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.

Confirmatory factor analysis of the baby eating behaviour questionnaire and associations with infant weight, gender and feeding mode in an Australian sample

The aim of this study was to evaluate the factor structure of the Baby Eating Behaviour Questionnaire (BEBQ) in an Australian community sample of mother-infant dyads. A secondary aim was to explore the relationship between the BEBQ subscales and infant gender, weight and current feeding mode. Confirmatory factor analysis (CFA) utilising structural equation modelling examined the hypothesised 4-factor model of the BEBQ. Only mothers (N=467) who completed all items on the BEBQ (infant age: M=17 weeks, SD=3 weeks) were included in the analysis. The original 4-factor model did not provide an acceptable fit to the data due to poor performance of the Satiety responsiveness factor. Removal of this factor (3 items) resulted in a well-fitting 3-factor model. Cronbach’s α was acceptable for the Enjoyment of food (α=0.73), Food responsiveness (α=0.78) and Slowness in eating (α=0.68) subscales but low for the Satiety responsiveness (α=0.56) subscale. Enjoyment of food was associated with higher infant weight whereas Slowness in eating and Satiety responsiveness were both associated with lower infant weight. Differences on all four subscales as a function of feeding mode were observed. This study is the first to use CFA to evaluate the hypothesised factor structure of the BEBQ. Findings support further development work on the Satiety responsiveness subscale in particular, but confirm the utility of the Enjoyment of food, Food responsiveness and Slowness in eating subscales.

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.

Impact of driver behaviour on availability of electric vehicle stored energy for STOR

As take up of low carbon vehicles increase, there is interest in using the energy stored in the vehicles to help maintain system frequency through ancillary services on the electricity grid system. Research into this area is generally classed as vehicle-to-grid research. In theory, the energy available from electric vehicles could be directly correlated to the vehicle's state of charge (SoC) and battery capacity during the time the car is parked and plugged in. However, not all the energy in the vehicle may be used, as some capacity is required by the driver for their next journey. As such, this paper uses data captured as part of a large scale electric vehicle trial to investigate the effect of three different types of driver routine on vehicle-to-grid availability. Each driver's behaviour is analysed to assess the energy that is available for STOR, with follow on journey requirements also considered.

The influence of safety ownership on occupational road safety outcomes

Questionnaires and interviews were conducted with employees and senior managers from three Australian organisations to explore the relationship between perceived managerial ownership of safety responsibilities and occupational road safety. It was found that the perceived authority of the person primarily responsible for managing road risks and perceived shared ownership of safety tasks were both significant independent predictors of safer driving behaviours. It was identified that the position of the person accepting primary risk management responsibilities was typically a member of the OHS team and typically in a management position. The extent that ownership was shared across members within the researched organisations varied, with personnel from OHS and fleet management typically accepting partial ownership of managing occupational road risks. Based on the findings, several recommendations are made to assist practitioners in managing occupational road risks.

The relative impact of work-related stress, life stress and driving environment stress on driving outcomes

Previous research has shown the association between stress and crash involvement. The impact of stress on road safety may also be mediated by behaviours including cognitive lapses, errors, and intentional traffic violations. This study aimed to provide a further understanding of the impact that stress from different sources may have upon driving behaviour and road safety. It is asserted that both stress extraneous to the driving environment and stress directly elicited by driving must be considered part of a dynamic system that may have a negative impact on driving behaviours. Two hundred and forty-seven public sector employees from Queensland, Australia, completed self-report measures examining demographics, subjective work-related stress, daily hassles, and aspects of general mental health. Additionally, the Driver Behaviour Questionnaire (DBQ) and the Driver Stress Inventory (DSI) were administered. All participants drove for work purposes regularly, however the study did not specifically focus on full-time professional drivers. Confirmatory factor analysis of the predictor variables revealed three factors: DSI negative affect; DSI risk taking; and extraneous influences (daily hassles, work-related stress, and general mental health). Moderate intercorrelations were found between each of these factors confirming the ‘spillover’ effect. That is, driver stress is reciprocally related to stress in other domains including work and domestic life. Structural equation modelling (SEM) showed that the DSI negative affect factor influenced both lapses and errors, whereas the DSI risk-taking factor was the strongest influence on violations. The SEMs also confirmed that daily hassles extraneous to the driving environment may influence DBQ lapses and violations independently. Accordingly, interventions may be developed to increase driver awareness of the dangers of excessive emotional responses to both driving events and daily hassles (e.g. driving fast to ‘blow off steam’ after an argument). They may also train more effective strategies for self-regulation of emotion and coping when encountering stressful situations on the road.

