events”. Variations in these stocks are influenced by initial event rates and driving need. The methodological approach provides an explanation for the peak in crash rates during the latter months post-licensure rather than the first month, which was not fully understood. The third, and final, aim of this teen driving dynamic model project sought to simulate driver feedback intervention and conduct its cost effectiveness analysis. To examine the impact of driver feedback intervention and its tradeoffs, a previous version of the model was extended to create a model that allows the simulation of the intervention and the comparison between its expected costs and benefits. The analysis suggested that the simulated intervention data are comparable to data from actual feedback intervention group. The simulation results indicate significant differences in the period over which the intervention is needed. While the intervention is economically beneficial for some drivers, it is worthless for others. The model also suggests the need of combining several interventions for some drivers for a faster improvement in risky driving. This research offered initial insights for understanding risky driving patterns, risk-taking behavior, and amount of driving among adolescent drivers and can be helpful when designing teen driving interventions, as the different trajectories may represent unique strata of crash risk level. The dynamic model developed can be used to design and evaluate teen driving interventions in order to identify key leverage points to guide policy and direct the optimum combination of prevention strategies.

Young novice drivers - that is, drivers aged 16-25 years who are relatively inexperienced in driving on the road and have a novice (Learner, Provisional) driver's licence - have been overrepresented in car crash, injury and fatality statistics around the world for decades. There are numerous persistent characteristics evident in young novice driver crashes, fatalities and offences, including variables relating to the young driver themselves, broader social influences which include their passengers, the car they drive, and when and how they drive, and their risky driving behaviour in particular. Moreover, there are a range of psychosocial factors influencing the behaviour of young novice drivers, including the social influences of parents and peers, and person-related factors such as age-related factors, attitudes, and sensation seeking. Historically, a range of approaches have been developed to manage the risky driving behaviour of young novice drivers. Traditional measures predominantly relying upon education have had limited success in regulating the risky driving behaviour of the young novice driver. In contrast, interventions such as graduated driver licensing (GDL) which acknowledges young novice drivers' limitations - principally pertaining to their chronological and developmental age, and their driving inexperience - have shown to be effective in ameliorating this pervasive public health problem. In practice, GDL is a risk management tool that is designed to reduce driving at risky times (e.g., at night) or in risky driving conditions (e.g., with passengers), while still enabling novice drivers to obtain experience. In this regard, the GDL program in Queensland, Australia, was considerably enhanced in July 2007, and major additions to the program include mandated Learner practice of 100 hours recorded in a logbook, and passenger limits during night driving in the Provisional phase. Road safety researchers have also continued to consider the influential role played by the young driver's psychosocial characteristics, including psychological traits and states. In addition, whilst the majority of road safety user research is epidemiological in nature, contemporary road safety research is increasingly applying psychological and criminological theories. Importantly, such theories not only can guide young novice driver research, they can also inform the development and evaluation of countermeasures targeting their risky driving behaviour. The research is thus designed to explore the self-reported behaviours - and the personal, psychosocial, and structural influences upon the behaviours - of young novice drivers This thesis incorporates three stages of predominantly quantitative research to undertake a comprehensive investigation of the risky driving behaviour of young novices. Risky driving behaviour increases the likelihood of the young novice driver being involved in a crash which may harm themselves or other road users, and deliberate risky driving such as driving in excess of the posted speed limits is the focus of the program of research. The extant literature examining the nature of the risky behaviour of the young novice driver - and the contributing factors for this behaviour - while comprehensive, has not led to the development of a reliable instrument designed specifically to measure the risky behaviour of the young novice driver. Therefore the development and application of such a tool (the Behaviour of Young Novice Drivers Scale, or BYNDS) was foremost in the program of research. In addition to describing the driving behaviours of the young novice, a central theme of this program of research was identifying, describing, and quantifying personal, behavioural, and environmental influences upon young novice driver risky behaviour. Accordingly the 11 papers developed from the three stages of research which comprise this thesis are framed within Bandura's reciprocal determinism model which explicitly considers the reciprocal relationship between the environment, the person, and their behaviour. Stage One comprised the foundation research and operationalised quantitative and qualitative methodologies to finalise the instrument used in Stages Two and Three. The first part of Stage One involved an online survey which was completed by 761 young novice drivers who attended tertiary education institutions across Queensland. A reliable instrument for measuring the risky driving behaviour of young novices was developed (the BYNDS) and is currently being operationalised in young novice driver research in progress at the Centre for Injury Research and