Property damage crash equivalency factors to solve crash frequency-severity dilemma: Case study on South Korean rural roads

Safety interventions (e.g., median barriers, photo enforcement) and road features (e.g., median type and width) can influence crash severity, crash frequency, or both. Both dimensions—crash frequency and crash severity—are needed to obtain a full accounting of road safety. Extensive literature and common sense both dictate that crashes are not created equal, with fatalities costing society more than 1,000 times the cost of property damage crashes on average. Despite this glaring disparity, the profession has not unanimously embraced or successfully defended a nonarbitrary severity weighting approach for analyzing safety data and conducting safety analyses. It is argued here that the two dimensions (frequency and severity) are made available by intelligently and reliably weighting crash frequencies and converting all crashes to property-damage-only crash equivalents (PDOEs) by using comprehensive societal unit crash costs. This approach is analogous to calculating axle load equivalents in the prediction of pavement damage: for instance, a 40,000-lb truck causes 4,025 times more stress than does a 4,000-lb car and so simply counting axles is not sufficient. Calculating PDOEs using unit crash costs is the most defensible and nonarbitrary weighting scheme, allows for the simple incorporation of severity and frequency, and leads to crash models that are sensitive to factors that affect crash severity. Moreover, using PDOEs diminishes the errors introduced by underreporting of less severe crashes—an added benefit of the PDOE analysis approach. The method is illustrated with rural road segment data from South Korea (which in practice would develop PDOEs with Korean crash cost data).

Assessment of damages for property damage

In Gagner Pty t/as Indochine Café v Canturi Corporation Pty Ltd (2009) 262 ALR 691, the assessment of damages awarded for the rectification work to the premises of the respondent was in issue. The appellant operated a restaurant above the respondent’s jewellery store in Sydney. When the kitchen of the restaurant flooded, water escaped causing damage to the jewellery store’s fit-out. The escape of the water was held to be due to the negligence of persons for whom the appellant was vicariously liable. The trial judge awarded damages, measured by the amount required to return the premises as close as was possible to the condition prior to the flood damage as well as an allowance for interruption to the business for 10 days. The 10 day allowance reflected the number of days the store would have been closed for if it was to be returned to its previous condition. The evidence was that the flooding has only affected approximately 10% of the floor area of the store. However, instead of having work carried out to bring the premises back to its condition as before the water damage, the respondent closed the business for 29 working days for a complete internal refurbishment – at a cost substantially more than simple rectification. On appeal it was argued that the trial judge had assessed the damages incorrectly as by undertaking a complete refurbishment had the effect that the respondent did not suffer any loss as a consequence of the negligence in relation to the fit-out. It was asserted that the claim for damages was in the circumstances a claim for betterment. It was also argued that the damages should not include a component for GST. Campbell JA gave reasons, with Macfarlan JA and Sackville AJA agreeing.

Applying quantile regression for modeling equivalent property damage only crashes to identify accident blackspots

Hot spot identification (HSID) aims to identify potential sites—roadway segments, intersections, crosswalks, interchanges, ramps, etc.—with disproportionately high crash risk relative to similar sites. An inefficient HSID methodology might result in either identifying a safe site as high risk (false positive) or a high risk site as safe (false negative), and consequently lead to the misuse the available public funds, to poor investment decisions, and to inefficient risk management practice. Current HSID methods suffer from issues like underreporting of minor injury and property damage only (PDO) crashes, challenges of accounting for crash severity into the methodology, and selection of a proper safety performance function to model crash data that is often heavily skewed by a preponderance of zeros. Addressing these challenges, this paper proposes a combination of a PDO equivalency calculation and quantile regression technique to identify hot spots in a transportation network. In particular, issues related to underreporting and crash severity are tackled by incorporating equivalent PDO crashes, whilst the concerns related to the non-count nature of equivalent PDO crashes and the skewness of crash data are addressed by the non-parametric quantile regression technique. The proposed method identifies covariate effects on various quantiles of a population, rather than the population mean like most methods in practice, which more closely corresponds with how black spots are identified in practice. The proposed methodology is illustrated using rural road segment data from Korea and compared against the traditional EB method with negative binomial regression. Application of a quantile regression model on equivalent PDO crashes enables identification of a set of high-risk sites that reflect the true safety costs to the society, simultaneously reduces the influence of under-reported PDO and minor injury crashes, and overcomes the limitation of traditional NB model in dealing with preponderance of zeros problem or right skewed dataset.

