BIBIOGRAFÍA Y REFERENCIAS

Validity of accident data. A wide range of studies and reviews have focused on acc ident data and driving behaviour (Cooper, 1 990; Evans, 1 99 3 ; Fahrenkrug & Klingeman, 1 993 ; French, West, Elander & Wilding, 1 993; Hakamies-Blomqvist,

1 994 ). B roadly speaking, the val idity of accident data is compromised by the

definition of what constitutes an accident, indices of outcome and severity, and the type of recorded accident data. Definition of an accident is frequently constrained by whether or not it has been reported to an authority ( Risk, 1 98 1 ), and by available information (Galski et al. , 1 993). Nevertheless, despite these complications, accident frequency is a common method of gauging road safety and receives greater interest than non-accidental driving as a measurement criterion (Zimolong, 1 98 1 ).

Accident data is often implemented in the task analysis of specific driving situations. Evidence suggests that driver workload is correlated with detection failure and accident risk ( Hancock et al. , 1 990). More common , however, are studies which analyse the rel ationship between accident statistics and driver characteristics (Forbes, 1 972; Fahrenkrug & Klingemann, 1 993 ; Peck, 1 993; Sivak, 1 98 1 ) . The validity of these studies is limited by single factor approaches and broad assumptions about accident causation. In contrast, there are a lack of holistic approaches to the epidemiology of traffic acc idents which accommodate the combined effects of multiple factors. For i nstance, a 'systems' model approach would interpret traffic accidents as a fai lure within the person-machine-environment system. Where apparent failure occurs is open to interpretation (Willumeit et al., 1 98 1 ) .

Researc h designs. Accident data can be obtained from several sources using different research methods. A common method of investigation are large scale studies using archival data. However, archival records are often unre liable due to changes in policy and documented recording of events (Elvik, 1 988; Nicholl, 1 98 1 ; Zimolong, 1 98 1 ). Apart from these types of classification errors, archival sources may be insufficient when used out of context (Nicholl, 1 98 1 ). Hospital inj ury data, for

C H A PTER T H R EF.

characteristics, despite the fact that extent of injury (human consequences ) is the most accepted index of accident severity.

I nterview and questionnaire techniques are also used to collect accident data. Here, a more complete and integrated picture of indi vidual drivers is possible through d i fferentiating accident types. For example, one study of persons with CV A obtained accident data using operationally defined categories such as minor incidents and i nc i dents causing damage which were/were not reported to an insurance company ( S imms. 1 985b). Similarly, Cooper ( 1 990) used an interview technique to elicit more quali tati ve data on accidents among several groups of older drivers. Thi s type of data wou ld appear to be a better indicator of driving patterns and is more relevant to drivers with impairment where numerous small incidents are equally i mportant in an overall configuration of driving (Simms 1 985b). On the other hand, there is a reliance on self­ report and memory for events over what is a highly sensitive topic.

Other research designs have examined the interplay between a number of individual and envi ronmental factors. For example, one longitudinal study examined accident characteristics of older dri vers, with emphasis on responsibility for self-caused acci dents ( Hakamies-Blomqvist, 1 994). Here, accidents caused by the older subjects were di fferent from the younger comparison group. Older subjects had more accidents at intersections, caused either by the subject not seeing or not acting quickly enough to another vehicle turning into their path. In another study, which adopted an integrated approach, the role of driv i ng exposure in crash risk between drivers and driving environments was examined (Chipman, MacGregor, Smi ley & Lee-Gosselin, 1 993). Here. there were apparent differences in crash risk per kilometre which could be expla ined by differences in typical driving speed and environment, regardless of personal factors examined (e.g. age, gender). Further, exposure time was better than distance in explaining crash risk among drivers and regions with very different driving patterns and environments.

Ana lysis of accident data. The use of group data for making assumptions about

the individual is problematical, regardless of whether data is derived from archival or other sources. Research shows that accidents are highly variable, and thus may not be a val id i ndicator of driving behaviour for within subjects analysis, let alone between groups of subjects. Generally, there is a poor correlation between accidents in one

period and acc idents in another (Hauer, 1 986 ), although this is also dependent on what time frames are used for data collection. In this regard, Forbes, Nolan, Schmidt & Yanosdall ( 1 975) note that the "inherent unreliability of low probability events such as accidents makes predictive validity essentially impossible at the individual level" ( p.27 3 ) . However, they regard that accident data may be of practical use i n the comparison of large groups of drivers over relatively long time periods.

Interpretation of any data set is also limited by the range of different driving situations from which the accident data is taken. As highlighted by Cooper ( 1 990), most studies do not include contextual factors such as driving conditions, the impact of stress or fatigue, or whether the accident was the fault of the driver in question. Michon & Fairbank ( 1 969) provide the anecdote of the driver who is not involved but may be the cause of the accident itself!

Overall , there are many constraints on the use of accident data as either correlates or predictors of driving behaviour. The predictive power of individual data i s controversial because o f high variabil ity and the way in which analysis collapses numerous factors. Evidence suggests that many characteristics of individual drivers lack stability in certain driving situations, and therefore, cannot be used as overall predictors in accident involvement (Forbes, 1 972: Sivak, 1 98 1 ; Hauer, 1 986).