Prevention in Philadelphia, USA. In addition, regression analyses revealed that psychological distress influenced risky driving behaviour, and the differential influence of depression, anxiety, sensitivity to punishments and rewards, and sensation seeking propensity were explored. Path model analyses revealed that punishment sensitivity was mediated by anxiety and depression; and the influence of depression, anxiety, reward sensitivity and sensation seeking propensity were moderated by the gender of the driver. Specifically, for males, sensation seeking propensity, depression, and reward sensitivity were predictive of self-reported risky driving, whilst for females anxiety was also influential. In the second part of Stage One, 21 young novice drivers participated in individual and small group interviews. The normative influences of parents, peers, and the Police were explicated. Content analysis supported four themes of influence through punishments, rewards, and the behaviours and attitudes of parents and friends. The Police were also influential upon the risky driving behaviour of young novices. The findings of both parts of Stage One informed the research of Stage Two. Stage Two was a comprehensive investigation of the pre-Licence and Learner experiences, attitudes, and behaviours, of young novice drivers. In this stage, 1170 young novice drivers from across Queensland completed an online or paper survey exploring their experiences, behaviours and attitudes as a pre- and Learner driver. The majority of novices did not drive before they were licensed (pre-Licence driving) or as an unsupervised Learner, submitted accurate logbooks, intended to follow the road rules as a Provisional driver, and reported practicing predominantly at the end of the Learner period. The experience of Learners in the enhanced-GDL program were also examined and compared to those of Learner drivers who progressed through the former-GDL program (data collected previously by Bates, Watson, & King, 2009a). Importantly, current-GDL Learners reported significantly more driving practice and a longer Learner period, less difficulty obtaining practice, and less offence detection and crash involvement than Learners in the former-GDL program. The findings of Stage Two informed the research of Stage Three. Stage Three was a comprehensive exploration of the driving experiences, attitudes and behaviours of young novice drivers during their first six months of Provisional 1 licensure. In this stage, 390 of the 1170 young novice drivers from Stage Two completed another survey, and data collected during Stages Two and Three allowed a longitudinal investigation of self-reported risky driving behaviours, such as GDL-specific and general road rule compliance; risky behaviour such as pre-Licence driving, crash involvement and offence detection; and vehicle ownership, paying attention to Police presence, and punishment avoidance. Whilst the majority of Learner and Provisional drivers reported compliance with GDL-specific and general road rules, 33% of Learners and 50% of Provisional drivers reported speeding by 10-20 km/hr at least occasionally. Twelve percent of Learner drivers reported pre-Licence driving, and these drivers were significantly more risky as Learner and Provisional drivers. Ten percent of males and females reported being involved in a crash, and 10% of females and 18% of males had been detected for an offence, within the first six months of independent driving. Additionally, 75% of young novice drivers reported owning their own car within six months of gaining their Provisional driver's licence. Vehicle owners reported significantly shorter Learner periods and more risky driving exposure as a Provisional driver. Paying attention to Police presence on the roads appeared normative for young novice drivers: 91% of Learners and 72% of Provisional drivers reported paying attention. Provisional drivers also reported they actively avoided the Police: 25% of males and 13% of females; 23% of rural drivers and 15% of urban drivers. Stage Three also allowed the refinement of the risky behaviour measurement tool (BYNDS) created in Stage One; the original reliable 44-item instrument was refined to a similarly reliable 36-item instrument. A longitudinal exploration of the influence of anxiety, depression, sensation seeking propensity and reward sensitivity upon the risky behaviour of the Provisional driver was also undertaken using data collected in Stages Two and Three. Consistent with the research of Stage One, structural equation modeling revealed anxiety, reward sensitivity and sensation seeking propensity predicted self-reported risky driving behaviour. Again, gender was a moderator, with only reward sensitivity predicting risky driving for males. A measurement model of Akers' social learning theory (SLT) was developed containing six subscales operationalising the four constructs of differential association, imitation, personal attitudes, and differential reinforcement, and the influence of parents and peers was captured within the items in a number of these constructs. Analyses exploring the nature and extent of the psychosocial influences of personal characteristics (step 1), Akers' SLT (step 2), and elements of the prototype/willingness model (PWM) (step 3) upon self-reported speeding by the Provisional driver in a hierarchical multiple regression model found the following significant predictors: gender (male), car ownership (own car), reward sensitivity (greater sensitivity), depression (greater depression), personal attitudes (more risky attitudes), and speeding (more speeding) as a Learner. The research findings have considerable implications for road safety researchers, policy-makers, mental health professionals and medical practitioners alike. A broad range of issues need to be considered when developing, implementing and evaluating interventions for both the intentional and unintentional risky driving behaviours of interest. While a variety of interventions have been historically utilised, including education, enforcement, rehabilitation and incentives, caution is warranted. A multi-faceted approach to improving novice road safety is more likely to be effective, and new and existing