Although it is well known that drivers’ accident risk changes with experience, it has never been specified exactly how this comes about in terms of changes of behaviour, or what features of their experiences are important for this change. One possibility is that drivers learn from their collision involvement, and change their behaviour after such events, as some studies indicate. However, relative accident involvement tends to be very stable over time, which indicates the opposite. Repeated measurements of celeration (speed change) behaviour of bus drivers were compared between two groups; drivers without accidents within the measurement period (about 3 years), and drivers with at least one crash. For the crash group, there was a steady decline in their celeration values over time, but this was not related to their crashes. A similar reduction was also present for the non-crash sample. The results would seem to be in agreement with the theory of accident proneness; there exist stability in driver behaviour over time, despite accident involvement. However, this stability is relative within the sample, and not absolute. The reduction in celeration values for both groups over time would seem to indicate that drivers learn from their experiences in general, but not specifically from accidents. The present study seems to indicate that daily experience of driving situations is the strongest factor for changes in driving behaviour.

A study was undertaken to investigate whether driver celeration (overall mean speed change) behavior can predict traffic accident involvement. Also, to test whether acceleration, deceleration or the combined celeration measure was the better predictor. Bus driver celeration behavior was measured repeatedly in real traffic, driving en route, and correlated with accidents for which the drivers were deemed at least partly responsible. Correlations around. 20 were found in several samples between celeration behavior and culpable accidents for a 2-year period. The results show that although celeration behavior is only semi-stable over time, it predicts with some accuracy individual accident involvement over 2 years. The predictive power of acceleration and deceleration was slightly lower than the combined measure, in accordance with theory. The correlations found were strong enough to warrant the use of celeration behavior as a predictive variable for transportation companies in their safety work.

Traffic accident risk has in some studies been found to change with the time of day, after controlling for exposure, probably due to diurnal changes in the human body, which changes alertness. However, exposure data are not always of good quality, and culpability for accidents is not always taken into account. The change in culpable accident risk over the day for bus drivers was therefore investigated, with single accidents analysed separately, using induced exposure (non-culpable bus accidents) as well as general traffic density and number of buses on the road as controlling factors. It was found that the risk distribution was fairly similar to some previous results before controlling for exposure, but dissimilar to other, probably indicating that bus drivers have a somewhat different risk profile, but also that previous studies may not have controlled for exposure in a reliable way. When exposure was held constant, the risk distribution was different from all other studies. The three different exposure measures correlated strongly between themselves, and each would seem to be adequate for a basic control. However, although general traffic density was most strongly correlated with culpable bus accidents, the induced exposure parameter added some explained variance. Single accidents had a very different risk distribution as compared to other culpable accidents when exposure had been held constant. A number of unexpected effects were also noted, mainly that single accidents were associated most strongly with general traffic density.

New ways of educating offending drivers are being introduced, notably e-learning. This type of education has rarely been tested for its safety effects before. An e-learning course for offending young drivers was therefore evaluated as to its effects upon offence and self-reported collision rates. Significant reductions in number of offences and penalty points were found for an e-learning group, while this was not the case for drivers who had been fined only, or had taken a more traditional solely class-room based educational scheme. The e-learners also reported a larger reduction in collision involvement than a random control group, although a regression to the mean effect could not be ruled out. The results seem to indicate a positive effect of the e-learning course for young driving offenders. This conclusion, however, is to be interpreted in relation to the weak association between penalty points and collisions, and the low validity of self-reported collision involvement data. The present results lend further support to the use of e-learning driver improvement courses, although the most important type of data, recorded collisions, is still missing.

Problem: The use of lie scales to control for common method variance in driver behavior inventories has been very limited. Given that such questionnaires often use self-reported safety variables as criteria, and have social implications, the risk of artefactual associations is high. Method: A questionnaire containing scales from several well known driver inventories that have been claimed to predict traffic accident involvement was distributed three times to a group of young drivers in a driver education program, as well as a random group twice. The Driver Impression Management scale (DIM) was used to control for socially desirable responding. Results: For all behavior scales, the correlation with the DIM scale was substantial. If a scale correlated with self-reported crashes, the amount of predictive power was more than halved when social desirability was controlled for. Results were similar for both samples and all waves. The predictive power of the behavior scales was not increased when values were averaged over questionnaire waves, as should have been the case if the measurement and predictive power were valid. Results were similar for self-reported penalty points. The present results indicate that even the most well-known and accepted psychometric scales used in driver research are susceptible to social desirability bias. Discussion: As social desirability is only one of a number of common method variance mechanisms that can create artefactual associations, and the great popularity of the self-report methodology, the problem for traffic research is grave. Impact on industry: Organizations that fund traffic safety research need to re-evaluate their policies regarding what methods are acceptable. The use of self-reported independent and dependent variables can lead to directly misleading results, with negative effects on traffic safety.

