Articulating cooperative systems and driver behaviour theories

There has been increased research interest in Co-operative Vehicle Infrastructure Systems (CVIS) from the eld of Intelligent Transport Systems (ITS). However most of the research have focused on the engineering aspects and overlooked their relevance to the drivers' behaviour. This paper argues that the priority for cooperative systems is the need to improve drivers decision making and reduce drivers' crash risk exposure to improve road safety. Therefore any engineering solutions need to be considered in conjuction with traffic psychology theories on driver behaviour. This paper explores the advantages and limitations of existing systems and emphasizes various theoretical issues that arise in articulating cooperative systems' capabilities and drivers' behaviour.

Driver behaviour and decision making at railway level crossings : An exploratory on-road case study

Crashes at rail level crossings represent a significant problem, both in Australia and worldwide. Advances in driving assessment methods, such as the provision of on-road instrumented test vehicles, now provide researchers with the opportunity to further understand driver behaviour at rail level crossings in ways not previously possible. This paper gives an overview of a recent on-road pilot study of driver behaviour at rail level crossings in which 25 participants drove a pre-determined route, incorporating 4 rail level crossings, using MUARC's instrumented On-Road Test Vehicle (ORTeV). Drivers provided verbal commentary whilst driving the route, and a range of other data were collected, including eye fixations, forward, cockpit and driver video, and vehicle data (speed, braking, steering wheel angle, lane tracking etc). Participants also completed a post trial cognitive task analysis interview. Extracts from the wider analyses are used to examine in depth driver behaviour at one of the rail level crossings encountered during the study. The analysis presented, along with the overall analysis undertaken, gives insight into the driver and wider systems factors that shape behaviour at rail level crossings, and highlights the utility of using a multi-method, instrumented vehicle approach for gathering data regarding driver behaviour in different contexts.

Safety in occupational driving : development of a driver behaviour scale for the workplace context

International statistics indicate that occupational, or work-related driving, crashes are the most common cause of workplace injury, death, and absence from work. The majority of research examining unsafe driver behavior in the workplace has relied on general road safety questionnaires. However, past research has failed to consider the organizational context in the use of these questionnaires, and as such, there is ambiguity in the dimensions constituting occupational driving. Using a theoretical model developed by Hockey (1993, 1997), this article proposes and validates a new scale of occupational driver behavior. This scale incorporates four dimensions of driver behavior that are influenced by demanding workplace conditions; speeding, rule violation, inattention, and driving while tired. Following a content validation process, three samples of occupational drivers in Australia were used to assess the scale. Data from the first sample (n=145) were used to reduce the number of scale items and provide an assessment of the factorial validity of the scale. Data from the second sample (n=645) were then used to confirm the factor structure and psychometric properties of the scale including reliability and construct validity. Finally, data from the third sample (n=248) were used to establish criterion validity. The results indicated that the scale is a reliable and valid measure of occupational driver behavior.

Commentaries and Responses to "The Driver Behaviour Questionnaire as a predictor of accidents: A meta-analysis" [Commentaries lead by Anders af Wahlberg; Responses lead by J.C.F. de Winter]

The following discussion is in response to a 2010 article published in the Journal of Safety Research by J.C.F. de Winter and D. Dodou entitled “The Driver Behaviour Questionnaire as a predictor of accidents: A meta-analysis” (Volume 41, Number 6, pp. 463-470, available on sciencedirect.com). The editors are pleased to provide a forum for this exchange and welcome further comments.

Effects of speeding and headway related variable message signs on driver behaviour and attitudes

This research project examined objective measures of driver behaviour and road users' perceptions on the usefulness and effectiveness of three specific VMS (Variable Message Signs) interventions to improve speeding and headway behaviours. The interventions addressed speeding behaviour alone (intervention 1), headway behaviour alone (intervention 2) and a combination of speeding and headway behaviour (intervention 3). Six VMS were installed along a segment of the Bruce Highway, with a series of three signs for each of the northbound and southbound traffic. Speeds and headway distances were measured with loop detectors installed before and after each VMS. Messages addressing speeding and headway were devised for display on the VMS. A driver could receive a message if they were detected as exceeding the posted speed limit (of 90km/hr) or if the distance to the vehicle in front was less than 2.0s. In addition to the on-road objective measurement of speeding and headway behaviours, the research project elicited self-reported responses to the speeding and headway messages from a sample of drivers via a community-based survey. The survey sought to examine the drivers' beliefs about the effectiveness of the signs and messages, and their views about the role, use, and effectiveness of VMS more generally.