countermeasures should capitalise on the potential of parents, peers and Police to be a positive influence upon the risky behaviour of young novice drivers. However, the efficacy of some interventions remains undetermined at this time. Notwithstanding this caveat, countermeasures such as augmenting and strengthening Queensland's GDL program and targeting parents and adolescents particularly warrant further attention. The findings of the research program suggest that Queensland's current-GDL can be strengthened by increasing compliance of young novice drivers with existing conditions and restrictions. The rates of speeding reported by the young Learner driver are particularly alarming for a number of reasons. The Learner is inexperienced in driving, and travelling in excess of speed limits places them at greater risk as they are also inexperienced in detecting and responding appropriately to driving hazards. In addition, the Learner period should provide the foundation for a safe lifetime driving career, enabling the development and reinforcement of non-risky driving habits. Learners who sped reported speeding by greater margins, and at greater frequencies, when they were able to drive independently. Other strategies could also be considered to enhance Queensland's GDL program, addressing both the pre-Licence adolescent and their parents. Options that warrant further investigation to determine their likely effectiveness include screening and treatment of novice drivers by mental health professionals and/or medical practitioners; and general social skills training. Considering the self-reported pre-licence driving of the young novice driver, targeted education of parents may need to occur before their child obtains a Learner licence. It is noteworthy that those participants who reported risky driving during the Learner phase also were more likely to report risky driving behaviour during the Provisional phase; therefore it appears vital that the development of safe driving habits is encouraged from the beginning of the novice period. General education of parents and young novice drivers should inform them of the considerably-increased likelihood of risky driving behaviour, crashes and offences associated with having unlimited access to a vehicle in the early stages of intermediate licensure. Importantly, parents frequently purchase the car that is used by the Provisional driver, who typically lives at home with their parents, and therefore parents are ideally positioned to monitor the journeys of their young novice driver during this early stage of independent driving. Parents are pivotal in the development of their driving child: they are models who are imitated and are sources of attitudes, expectancies, rewards and punishments; and they provide the most driving instruction for the Learner. High rates of self-reported speeding by Learners suggests that GDL programs specifically consider the nature of supervision during the Learner period, encouraging supervisors to be vigilant to compliance with general and GDL-specific road rules, and especially driving in excess of speed limit. Attitudes towards driving are formed before the adolescent reaches the age when they can be legally licensed. Young novice drivers with risky personal attitudes towards driving reported more risky driving behaviour, suggesting that countermeasures should target such attitudes and that such interventions might be implemented before the adolescent is licensed. The risky behaviours and attitudes of friends were also found to be influential, and given that young novice drivers tend to carry their friends as their passengers, a group intervention such as provided in a school class context may prove more effective. Social skills interventions that encourage the novice to resist the negative influences of their friends and their peer passengers, and to not imitate the risky driving behaviour of their friends, may also be effective. The punishments and rewards anticipated from and administered by friends were also found to influence the self-reported risky behaviour of the young novice driver; therefore young persons could be encouraged to sanction the risky, and to reward the non-risky, driving of their novice friends. Adolescent health programs and related initiatives need to more specifically consider the risks associated with driving. Young novice drivers are also adolescents, a developmental period associated with depression and anxiety. Depression, anxiety, and sensation seeking propensity were found to be predictive of risky driving; therefore interventions targeting psychological distress, whilst discouraging the expression of sensation seeking propensity whilst driving, warrant development and trialing. In addition, given that reward sensitivity was also predictive, a scheme which rewards novice drivers for safe driving behaviour - rather than rewarding the novice through emotional and instrumental rewards for risky driving behaviour - requires further investigation. The Police were also influential in the risky driving behaviour of young novices. Young novice drivers who had been detected for an offence, and then avoided punishment, reacted differentially, with some drivers appearing to become less risky after the encounter, whilst for others their risky behaviour appeared to be reinforced and therefore was more likely to be performed again. Such drivers saw t

This work exploits augmented reality to safely train and validate autonomous vehicles’ performance in the real world under safety-critical scenarios. Toward this goal, we first develop algorithms that create virtual traffic participants with risky behaviors and seamlessly insert the virtual events into real images perceived from the physical world. The resulting composed images are photorealistic and physically grounded. The manipulated images are fed into the autonomous vehicle during testing, allowing the self-driving vehicle to react to such virtual events within either a photorealistic simulator or a real-world test track and real hardware systems. Our presented technique allows us to develop safe, hardware-in-the-loop, and cost-effective tests for self-driving cars to respond to immersive safety-critical traffic scenarios.