Summary of property damage control programs of the United States Department of Energy.

"DOE/EV-0053."

Summary of property damage experience and loss control programs of the United States Department of Energy.

Mode of access: Internet.

Response and damage evaluation of reinforced concrete frames subjected to blast loading

Bomb attacks carried out by terrorists, targeting high occupancy buildings, have become increasingly common in recent times. Large numbers of casualties and property damage result from overpressure of the blast followed by failing of structural elements. Understanding the blast response of multi-storey buildings and evaluating their remaining life have therefore become important. Response and damage analysis of single structural components, such as columns or slabs, to explosive loads have been examined in the literature, but the studies on blast response and damage analysis of structural frames in multi-storey buildings is limited and this is necessary for assessing the vulnerability of them. This paper investigates the blast response and damage evaluation of reinforced concrete (RC) frames, designed for normal gravity loads, in order to evaluate their remaining life. Numerical modelling and analysis were carried out using the explicit finite element software, LS DYNA. The modelling and analysis takes into consideration reinforcement details together and material performance under higher strain rates. Damage indices for columns are calculated based on their residual and original capacities. Numerical results generated in the can be used to identify relationships between the blast load parameters and the column damage. Damage index curve will provide a simple means for assessing the damage to a typical multi-storey building RC frame under an external bomb circumstance.

Damage propagation in reinforced concrete frames under external blast loading

Iconic and significant buildings are the common target of bombings by terrorists causing large numbers of casualties and extensive property damage. Recent incidents were external bomb attacks on multi-storey buildings with reinforced concrete frames. Under a blast load circumstance, crucial damage initiates at low level storeys in a building and may then lead to a progressive collapse of whole or part of the structure. It is therefore important to identify the critical initial influence regions along the height, width and depth of the building exposed to blast effects and the structure response in order to assess the vulnerability of the structure to disproportionate and progressive collapse. This paper discusses the blast response and the propagation of its effects on a two dimensional reinforced concrete (RC) frame, designed to withstand normal gravity loads. The explicit finite element code, LS DYNA is used for the analysis. A complete RC portal frame seven storeys by six bays is modelled with reinforcement details and appropriate materials to simulate strain rate effects. Explosion loads derived from standard manuals are applied as idealized triangular pressures on the column faces of the numerical models. The analysis reports the influence of blast propagation as displacements and material yielding of the structural elements in the RC frame. The effected regions are identified and classified according to the load cases. This information can be used to determine the vulnerability of multi-storey RC buildings to various external explosion scenarios and designing buildings to resist blast loads.

Damage assessment of 3D reinforced concrete frame under external explosion loading

Multi-storey buildings are highly vulnerable to terrorist bombing attacks in various parts of the world. Large numbers of casualties and extensive property damage result not only from blast overpressure, but also from the failing of structural components. Understanding the blast response and damage consequences of reinforced concrete (RC) building frames is therefore important when assessing multi-storey buildings designed to resist normal gravity loads. However, limited research has been conducted to identify the blast response and damage of RC frames in order to assess the vulnerability of entire buildings. This paper discusses the blast response and evaluation of damage of three-dimension (3D) RC rigid frame under potential blast loads scenarios. The explicit finite element modelling and analysis under time history blast pressure loads were carried out by LS DYNA code. Complete 3D RC frame was developed with relevant reinforcement details and material models with strain rate effect. Idealised triangular blast pressures calculated from standard manuals are applied on the front face of the model in the present investigation. The analysis results show the blast response, as displacements and material yielding of the structural elements in the RC frame. The level of damage is evaluated and classified according to the selected load case scenarios. Residual load carrying capacities are evaluated and level of damage was presented by the defined damage indices. This information is necessary to determine the vulnerability of existing multi-storey buildings with RC frames and to identify the level of damage under typical external explosion environments. It also provides basic guidance to the design of new buildings to resist blast loads.