Achieving safe road use in a rapidly motorising country : The influence of longstanding beliefs on risky driver behaviour in Pakistan

Road crashes are one of the most significant public health problems in Pakistan; however the factors that contribute to road crashes in Pakistan are not well-researched. Traditional beliefs and values can act as a barrier to health-promoting and injury prevention behaviours, in general and especially in relation to road safety, and may also contribute to risk-taking behaviours. Such beliefs can present significant challenges for health advocates who aim to change behaviour in order to avert road crashes and diminish their consequences. Qualitative research was undertaken in Islamabad, Rawalpindi and Lahore with a range of drivers, religious orators, police and policy makers to explore cultural and religious beliefs and their association with risky road use, and to understand how they might affect development of road safety interventions. The findings highlight a range of issues, including the identification of aspects of beliefs that have complex social implications when designing safety intervention strategies. The pervasive nature of religious and superstitious beliefs in Pakistan can affect road user behaviour by reinforcing the presumption that the individual has no part to play in safety, thereby supporting continued risk taking behaviour. It is anticipated that the findings could be used to inform the design of interventions aimed at influencing broad-spectrum health attitudes and practices among the communities where such beliefs are prevalent.

Three social car visions to improve driver behaviour

The social cost of road injury and fatalities is still unacceptable. The driver is often mainly responsible for road crashes, therefore changing the driver behaviour is one of the most important and most challenging priority in road transport. This paper presents three innovative visions that articulate the potential of using Vehicle to Vehicle (V2V) communication for supporting the exchange of social information amongst drivers. We argue that there could be tremendous benefits in socialising cars to influence human driving behaviours for the better and that this aspect is still relevant in the age of looming autonomous cars. Our visions provide theoretical grounding how V2V infrastructure and emerging human–machine interfaces (HMI) could persuade drivers to: (i) adopt better (e.g. greener) driving practices, (ii) reduce drivers aggressiveness towards pro-social driving behaviours, and (iii) reduce risk-taking behaviour in young, particularly male, adults. The visions present simple but powerful concepts that reveal ‘good’ aspects of the driver behaviour to other drivers and make them contagious. The use of self-efficacy, social norms, gamification theories and social cues could then increase the likelihood of a widespread adoption of such ‘good’ driving behaviours.

An application of the Driver Behaviour Questionnaire in a large Australian organisational fleeting setting : can it predict crashes and demerit point loss?

This study reports on the utilisation of the Manchester Driver Behaviour Questionnaire (DBQ) to examine the self-reported driving behaviours of a large sample of Australian fleet drivers (N = 3414). Surveys were completed by employees before they commenced a one day safety workshop intervention. Factor analysis techniques identified a three factor solution similar to previous research, which was comprised of: (a) errors, (b) highway-code violations and (c) aggressive driving violations. Two items traditionally related with highway-code violations were found to be associated with aggressive driving behaviours among the current sample. Multivariate analyses revealed that exposure to the road, errors and self-reported offences predicted crashes at work in the last 12 months, while gender, highway violations and crashes predicted offences incurred while at work. Importantly, those who received more fines at work were at an increased risk of crashing the work vehicle. However, overall, the DBQ demonstrated limited efficacy at predicting these two outcomes. This paper outlines the major findings of the study in regards to identifying and predicting aberrant driving behaviours and also highlights implications regarding the future utilisation of the DBQ within fleet settings.

Using the Driver Behaviour Questionnaire to predict crashes and demerit point loss : does it get better with larger sample sizes?