This paper explores the empirical determinants of external crises on a world panel dataset of 62 countries over the fifty-year period 1970-2019 and estimates their risk trade-offs with the aim of informing macrofinancial prudential policies. The determinants include countries external balance sheets, macroeconomic imbalances, and structural and global factors. It finds that information on the composition of gross positions in countries external financial portfolios is required to gauge the risk of external crisis: debt liabilities are the riskiest component, FDI liabilities are half as risky, and FDI assets are the most protective. Macroeconomic imbalances increase risk but are usually not the key drivers of crises. Adverse global shocks significantly leverage domestic risks. International reserves are powerful risk mitigants that provide high insurance value. The evidence shows that advanced economies are structurally more resilient to withstand exposure to weak external portfolios, macroeconomic imbalances, and global shocks. For the average country the risk of external crisis is on a declining trend mainly driven by improvements in the composition of external portfolio assets magnified by increasing financial integration as well as rising international reserves.

On-road vehicles equipped with driving automation features—where a human might not be needed for operation on-board—are entering the mainstream public space. However, questions like “How safe is safe enough?” and “What to do if the system fails?” persist. This is where remote operation comes in, which is an additional layer to the automated driving system where a human remotely assists the so-called “driverless” vehicle in certain situations. Such remote-operation solutions introduce additional challenges and potential risks as the entire vehicle-network-human now needs to work together safely, effectively, and practically. Unsettled Issues in Remote Operation for On-road Driving Automation highlights technical questions (e.g., network latency, bandwidth, cyber security) and human aspects (e.g., workload, attentiveness, situational awareness) of remote operation and introduces evolving solutions. The report also discusses standards development and regulations—both of which are needed to provide frameworks for the deployment of driving automation with remote operation.

Smoking, poor diet, physical inactivity and harmful alcohol use are leading risk factors driving the UK’s high burden of preventable ill health and premature mortality. All are socioeconomically patterned and contribute significantly to widening health inequalities. This Health Foundation report summarises recent trends for each of these risk factors and looks at national-level policies for England, introduced or proposed by the UK government between 2016 and 2021. The report reviews the government’s approach and finds a heavy reliance on policies aimed at changing individual behaviour and an uneven approach across risk factors, with particularly weak action on alcohol. The report also identifies that decision making across departments has been disjointed, undermining health improvement targets.

Novel materials with unique or enhanced properties relative to conventional materials are being developed at an increasing rate. These materials are often referred to as advanced materials (AdMs) and they enable technological innovations that can benefit society. Despite their benefits, however, the unique characteristics of many AdMs, including many nanomaterials, are poorly understood and may pose environmental safety and occupational health (ESOH) risks that are not readily determined by traditional risk assessment methods. To assess these risks while keeping up with the pace of development, technology developers and risk assessors frequently employ risk-screening methods that depend on a clear definition for the materials that are to be assessed (e.g., engineered nanomaterial) as well as a method for binning materials into categories for ESOH risk prioritization. In this study, we aim to establish a practitioner-driven definition for AdMs and a practitioner-validated framework for categorizing AdMs into conceptual groupings based on material characteristics. The definition and categorization framework established here serve as a first step in determining if and when there is a need for specific ESOH and regulatory screening for an AdM as well as the type and extent of risk-related information that should be collected or generated for AdMs and AdM-enabled technologies.