Research gaps

While there have been numerous studies attempting to determine the reasons for this disconnect by studying the influencing factors, magnitude and impact of risky driving

behaviour, most studies are limited by the reliance on self-reported and police- enforcement data both of which suffer from underreporting (Hatfield et al., 2008; Yamamoto et al., 2008). It has also been identified that crash data represents a very small proportion of all driving activity and this makes them vulnerable to random variability (Wundersitz and Hutchinson, 2012). Since casualty crashes account for a small proportion of vehicle crashes and vehicle crashes are in themselves extremely rare events, it has been argued that studying crashes is not the best way to study driver behaviour (Wundersitz and Hutchinson, 2012). Wundersitz and Hutchinson (2012) suggest that researchers should find proxies of driver behaviour that can be objectively observed and measured which have a direct link to road safety. However, this approach would require researchers to monitor drivers across time during their normal driving routines, a capability that is not possible using many widely used methods (of which self-reported and crash records are two) of studying driver behaviour.

This is reinforced by research which has shown that in addition to individual driver behaviour there is an inherent risk in each vehicle kilometre travelled (VKT) with higher risks of crashes associated with certain temporal (night) and spatial (rural) characteristics (Litman, 2010). Capturing this level of data is a necessary element in developing accurate risk profiles for individual motorists (Jun et al., 2007). Many of these limitations were imposed by limitations of aforementioned measurement techniques. However, wider availability of sensing technologies, primarily Global Positioning System (GPS) technology has improved the ability to relatively

unobtrusively collect large amounts of data from individual drivers (albeit at the expense of higher resource requirements and smaller sample sizes) (Greaves et al., 2010).

Possibly as a symptom of these limitations, studies and road safety campaigns have invariably attempted to categorise drivers by common demographics relying on the assumption that drivers of similar age and gender are similar in their perception and attitudes towards risk, and individual behaviour. Evidence – particularly from the literature on speeding – suggests that at least in terms of on-road driving behaviour this is not an entirely accurate assumption (Greaves and Ellison, 2011) and therefore

driver risk assessment requires a more robust approach. Although improved road safety campaigns are not an outcome of this thesis, it is expected that the findings and techniques developed will improve the effectiveness of future road safety interventions and provide a means for evaluating the benefits of future campaigns and

interventions.

1.3 Contribution

This research focuses on two over-arching themes. The first relates to identifying if drivers‘ risk perceptions, concerns of injury, confidence in their driving skills and personalities can be used to predict the frequency and magnitude of their speeding, acceleration and braking behaviour within their normal driving routines – that is, outside of the controlled environment of a driving simulator or survey environment. The second theme relates to how driver behaviour can be improved – as defined by reductions in speeding, aggressive acceleration and aggressive braking – by making drivers both aware of what they are doing and providing a financial incentive to change behaviour. In the process of investigating these issues, this thesis makes a number of contributions to research and practice.

1.3.1 Research and methodology

This thesis makes contributions to the road safety literature and methodological techniques, designed to improve understanding of driver behaviour. The research contributions include:

 Introducing processes for integrating naturalistic driving data with related road environment, trip information, driver characteristics, attitudes and personality together with detailed responses to multi-faceted interventions (Chapter 5);  Designing a methodology for controlling for the influence of the road

environment in analyses of naturalistic driving which can also be used as a method of aggregation that retains the same structure as the disaggregate datasets (Chapter 7);

 Developing a framework and methodology for describing drivers‘ speeding, acceleration and braking behaviour as a function of the risk of a fatality crash at any level of aggregation incorporating different magnitudes, frequencies and

VKT, which provides an effective method of measuring changes in behaviour across time and space (Chapter 8);

 Developing a driver behaviour profiling/scoring approach, that incorporates several behavioural measures into a single composite driver behaviour profile that can be used to describe an entirety of driver‘s behaviour (Chapter 8);  Employing multilevel/hierarchical modelling to identify variables that (in combination) predict drivers‘ speeding, acceleration and braking behaviour (Chapter 9);

 Employing multilevel/hierarchical modelling to identify changes in driving behaviour that occur as a result of informing drivers of their speeding behaviour and providing a financial incentive to reduce their speeding behaviour (Chapter 10); and

 Identifying a number of implications for research on driver behaviour and before-and-after studies (Chapter 11).

1.3.2 Practice and policy

In addition to the contributions to research, this thesis also makes a number of contributions to practice and policy. These contributions can be applied towards improving the effectiveness of road safety policies and strategies. These include:  Identifying the potential to redesign the road environment to limit drivers‘

ability to drive in excess of the posted speed limit (Chapter 11);

 Introducing a framework and tool for describing driver behaviour as a function of risk that can be applied to measure the effectiveness of road safety strategies (Chapter 8);

 Identifying that risk perceptions and personality are related to speeding behaviour – and that these relationships are stronger than demographics – which can be used to improve the design and targeting of road safety campaigns (Chapter 9);

 Identifying two broad groups of drivers, the largest of which comprising

approximately 80 percent of drivers, can be encouraged to reduce their speeding behaviour by making them aware of their speeding behaviour and/or providing a financial incentive (Chapter 10); and

 Recommending a suite of hard and soft policy measures, based on the findings of this research, which could be applied to reduce speeding behaviour among both of the aforementioned groups (Chapter 11).