The Driver Behaviour Questionnaire (DBQ) continues to be the most widely utilised self-report scale globally to assess crash risk and aberrant driving behaviours among motorists. However, the scale also attracts criticism regarding its perceived limited ability to accurately identify those most at risk of crash involvement. This study reports on the utilisation of the DBQ to examine the self-reported driving behaviours (and crash outcomes) of drivers in three separate Australian fleet samples (N = 443, N = 3414, & N = 4792), and whether combining the samples increases the tool’s predictive ability. Either on-line or paper versions of the questionnaire were completed by fleet employees in three organisations. Factor analytic techniques identified either three or four factor solutions (in each of the separate studies) and the combined sample produced expected factors of: (a) errors, (b) highway-code violations and (c) aggressive driving violations. Highway code violations (and mean scores) were comparable across the studies. However, across the three samples, multivariate analyses revealed that exposure to the road was the best predictor of crash involvement at work, rather than DBQ constructs. Furthermore, combining the scores to produce a sample of 8649 drivers did not improve the predictive ability of the tool for identifying crashes (e.g., 0.4% correctly identified) or for demerit point loss (0.3%). The paper outlines the major findings of this comparative sample study in regards to utilising self-report measurement tools to identify “at risk” drivers as well as the application of such data to future research endeavours.

The Driver Behaviour Questionnaire as accident predictor: A methodological re-meta-analysis

Problem The Manchester Driver Behaviour Questionnaire (DBQ) is the most commonly used self-report tool in traffic safety research and applied settings. It has been claimed that the violation factor of this instrument predicts accident involvement, which was supported by a previous meta-analysis. However, that analysis did not test for methodological effects, or include contacting researchers to obtain unpublished results. Method The present study re-analysed studies on prediction of accident involvement from DBQ factors, including lapses, and many unpublished effects. Tests of various types of dissemination bias and common method variance were undertaken. Results Outlier analysis showed that some effects were probably not reliable data, but excluding them did not change the results. For correlations between violations and crashes, tendencies for published effects to be larger than unpublished ones and for effects to decrease over time were observed, but were not significant. Also, analysis using the proxy of the mean of accidents in studies indicated that studies where effects for violations are unknown have smaller effect sizes. These differences indicate dissemination bias. Studies using self-reported accidents as dependent variables had much larger effects than those using recorded accident data. Also, zero-order correlations were larger than partial correlations that controlled for exposure. Similarly, violations/accidents effects were strong only when there was also a strong correlation between accidents and exposure. Overall, the true effect is probably very close to zero (r<.07) for violations versus traffic accident involvement, depending upon which systematic tendencies in the data are controlled for. Conclusions: Methodological factors and dissemination bias have inflated the mean effect size of the DBQ in the published literature. Strong evidence of various artefactual effects is apparent. Practical Applications A greater level of care should be taken if the DBQ continues to be used in traffic safety research. Also, validation of self-reports should be more comprehensive in the future, taking into account the possibility of common method variance.

A Filter for Visual Tracking Based on a Stochastic Model for Driver Behaviour

A driver controls a car by turning the steering wheel or by pressing on the accelerator or the brake. These actions are modelled by Gaussian processes, leading to a stochastic model for the motion of the car. The stochastic model is the basis of a new filter for tracking and predicting the motion of the car, using measurements obtained by fitting a rigid 3D model to a monocular sequence of video images. Experiments show that the filter easily outperforms traditional filters.

Driver behaviour prediction for motion simulators using changepoint segmentation

Driving phenomenon is a repetitive process, that permits sequential learning under identifying the proper change periods. Sequential filtering is widely used for tracking and prediction of state dynamics. However, it suffers at abrupt changes, which cause sudden incremental prediction error. We provide a sequential filtering approach using online Bayesian detection of change points to decrease prediction error generally, and specifically at abrupt changes. The approach learns from optimally detected segments for identifying driving behaviour. Change points detection is done by the Pruned Exact Linear Time algorithm. Computational cost of our approach is bounded by the cost of the implemented sequential filter. This computational performance is suitable to the online nature of motion simulator's delay reduction. The approach was tested on a simulated driving scenario using Vortex by CM Labs. The state dimensions are simulated 2D space coordinates, and velocity. Particle filter was used for online sequential filtering. Prediction results show that change-point detection improves the quality of state estimation compared to traditional sequential filters, and is more suitable for predicting behavioural activities.