Assessing the immediate and short-term impact of flooding on residential property participant behaviour

The past decade has seen an increase in the number of significant natural disasters that have caused considerable loss of life as well as damage to all property markets in the affected areas. In many cases, these natural disasters have not only caused significant property damage, but in numerous cases, have resulted in the total destruction of the property in the location. With these disasters attracting considerable media attention, the public are more aware of where these affected property markets are, as well as the overall damage to properties that have been damaged or destroyed. This heightened level of awareness has to have an impact on the participants in the property market, whether a developer, vendor seller or investor. To assess this issue, a residential property market that has been affected by a significant natural disaster over the past 2 years has been analysed to determine the overall impact of the disaster on buyer, renter and vendor behaviour, as well as prices in these residential markets. This paper is based on data from the Brisbane flood in January 2011. This natural disaster resulted in loss of life and partial and total devastation of considerable residential property sectors. Data for the research have been based on the residential sales and rental listings for each week of the study period to determine the level of activity in the specific property sectors, and these are also compared to the median house prices for the various suburbs for the same period based on suburbs being either flood affected or flood free. As there are 48 suburbs included in the study, it has been possible to group these suburbs on a socio-economic basis to determine possible differences due to location and value. Data were accessed from realestate.com.au, a free real estate site that provides details of current rental and sales listings on a suburb basis, RP Data a commercial property sales database and the Australian Bureau of Statistics. The paper found that sales listings fell immediately after the flood in the affected areas, but there was no corresponding fall or increase in sales listings in the flood-free suburbs. There was a significant decrease in the number of rental listings follow the flood as affected parties sought alternate accommodation. The greatest fall in rental listings was in areas close to the flood-affected suburbs indicating the desire to be close to the flooded property during the repair period.

Fijian coral reef damage and recovery from Cyclone Kina

EXTRACT (SEE PDF FOR FULL ABSTRACT): Early in 1993, Cyclone Kina struck the Fiji Islands, causing more than $100 million in property damage and damaging the coral environment as well. A few days after the cyclone, the most damaged reef was studied. The same reef had been studied 6 months before. This reef crest is dominated by Acropora. Comparison showed that 80-90% of the Acropora was torn from the outer reef and deposited in the inner lagoon. ... It is estimated that it will take a few years to 30 years for the reef to recover to pre-Kina conditions.

Building a successful partnership to increase affordable rental housing supply in Queensland

Accessibility to housing for low to moderate income groups in Australia has been experiencing a severe decline since 2001. On the supply side, the public sector has been reducing its commitment to the direct provision of public housing. Despite high demand for affordable housing, there has been limited supply generated by non-government housing providers. One possible solution to promote an increase in affordable housing supply, like other infrastructure, is through the development of multi-stakeholder partnerships and private financing. This research aims to identify current issues underlying decision-making criteria for building multi-stakeholder partnerships to deliver affordable housing projects. It also investigates strategies for minimising risk and ensuring the financial outcomes of these partnership arrangements. A mix of qualitative in-depth interviews and quantitative surveys has been used as the main method to explore stakeholder experiences regarding their involvement in partnership arrangements in the affordable housing sector in Queensland. Two sets of interviews were conducted following an exploratory pilot study: one set in 2003-2004 and the other in 2007-2008. There were nineteen respondents representing government, private and not-for-profit organisations in the first stage interviews and surveys. The second stage interviews were focussed on twenty-two housing providers in South East Queensland. Initial analyses have been conducted using thematic and statistical analyses. This study extends the use of existing decision making tools and combines the use of a Soft System Framework to analyse the ideal state questionnaires using qualitative thematic analysis. Soft System Methodology (SSM) has been used to analyse this unstructured complex problem by using systematic thinking to develop a conceptual model and carrying it to the real world situations to solve the problem. This research found that the diversity of stakeholder capability and their level of risk acceptance will allow partnerships to develop the best synergies and a degree of collaboration which achieves the required financial return within acceptable risk parameters. However, some of the negativity attached to future commitment to such partnerships has been found to be the anticipation of a worse outcome than that expected from independent action. Many interviewees agree that housing providers' fear of financial risk and community rejection has been central to dampening their enthusiasm for entering such investment projects. The creation of a mixed-use development structure will mitigate both risk and return as the commercial income will subsidise the affordable housing development and will normalise concentration of marginalised low-income people who live in a prime location with an award winning design. In addition, tenant support schemes and rent-to-buy incentive programs will encourage them to secure their tenancies and significantly reduce the risk of rent arrears and property damage. There is also a breakthrough investment vehicle offered by the social developer which sells the non-physical but financial product to individual and institutional investors to mitigate further financial risk. Finally, this study recommends modification of the current value-for-money framework in favour of broader partnership arrangements which are more closely aligned with risk minimisation strategies.