When non-significance maybe significant: lessons learned from a study into the development, implementation and evaluation of a risk assessment tool for fleet settings

Introduction This study reports on the development of a self report assessment tool to increase the efficacy of crash prediction within Australian Fleet settings Over last 20 years an array of measures have been produced (Driver anger scale, Driving Skill Inventory, Manchester Driver Behaviour Questionnaire, Driver Attitude Questionnaire, Driver Stress Inventory, Safety Climate Questionnaire) While these tools are useful, research has demonstrated limited ability to accurately identify individuals most likely to be involved in a crash. Reasons cited include; - Crashes are relatively rare - Other competing factors may influence crash event - Ongoing questions regarding the validity of self report measures (common method variance etc) - Lack of contemporary issues relating to fleet driving performance

The wrong tool for the job? The predictive powers of the DBQ in a sample of Queensland motorists

Introduction This study reports on the application of the Manchester Driver Behaviour Questionnaire (DBQ) to examine the self-reported driving behaviours (e.g., speeding, errors & aggressive manoeuvres) and predict crash involvement among a sample of general Queensland motorists. Material and Methods Surveys were completed by 249 general motorists on-line or via a pen-and-paper format. Results A factor analysis revealed a three factor solution for the DBQ which was consistent with previous Australian-based research. It accounted for 40.5% of the total variance, although some cross-loadings were observed on nine of the twenty items. The internal reliability of the DBQ was satisfactory. However, multivariate analysis using the DBQ revealed little predictive ability of the tool to predict crash involvement or demerit point loss e.g. violation notices. Rather, exposure to the road was found to be predictive of crashes, although speeding did make a small contribution to those who recently received a violation notice. Conclusions Taken together, the findings contribute to a growing body of research that raises questions about the predictive ability of the most widely used driving assessment tool globally. Ongoing research (which also includes official crash and offence outcomes) is required to better understand the actual contribution that the DBQ can make to understanding and improving road safety. Future research should also aim to confirm whether this lack of predictive efficacy originates from broader issues inherent within self-report data (e.g., memory recall problems) or issues underpinning the conceptualisation of the scale.

Real or imagined? A study exploring the existence of common method variance effects in road safety research

Common method variance (CMV) has received little attention within the field of road safety research despite a heavy reliance on self-report data. Two surveys were completed by 214 motorists over a two-month period, allowing associations between social desirability and key road safety variables and relationships between scales across the two survey waves to be examined. Social desirability was found to have a strong negative correlation with the Driver Behaviour Questionnaire (DBQ) sub-scales as well as age, but not with crashes and offences. Drivers who scored higher on the social desirability scale were also less likely to report aberrant driving behaviours as measured by the DBQ. Controlling for social desirability did not substantially alter the predictive relationship between the DBQ and the crash and offences variables. The strength of the correlations within and between the two waves were also compared with the results strongly suggesting that effects associated with CMV were present. Identification of CMV would be enhanced by the replication of this study with a larger sample size and comparing self-report data with official sources.

It wasn't my fault : does crash culpability improve predictive outcomes in a self-report study?

Introduction: Research that has focused on the ability of self-report assessment tools to predict crash outcomes has proven to be mixed. As a result, researchers are now beginning to explore whether examining culpability of crash involvement can subsequently improve this predictive efficacy. This study reports on the application of the Manchester Driver Behaviour Questionnaire (DBQ) to predict crash involvement among a sample of general Queensland motorists, and in particular, whether including a crash culpability variable improves predictive outcomes. Surveys were completed by 249 general motorists on-line or via a pen-and-paper format. Results: Consistent with previous research, a factor analysis revealed a three factor solution for the DBQ accounting for 40.5% of the overall variance. However, multivariate analysis using the DBQ revealed little predictive ability of the tool to predict crash involvement. Rather, exposure to the road was found to be predictive of crashes. An analysis into culpability revealed 88 participants reported being “at fault” for their most recent crash. Corresponding between and multi-variate analyses that included the culpability variable did not result in an improvement in identifying those involved in crashes. Conclusions: While preliminary, the results suggest that including crash culpability may not necessarily improve predictive outcomes in self-report methodologies, although it is noted the current small sample size may also have had a deleterious effect on this endeavour. This paper also outlines the need for future research (which also includes official crash and offence outcomes) to better understand the actual contribution of self-report assessment tools, and culpability variables, to understanding and improving road safety.

