Environmental determinants of celeration behaviour.

Celeration (speed change) behaviour of drivers has been posited to be the best predictor of their traffic accident involvement. The origins of this behaviour, however, have not been specified. A model is therefore introduced, where celeration is partly due to the individual disposition of the driver (i.e., driving style), and partly to the environment (road layout, rules and traffic density). Three measurement problems for celeration were studied; the effect of traffic density, of regular versus irregular routes, and weight of the vehicle (loaded/unloaded) on celeration behaviour. Two small samples of truck drivers in Sweden were measured for several months each. There was a strong effect of vehicle load, with behaviour being more cautious with increased weight. Driving on different roads also yielded differences in behaviour, although the design used did not permit conclusions about what caused these. Traffic volume was not found to have any reliable effect on celeration.

Accuracy of conversion formula for effect sizes: A Monte Carlo simulation.

In meta-analysis, effect sizes often need to be converted into a common metric. For this purpose conversion formulas have been constructed; some are exact, others are approximations whose accuracy has not yet been systematically tested. We performed Monte Carlo simulations where samples with pre-specified population correlations between the x and y-variables were drawn from a normally distributed population. A number of commonly used effect size measures and statistics were calculated from each sample. Using several available conversion formula these statistics were converted into Pearson r and Cohen's d and compared to r and d calculated directly from the original data. Converted values were systematically lower than the directly calculated values. While conversions to d were quite accurate, some of the conversions to r resulted in large biases. These systematic errors can in most cases be adjusted for by simply multiplying the converted values with a corresponding correction factor.

Development and validation of a self-report measure of bus driver behaviour.

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.

Work-related road safety: an analysis based on U.K. bus driver performance.

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.

Long-term prediction of traffic accident record from bus driver celeration behavior.

Driver celeration (speed change) behavior of bus drivers measured a number of times was used to predict their culpable accidents over increasing time periods. It was found that predictive power was considerable (>.30 correlation) over 5 years of time with aggregated celeration (mean of repeated measurements) as independent variables, and there were also indications that power reached even further, although too low Ns made these results unreliable. Similarly, there were indications of even stronger correlations with increased aggregation of celeration values. The results were discussed in terms of the methodology needed to bring out such results, and the stability of accident-causing behavior over time.

Driver celeration behavior and the prediction of traffic accidents.

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.

Predicting Crashes Using Traffic Offences. A Meta-Analysis that Examines Potential Bias between Self-Report and Archival Data.

BACKGROUND: Traffic offences have been considered an important predictor of crash involvement, and have often been used as a proxy safety variable for crashes. However the association between crashes and offences has never been meta-analysed and the population effect size never established. Research is yet to determine the extent to which this relationship may be spuriously inflated through systematic measurement error, with obvious implications for researchers endeavouring to accurately identify salient factors predictive of crashes. METHODOLOGY AND PRINCIPAL FINDINGS: Studies yielding a correlation between crashes and traffic offences were collated and a meta-analysis of 144 effects drawn from 99 road safety studies conducted. Potential impact of factors such as age, time period, crash and offence rates, crash severity and data type, sourced from either self-report surveys or archival records, were considered and discussed. After weighting for sample size, an average correlation of r = .18 was observed over the mean time period of 3.2 years. Evidence emerged suggesting the strength of this correlation is decreasing over time. Stronger correlations between crashes and offences were generally found in studies involving younger drivers. Consistent with common method variance effects, a within country analysis found stronger effect sizes in self-reported data even controlling for crash mean. SIGNIFICANCE: The effectiveness of traffic offences as a proxy for crashes may be limited. Inclusion of elements such as independently validated crash and offence histories or accurate measures of exposure to the road would facilitate a better understanding of the factors that influence crash involvement.

Absence behavior as traffic crash predictor in bus drivers.

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.

Risk perception and new age beliefs.

This is a study of risk perception in relation to New Age (NA) beliefs, including traditional folk superstition and belief in paranormal phenomena, as well as use of alternative healing practices. Data were also obtained on trust dimensions and on personality and psychopathology variables, as well as religious involvement. It was found that four factors accounted for the investigated NA beliefs, which were termed higher consciousness beliefs, denial of analytic knowledge, traditional superstition, and belief in the physical reality of the soul. NA beliefs were strongly and positively related to religious involvement, and negatively to educational level. These beliefs were also positively related to maladjustment and to concerns over tampering with nature. In regression analyses, it was found that NA beliefs explained about 15% of the variance of perceived risk, and that the most powerful explanatory factors were higher consciousness beliefs and beliefs in paranormal phenomena. Traditional superstition and use of healing practices did not contribute to explaining perceived risk.