Forecasting crashes at the planning level: Simultaneous negative binomial crash model applied in Tucson, Arizona

At least two important transportation planning activities rely on planning-level crash prediction models. One is motivated by the Transportation Equity Act for the 21st Century, which requires departments of transportation and metropolitan planning organizations to consider safety explicitly in the transportation planning process. The second could arise from a need for state agencies to establish incentive programs to reduce injuries and save lives. Both applications require a forecast of safety for a future period. Planning-level crash prediction models for the Tucson, Arizona, metropolitan region are presented to demonstrate the feasibility of such models. Data were separated into fatal, injury, and property-damage crashes. To accommodate overdispersion in the data, negative binomial regression models were applied. To accommodate the simultaneity of fatality and injury crash outcomes, simultaneous estimation of the models was conducted. All models produce crash forecasts at the traffic analysis zone level. Statistically significant (p-values < 0.05) and theoretically meaningful variables for the fatal crash model included population density, persons 17 years old or younger as a percentage of the total population, and intersection density. Significant variables for the injury and property-damage crash models were population density, number of employees, intersections density, percentage of miles of principal arterial, percentage of miles of minor arterials, and percentage of miles of urban collectors. Among several conclusions it is suggested that planning-level safety models are feasible and may play a role in future planning activities. However, caution must be exercised with such models.

Identifying large truck hot spots using crash counts and PDOEs

Large trucks are involved in a disproportionately small fraction of the total crashes but a disproportionately large fraction of fatal crashes. Large truck crashes often result in significant congestion due to their large physical dimensions and from difficulties in clearing crash scenes. Consequently, preventing large truck crashes is critical to improving highway safety and operations. This study identifies high risk sites (hot spots) for large truck crashes in Arizona and examines potential risk factors related to the design and operation of the high risk sites. High risk sites were identified using both state of the practice methods (accident reduction potential using negative binomial regression with long crash histories) and a newly proposed method using Property Damage Only Equivalents (PDOE). The hot spots identified via the count model generally exhibited low fatalities and major injuries but large minor injuries and PDOs, while the opposite trend was observed using the PDOE methodology. The hot spots based on the count model exhibited large AADTs, whereas those based on the PDOE showed relatively small AADTs but large fractions of trucks and high posted speed limits. Documented site investigations of hot spots revealed numerous potential risk factors, including weaving activities near freeway junctions and ramps, absence of acceleration lanes near on-ramps, small shoulders to accommodate large trucks, narrow lane widths, inadequate signage, and poor lighting conditions within a tunnel.

Modeling crash types: New insights into the effects of covariates on crashes at rural intersections

Many studies focused on the development of crash prediction models have resulted in aggregate crash prediction models to quantify the safety effects of geometric, traffic, and environmental factors on the expected number of total, fatal, injury, and/or property damage crashes at specific locations. Crash prediction models focused on predicting different crash types, however, have rarely been developed. Crash type models are useful for at least three reasons. The first is motivated by the need to identify sites that are high risk with respect to specific crash types but that may not be revealed through crash totals. Second, countermeasures are likely to affect only a subset of all crashes—usually called target crashes—and so examination of crash types will lead to improved ability to identify effective countermeasures. Finally, there is a priori reason to believe that different crash types (e.g., rear-end, angle, etc.) are associated with road geometry, the environment, and traffic variables in different ways and as a result justify the estimation of individual predictive models. The objectives of this paper are to (1) demonstrate that different crash types are associated to predictor variables in different ways (as theorized) and (2) show that estimation of crash type models may lead to greater insights regarding crash occurrence and countermeasure effectiveness. This paper first describes the estimation results of crash prediction models for angle, head-on, rear-end, sideswipe (same direction and opposite direction), and pedestrian-involved crash types. Serving as a basis for comparison, a crash prediction model is estimated for total crashes. Based on 837 motor vehicle crashes collected on two-lane rural intersections in the state of Georgia, six prediction models are estimated resulting in two Poisson (P) models and four NB (NB) models. The analysis reveals that factors such as the annual average daily traffic, the presence of turning lanes, and the number of driveways have a positive association with each type of crash, whereas median widths and the presence of lighting are negatively associated. For the best fitting models covariates are related to crash types in different ways, suggesting that crash types are associated with different precrash conditions and that modeling total crash frequency may not be helpful for identifying specific countermeasures.

