Can traffic violations be traced to gender-role,sensation seeking,demographics and driving exposure?

BackgroundTraffic safety is often expressed as the ‘inverse of accidents’. However, it is more than the mere absence of accidents. Past studies often looked for associations between accidents and self-reports like the Driver Behaviour Questionnaire (DBQ; Reason, Manstead, Stradling, Baxter, & Campbell, 1990). The focus in this study changed from counting accidents to quantifying unsafe acts as violations. The objective was to show that drivers' specific violations can be traced to personal characteristics such as sensation seeking (SSS-V; Zuckerman, 1994), gender role (BSRI; Bem sex role inventory, Bem, 1974), demographics, and driving exposure.MethodA web-based questionnaire was distributed, integrating several known questionnaires. Five hundred and twenty-seven questionnaires were completed and analyzed.ResultsSensation seeking, gender role, experience, and age predicted respondents’ score on the DBQ, as well as the interaction of sensation seeking with gender and gender role. Gender role was a more valid predictor of driver behavior than gender.ConclusionsThe effect of gender role on drivers’ self-reported violation tendency is the most interesting and the most intriguing finding of this survey and indicates the need to further examine gender role affects in driving.     

The electric vehicle: A new driving experience involving specific skills and rules

The arrival of the electric vehicle (EV) on the market is one consequence of government measures to improve air quality and reduce CO² emissions. However, the EV has specific properties of use associated with its limited range and relative silence compared to normal vehicles, influencing the mobility behaviours of drivers and requiring them to develop some new driving abilities. This paper examines the behavioural modifications brought about by daily use of an electric vehicle at three different levels of driving activity: strategic, tactical and operational. The study collected and analyzed the self-reported behaviours (via questionnaires and travels dairies) of 36 Parisian private drivers, each of whom drove for six months an electric MINI E prototype. The results of the study show that driving an EV requires a learning phase to acquire the skills and knowledge necessary to operate the vehicle. At the strategic level of driving, drivers take into account the restricted range of the EV, implement a daily charge process, and develop new behaviours related to trip planning. The study also examines driver behaviour at the tactical level, in terms of driver interactions with other road users to deal with the silent nature of the EV, and at the operational level of driving, in terms of braking behaviour to master the regenerative braking function of the EV. The paper discusses the interactions between these three levels of driving activity.     

From Gibson and Crooks to Damasio: The role of psychology in the development of driver behaviour models

This article presents a brief history and perspective of behavioural model development in traffic psychology. As one specific example of a key behavioural model, Gibson and Crooks (1938), in their classic field theoretical study, offered the first scientific attempt to deal with the issue of compensation. Two central theoretical concepts were developed: “Field of safe travel” and “Minimum stopping zone”. The interplay between the two was used to describe and explain risk compensation and illustrated by observing the impact of brakes on driver behaviour: Better brakes could make the field of safe travel – i.e. the distance to the car in front – shorter. Nearly 50 years later, the launch of Wilde’s Risk Homeostasis Theory (RHT) gave rise to a profound debate about risk homeostasis and risk compensation. The core issue in the debate was Wilde’s strict assertion that all individuals, not only car-drivers, carry an inherent target level of risk that they are seeking to maintain or restore. Gibson and Crooks fell well within psychological theories of the time, while Wilde’s RHT emerged more from control theory and economic utility theory than from psychology. In the 1990s neuroscience emerges, especially by Damasio who introduces a paradigm that has proven fruitful as a framework of more recent driver behaviour models. But neuroscience also had its forerunner in Taylor’s proposal that driver behaviour is governed by a constancy in Galvanic Skin Response (GSR) which makes driving a self-paced task aiming at keeping the GSR at a constant level. Näätänen and Summala’s integrated Taylor in their “Zero-risk model” which has persisted and still prevails as a solid and well accepted model. Psychological learning theory has, however, rarely been adequately dealt with which is quite odd given the prevalence of speeding and risk compensation which cannot escape explanations based on operant conditioning. The paper discusses the emerging role of psychology and psychological concepts that has been proposed and evolved through the development of driver behaviour models since Gibson and Crooks’ study of 1938. The views presented are subjective, they do not represent any attempt to describe the objective reality of the time.     

Car status as an inhibitor of passing responses to a low-speed frustrator

Studies have shown that the high status of a car used as a frustrator acts as an inhibitor of drivers’ horn-honking responses at traffic lights. In this field study, we extended the role played by car status through examining its effect on another driver behavior. A confederate driving either a high-status or a low-status car was instructed to drive at a speed below the speed limit. What was measured was the frequency of responses to the frustrator that resulted in passing the slow-moving vehicle. It was found that more passing behaviors occurred in the low-status condition, and that the difference between the low and the high status increased as soon as the confederate’s speed decreased.     