Problem: The driver celeration behavior theory predicts that celerations are associated with incidents for which the driver has some responsibility in causing, but not other incidents. Method: The hypothesis was tested in 25 samples of repeated measurements of bus drivers' celeration behavior against their incidents for two years. Results: The results confirmed the prediction; in 18 samples, the correlation for culpable incidents only was higher than for all incidents, despite the higher means of the latter. Non-culpable incidents had correlations close to zero with celeration. Discussion: It was pointed out that most individual crash prediction studies have not made this differentiation, and thus probably yielded underestimates of the associations sought, although the effect is not strong, due to non-culpable accident involvements being few (less than a third of the total). The methods for correct identification of culpable incident involvements were discussed.

The use of lie scales has a fairly long history in psychometrics, with the intention of identifying and correcting for socially desirable answers. This represents one type of common method variance (bias introduced when both predictors and predicted variables are gathered from the same source), which may lead to spurious associations in self-reports. Within traffic safety research, where self-report methods are used abundantly, it is uncommon to control for social desirability artifacts, or reporting associations between lie scales, crashes and driver behaviour scales. In the present study, it was shown that self-reports of traffic accidents were negatively associated with a lie scale for driving, while recorded ones were not, as could be expected if the scale was valid and a self-report bias existed. We conclude that whenever self-reported crashes are used as an outcome variable and predicted by other self-report measures, a lie scale should be included and used for correcting the associations. However, the only existing lie scale for traffic safety is not likely to catch all socially desirable responding, because traffic safety may not be desirable for all demographic groups. New lie scales should be developed specifically for driver behaviour questionnaires, to counter potential bias and artifactual results. Alternatively, the use of a single source of data should be discontinued.

Problem

Various indicators of health have been shown to be associated with traffic crash involvement. As general health is also related to absence from work, the latter variable may be more strongly related to crashes, especially for professional drivers.

Method

Bus driver absence from work was analyzed in association with their crash records. Two British samples and one Swedish sample were used.

Results

One of the British samples yielded fair correlations between crash record and absence, while for the other the effect was restricted to the first three months of driving. The Swedish data had effects in the expected direction but these were not significant.

Discussion

The use of an indirect, overall measurement of health, may be a viable method for predicting the traffic crash involvement for professional drivers, although replications are needed in larger samples and other populations.

Impact on industry

The use of absence records for the identification of at risk drivers would seem to be a simple and useful method for companies with major fleets, and it also shows the importance of promoting employee health and well being at work as a potential method of reducing the cost, not only of absenteeism, but also of crashes in company vehicles.

Introduction: It is often implicitly or explicitly assumed in traffic accident research that drivers with accidents designated as non-culpable are a random sample from the population. However, this assumption is dependent upon differences in the criterion used for culpability. If drivers are erroneously categorized by assuming randomness, results could be grossly misleading. Method: The assumption of randomness leads to two predictions: first, no correlation should exist between culpable and non-culpable crashes; and second, the accident groups should differ on the variables known to be associated with accidents, such as amount of driving experience. These predictions were tested in two samples of bus drivers. Results: It was found that in a sample with a harsh criterion (70% culpable accidents) for crash responsibility, the drivers with non-culpable accidents had the features expected, namely, they were more experienced for example, while in a sample with a lenient criterion (50 % culpable), this was not so. Discussion: It was concluded that similar studies to the present one would need to be undertaken to establish exactly what percentage of drivers in a given population should be assigned culpable accidents, and construct a criterion that yields this ratio. Otherwise, the theoretical assumptions of randomness and non-responsibility will probably be violated to some degree. Impact on Industry: Many estimates of risk of crash involvement may have been wrong. Given the potential for erroneous criteria, a number of studies may make invalid assumptions from their data.

There are likely to be individual differences in bus driver behaviour when adhering to strict schedules under time pressure. A reliable and valid assessment of these individual differences would be useful for bus companies keen to mitigate risk of crash involvement. This paper reports on three studies to develop and validate a self-report measure of bus driver behaviour. For study 1, two principal components analyses of a pilot questionnaire revealed six components describing bus driver behaviour and four bus driver coping components. In study 2, test-retest reliability of the components were tested in a sub-sample and found to be adequate. Further, the 10 components were used to predict bus crash involvement at three levels of culpability with consistently significant associations found for two components. For study 3, avoidance coping was consistently associated with celeration variables in a bus simulator, especially for a time-pressured drive. Statement of Relevance: The instrument can be used by bus companies for driver stress and fatigue management training to identify at-risk bus driver behaviour. Training to reduce the tendency to engage in avoidance coping strategies, improve evaluative coping strategies and hazard monitoring when under stress may improve bus driver safety.

The effects of age and experience on accident involvement for bus drivers were investigated, with special emphasis upon the first years of being an operator, using two methods. First, direct calculations between these variables were undertaken. Thereafter, a variant of the method of quasi-induced exposure (a ratio of culpable versus nonculpable accidents in the population) was used and referred to as the indirect method. These methods yielded fairly similar results, given that the samples used were drawn from the same population but only partly overlapping. It was found that experience had the strongest effect on accidents in the first year of driving, while age had a u-shaped association with accidents, that is, young and old drivers had more accidents, something that was more apparent when experience was held constant. These results show that, for bus drivers, experience is initially more important than age, but after two or three years, the effect is small. Thereafter, age is the more discernible variable, although it is a very weak factor in predicting crash risk.