The psychosocial factors influencing aggressive driving behaviour

Many drivers in highly motorised countries believe that aggressive driving is increasing. While the prevalence of the behaviour is difficult to reliably identify, the consequences of on-road aggression can be severe, with extreme cases resulting in property damage, injury and even death. This research program was undertaken to explore the nature of aggressive driving from within the framework of relevant psychological theory in order to enhance our understanding of the behaviour and to inform the development of relevant interventions. To guide the research a provisional ‘working’ definition of aggressive driving was proposed encapsulating the recurrent characteristics of the behaviour cited in the literature. The definition was: “aggressive driving is any on-road behaviour adopted by a driver that is intended to cause physical or psychological harm to another road user and is associated with feelings of frustration, anger or threat”. Two main theoretical perspectives informed the program of research. The first was Shinar’s (1998) frustration-aggression model, which identifies both the person-related and situational characteristics that contribute to aggressive driving, as well as proposing that aggressive behaviours can serve either an ‘instrumental’ or ‘hostile’ function. The second main perspective was Anderson and Bushman’s (2002) General Aggression Model. In contrast to Shinar’s model, the General Aggression Model reflects a broader perspective on human aggression that facilitates a more comprehensive examination of the emotional and cognitive aspects of aggressive behaviour. Study One (n = 48) examined aggressive driving behaviour from the perspective of young drivers as an at-risk group and involved conducting six focus groups, with eight participants in each. Qualitative analyses identified multiple situational and person-related factors that contribute to on-road aggression. Consistent with human aggression theory, examination of self-reported experiences of aggressive driving identified key psychological elements and processes that are experienced during on-road aggression. Participants cited several emotions experienced during an on-road incident: annoyance, frustration, anger, threat and excitement. Findings also suggest that off-road generated stress may transfer to the on-road environment, at times having severe consequences including crash involvement. Young drivers also appeared quick to experience negative attributions about the other driver, some having additional thoughts of taking action. Additionally, the results showed little difference between males and females in the severity of behavioural responses they were prepared to adopt, although females appeared more likely to displace their negative emotions. Following the self-reported on-road incident, evidence was also found of a post-event influence, with females being more likely to experience ongoing emotional effects after the event. This finding was evidenced by ruminating thoughts or distraction from tasks. However, the impact of such a post-event influence on later behaviours or interpersonal interactions appears to be minimal. Study Two involved the quantitative analysis of n = 926 surveys completed by a wide age range of drivers from across Queensland. The study aimed to explore the relationships between the theoretical components of aggressive driving that were identified in the literature review, and refined based on the findings of Study One. Regression analyses were used to examine participant emotional, cognitive and behavioural responses to two differing on-road scenarios whilst exploring the proposed theoretical framework. A number of socio-demographic, state and trait person-related variables such as age, pre-study emotions, trait aggression and problem-solving style were found to predict the likelihood of a negative emotional response such as frustration, anger, perceived threat, negative attributions and the likelihood of adopting either an instrumental or hostile behaviour in response to Scenarios One and Two. Complex relationships were found to exist between the variables, however, they were interpretable based on the literature review findings. Factor analysis revealed evidence supporting Shinar’s (1998) dichotomous description of on-road aggressive behaviours as being instrumental or hostile. The second stage of Study Two used logistic regression to examine the factors that predicted the potentially hostile aggressive drivers (n = 88) within the sample. These drivers were those who indicated a preparedness to engage in direct acts of interpersonal aggression on the road. Young, male drivers 17–24 years of age were more likely to be classified as potentially hostile aggressive drivers. Young drivers (17–24 years) also scored significantly higher than other drivers on all subscales of the Aggression Questionnaire (Buss & Perry, 1992) and on the ‘negative problem orientation’ and ‘impulsive careless style’ subscales of the Social Problem Solving Inventory – Revised (D’Zurilla, Nezu & Maydeu-Olivares, 2002). The potentially hostile aggressive drivers were also significantly more likely to engage in speeding and drink/drug driving behaviour. With regard to the emotional, cognitive and behavioural variables examined, the potentially hostile aggressive driver group also scored significantly higher than the ‘other driver’ group on most variables examined in the proposed theoretical framework. The variables contained in the framework of aggressive