Using topic modeling to develop multi-level descriptions of naturalistic driving data from drivers with and without sleep apnea

One challenge in using naturalistic driving data is producing a holistic analysis of these highly variable datasets. Typical analyses focus on isolated events, such as large g-force accelerations indicating a possible near-crash. Examining isolated events is ill-suited for identifying patterns in continuous activities such as maintaining vehicle control. We present an alternative approach that converts driving data into a text representation and uses topic modeling to identify patterns across the dataset. This approach enables the discovery of non-linear patterns, reduces the dimensionality of the data, and captures subtle variations in driver behavior. In this study topic models were used to concisely described patterns in trips from drivers with and without untreated obstructive sleep apnea (OSA). The analysis included 5000 trips (50 trips from 100 drivers; 66 drivers with OSA; 34 comparison drivers). Trips were treated as documents, and speed and acceleration data from the trips were converted to “driving words.” The identified patterns, called topics, were determined based on regularities in the co-occurrence of the driving words within the trips. This representation was used in random forest models to predict the driver condition (i.e., OSA or comparison) for each trip. Models with 10, 15 and 20 topics had better accuracy in predicting the driver condition, with a maximum AUC of 0.73 for a model with 20 topics. Trips from drivers with OSA were more likely to be defined by topics for smaller lateral accelerations at low speeds. The results demonstrate topic modeling as a useful tool for extracting meaningful information from naturalistic driving datasets.     

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.     

Accident involvement among Iranian lorry drivers: Direct and indirect effects of background variables and aberrant driving behaviour

Road accident rates among Iranian lorry drivers are considerably high and, according to empirical evidence, aberrant driving behaviours, summed to certain demographic, psycho-social and work-related factors, may explain their accident involvement. Consequently, the main aim of the study was to examine the direct and indirect effects of background variables (i.e. annual mileage, lorry driving experience, demographic and socioeconomic factors) on accident involvement mediated through aberrant driving behaviour among Iranian lorry drivers. A cross-sectional questionnaire survey was conducted in 2012 among 914 lorry drivers in 10 selected provinces in Iran. The 27-item Driver Behaviour Questionnaire (DBQ) was used to measure aberrant driving behaviour. Results from valid observations (n = 785) confirmed a four-factor solution (including ordinary violations, aggressive violations, errors, and lapses) of the DBQ. Errors, ordinary violations and aggressive violations were positively associated with accident involvement. However, lapses were not significantly associated with accident involvement. The results of structural equation modeling (SEM) further showed that, in addition to direct effects of background variables on accident involvement, several variables had indirect effects mediated by three-DBQ factors; ordinary violations, aggressive violations, and errors. Higher age, having more lorry driving experience, having higher educational attainment, and married drivers were indirectly related to less accident involvement. Annual driving mileage and the resting rate of drivers was both directly and indirectly related to accident involvement. Higher income and car ownership were directly related to fewer accidents. Interventions could aim to decrease ordinary violations, aggressive violations and errors among younger, less educated and single lorry drivers. Initiatives targeted to increase the scheduled resting frequency of lorry drivers may also hold promise.     

Eyes on the road,hands upon the wheel? Reciprocal dynamics between smartphone use while driving and job crafting

Smartphones are essential tools for communications and information management in organizational settings. However, smartphone use is a risky behavior when used while driving to and from work. As work experiences have been found to influence risky commuting behaviors, we hypothesized that job crafting, i.e., a set of proactive work behaviors through which employees change their job demands and resources, influences and is influenced by risky commuting behaviors. We argued that employees' smartphone use during driving commutes is related to how employees proactively choose to transform their demands and resources at work. A quantitative diary study was designed to investigate the process linking smartphone use during driving commutes to and from work and job crafting. A sample of 128 office employees completed two short daily questionnaires for five consecutive workdays (N = 627 observations). Results from multilevel analyses showed that daily talking on the phone while driving to work was positively associated with the proactive optimization of job demands, while daily proactive pursuing of challenging stimuli at work (i.e., seeking challenges) was positively related to looking at the phone when employees drove back from work. Furthermore, on days when employees reduced their hindering job demands, they reported less frequent talking on the phone while driving back from work. Results provide practical implications for the prevention of distracted driving and other risky driving behaviors.     

Effects of blood alcohol testing programmes on drinking behaviour among driving crash patients in emergency departments: A cluster quasi-experimental study

This study aimed to evaluate the effectiveness of blood alcohol testing in decreasing the prevalence of current one-month drinkers among road traffic crash patients within emergency departments at one, two, and three months after a crash. A cluster quasi-experimental study was conducted on 600 crash patients who visited one of the emergency departments at 21 hospitals in Udon Thani province, Thailand. The hospitals were categorised into a (i) high-adherence hospital group (≥70% of all patients) and (ii) low-adherence hospital group (<70% of all patients) according to the compliance of blood alcohol testing in their emergency departments. The data were collected by a trained nurse using a structured questionnaire. The primary outcome was the prevalence of one-month current drinkers. We included 600 patients: 291 from six hospitals in the high-adherence group and 309 from 15 hospitals in the low-adherence group. The prevalence of one-month current drinkers significantly decreased in both the high-adherence and low-adherence groups. However, the prevalence of current drinkers at two and three months after a crash was not statistically significant compared to that one month prior to a crash (48.0% to 19.3% and 31.7% to 13.8% in the high- and low-adherence hospital groups, respectively; p < 0.05 from McNemar’s test). The effectiveness of blood alcohol testing in decreasing the prevalence of one-month current drinkers among traffic crash patients within emergency departments was observed to be statistically significant only at one month after a crash, and not at two and three months.