Errores humanos autorreferenciados por los peatones del sistema vial de la ciudad de Bahía Blanca, Argentina: estudio descriptivo observacional

Introducción: los peatones representan el 22% del total mundial de fallecimientos por colisiones viales. Constituyen la población más vulnerable del sistema vial. Objetivo: describir los tipos de errores humanos autorreferenciados por los peatones que circularon en el sistema vial de la ciudad de Bahía Blanca durante el período septiembre del 2014 a abril del 2015. Metodología: estudio descriptivo observacional de corte transversal. Muestra por conveniencia. Se confeccionó una encuesta estructurada en base al Driver Behaviour Questionnaire en la versión traducida al español. Se validó mediante el método Delphi. Se analizó con software stadístico SPSS. Resultados: el 46,5% refiere no mirar a ambos lados de la calle antes de cruzar. El 22,3% refiere no ver cuando está saliendo un auto de un garaje y debe frenar bruscamente. El 32,2% refiere cruzar la calle hablando por teléfono celular y no darse cuenta si viene un vehículo. El 89,2% refiere cruzar a la vereda de enfrente por la mitad de la calle. El 69,7% refiere cruzar la calle aunque el semáforo peatonal esté en rojo. Conclusiones: las conductas que se observan en el estudio demuestran la inobservancia de las normas por parte de los peatones. Muchas de dichas conductas son condiciones que podrían generar una colisión con lesiones graves. El foco de la intervención debería orientarse al control más estricto de los peatones cuando circulan por el sistema vial.

Understanding the psychosocial factors influencing the risky behaviour of young drivers

Young people aged 17–24 years are at high risk of being killed in road crashes around the world. Road safety interventions consider some influences upon young driver behaviour; for example, imposing passenger restrictions on young novice drivers indirectly minimises the potential negative social influences of peers as passengers. To change young driver risky behaviour, the multitude of psychosocial influences upon its initiation and maintenance must be identified. A study questionnaire was developed to investigate the relationships between risky driving and Akers’ social learning theory, social identity theory, and thrill seeking variables. The questionnaire was completed by 165 participants (105 women,60 men) residing in south-east Queensland, Australia. The sociodemographic variables of age, gender, and exposure explained 19% of the variance in self-reported risky driving behaviour, whilst Akers’ social learning variables explained an additional 42%. Thrill seeking and social identity variables did not explain any significant additional variance. Significant predictors of risky driving included imitation of the driving behaviours of, and anticipated rewards and punishments administered by, parents and peers. Road safety policy that directly considers and incorporates these factors in their design, implementation, and enforcement of young driver road safety interventions should prove more efficacious than current approaches.

Barriers and facilitators of a designated driver program

The general aim of designated driver programs is to reduce the level of drink driving by encouraging potential drivers to travel with a driver who has abstained from (or at least limited) consuming alcohol. Designated driver programs are quite widespread around the world, however a limited number have been rigorously evaluated. This paper reports the qualitative results from an evaluation of a designated driver program known as ‘Skipper’, in a provincial city in Queensland. Focus groups were conducted with 108 individuals from the intervention area. These focus groups aimed to assess the barriers and facilitators to the programs’ effectiveness by obtaining information about the patrons’ views on various aspects of the program, as well as designated driver and travelling after drinking more generally. A brief questionnaire was also given to participants in order to present responses in terms of the participants’ characteristics. Results suggest general support for the designated driver concept and the ‘Skipper’ program specifically. Facilitating factors reported by participants included the media coverage highlighting the risks associated with drink driving and the social acceptability of choosing not to drink. However, there was also some suggestion that the impact of the program was mainly to encourage those who already engage in designated driver behaviour to continue doing so, rather than encouraging the uptake of the behaviour among potential new users. Some of the suggested barriers to this kind of behaviour change include: social pressure to drink; alcohol dependency; and a failure to plan ahead.