driving reliably distinguished potentially hostile aggressive drivers from other drivers (Nagalkerke R2 = .39). Study Three used a case study approach to conduct an in-depth examination of the psychosocial characteristics of n = 10 (9 males and 1 female) self-confessed hostile aggressive drivers. The self-confessed hostile aggressive drivers were aged 24–55 years of age. A large proportion of these drivers reported a Year 10 education or better and average–above average incomes. As a group, the drivers reported committing a number of speeding and unlicensed driving offences in the past three years and extensive histories of violations outside of this period. Considerable evidence was also found of exposure to a range of developmental risk factors for aggression that may have contributed to the driver’s on-road expression of aggression. These drivers scored significantly higher on the Aggression Questionnaire subscales and Social Problem Solving Inventory Revised subscales, ‘negative problem orientation’ and ‘impulsive/careless style’, than the general sample of drivers included in Study Two. The hostile aggressive driver also scored significantly higher on the Barrett Impulsivity Scale – 11 (Patton, Stanford & Barratt, 1995) measure of impulsivity than a male ‘inmate’, or female ‘general psychiatric’ comparison group. Using the Carlson Psychological Survey (Carlson, 1982), the self-confessed hostile aggressive drivers scored equal or higher scores than the comparison group of incarcerated individuals on the subscale measures of chemical abuse, thought disturbance, anti-social tendencies and self-depreciation. Using the Carlson Psychological Survey personality profiles, seven participants were profiled ‘markedly anti-social’, two were profiled ‘negative-explosive’ and one was profiled as ‘self-centred’. Qualitative analysis of the ten case study self-reports of on-road hostile aggression revealed a similar range of on-road situational factors to those identified in the literature review and Study One. Six of the case studies reported off-road generated stress that they believed contributed to the episodes of aggressive driving they recalled. Intense ‘anger’ or ‘rage’ were most frequently used to describe the emotions experienced in response to the perceived provocation. Less frequently ‘excitement’ and ‘fear’ were cited as relevant emotions. Notably, five of the case studies experienced difficulty articulating their emotions, suggesting emotional difficulties. Consistent with Study Two, these drivers reported negative attributions and most had thoughts of aggressive actions they would like to take. Similarly, these drivers adopted both instrumental and hostile aggressive behaviours during the self-reported incident. Nine participants showed little or no remorse for their behaviour and these drivers also appeared to exhibit low levels of personal insight. Interestingly, few incidents were brought to the attention of the authorities. Further, examination of the person-related characteristics of these drivers indicated that they may be more likely to have come from difficult or dysfunctional backgrounds and to have a history of anti-social behaviours on and off the road. The research program has several key theoretical implications. While many of the findings supported Shinar’s (1998) frustration-aggression model, two key areas of difference emerged. Firstly, aggressive driving behaviour does not always appear to be frustration driven, but can also be driven by feelings of excitation (consistent with the tenets of the General Aggression Model). Secondly, while the findings supported a distinction being made between instrumental and hostile aggressive behaviours, the characteristics of these two types of behaviours require more examination. For example, Shinar (1998) proposes that a driver will adopt an instrumental aggressive behaviour when their progress is impeded if it allows them to achieve their immediate goals (e.g. reaching their destination as quickly as possible); whereas they will engage in hostile aggressive behaviour if their path to their goal is blocked. However, the current results question this assertion, since many of the hostile aggressive drivers studied appeared prepared to engage in hostile acts irrespective of whether their goal was blocked or not. In fact, their behaviour appeared to be characterised by a preparedness to abandon their immediate goals (even if for a short period of time) in order to express their aggression. The use of the General Aggression Model enabled an examination of the three components of the ‘present internal state’ comprising emotions, cognitions and arousal and how these influence the likelihood of a person responding aggressively to an on-road situation. This provided a detailed insight into both the cognitive and emotional aspects of aggressive driving that have important implications for the design of relevant countermeasures. For example, the findings highlighted the potential value of utilising Cognitive Behavioural Therapy with aggressive drivers, particularly the more hostile offenders. Similarly, educational efforts need to be mindful of the way that person-related factors appear to influence one’s perception of another driver’s behaviour as aggressive or benign. Those drivers with a predisposition for aggression were more likely to perceive aggression or ‘wrong doing’ in an ambiguous on-road situation and respond with instrumental and/or hostile behaviour, highlighting the importance of perceptual processes in aggressive driving behaviour.