How reliable are self-report measures of mileage, violations and crashes?

The use of self-reported driver mileage, violations and crashes is very popular in traffic safety research, but their validity has been questioned. One way of testing validity is with an analysis of test–retest reliability. Three mechanisms might influence reliability in self report; actual changes in the variable over time, stable systematic reporting bias, and random error. Four samples of drivers who had responded twice to an online questionnaire asking them to report their mileage, violations and crashes were used and correlations between self reports for this data were calculated. The results for crashes were compared to expected correlations, calculated from the error introduced by the non-overlapping periods and the variable means. Reliability was fairly low, and controlling for mileage in the violations and crashes calculations did not strengthen the associations. The correlation between self reports of crashes in different time periods was found to be much larger than expected in one case, indicating a report bias, while the other correlation agreed with the predicted value. The correlations for overlapping time periods were much smaller than expected. These results indicate that drivers’ self reports about their mileage, violations and crashes are very unreliable, but also that several different mechanisms are operating. It is uncertain exactly under what circumstances different types of self report bias is operating. Traffic safety researchers should treat the use of self-reported mileage, violations and crashes with extreme caution and preferably investigate these variables with the use of objective data

Fuel efficient driving training - state of the art and quantification of effects

A new area of traffic education, training in fuel efficient driving, is reviewed. This training is often said to reduce fuel consumption, accidents, emissions, and wear and tear on vehicles. These claims, made mainly by educators and bureaucrats, and said to have scientific backing, are found to be wanting; most of the possible effects are totally unsubstantiated, while the most central, reduction in fuel consumption, is well below the highest figures mentioned. Research problems and general methodology regarding the variable of fuel consumption reduction are discussed. Although it is fairly easy to show the large potential of training under experimental conditions, it is rather complicated in a field setting. However, it is necessary to study the effects in the drivers' natural environment, because of the many possible sources of error in controlled settings which tend to inflate the effect. What is possible during training should therefore rather be seen as a maximum of what can be achieved, while the effect in real life driving is usually far below. Being a new area of research, it is uncertain exactly how effects should be measured, apart from fuel consumption. This problem is discussed and the results from a quantification of effects of training in fuel efficient driving are presented. The changes in driving style are described in terms of acceleration patterns; mean accelerations (over time) increased and mean decelerations decreased, while the time spent on a stable velocity decreased. Also, the mean acceleration and deceleration over distance was fairly well correlated with fuel consumption, and very clear differences could be seen on several acceleration-related variables as a result of training. These results show that acceleration patterns are a workable way of quantifying this type of training

Comparisons of predictive power for traffic accident involvement; Celeration behaviour versus age, sex, ethnic origin, and experience

Driver celeration behaviour theory (DCBT) assumes that risk for a driver of causing a road crash is linearly related to speed change in any given moment and that the speed change variable (celeration) captures all risk (all vehicle control movements can be measured as acceleration). When sampling driver behaviour, the celeration variable is calculated as the average of all absolute values of acceleration when the vehicle is moving. DCBT predicts that no other variable can be a stronger predictor of (the same set of) traffic accident involvements than celeration, given equal reliability of the predictors. Also, other predictors, regardless of which ones, should associate with celeration in ways that are similar to how they correlate with accidents. Predictions were tested in a sample of bus drivers, against variables with reliabilities close to 1 (age, sex, experience, ethnic origin), which are not necessarily optimal predictors for testing but were the only predictors available. The results were largely as predicted from theory. The principles for testing the kind of predictions made from celeration theory were discussed, outlining the importance of a larger number of variables, preferably with repeated measurements

The effect of driver improvement interventions on crash involvement; has it been under-estimated?

The available evidence suggests that driver improvement interventions (with the aim to increase driver safety, most often by education or training) do not work. The average effect calculated in several meta-analyses is close to, and not always possible to distinguish from, zero, despite total samples sizes of several hundred thousand drivers. However, it is possible that all studies included in these meta-analyses have under-estimated the effect, due to a methodological error; all crashes have been used as dependent variable, instead of only those that the targeted drivers have caused. This error is expected to have considerably deflated the effect sizes, but it is not known how large this effect could be. Using crash data for bus drivers in which culpability had been reliably established, a simple simulation was performed to determine the difference between using culpable and all crashes as an estimator of a safety effect. Using data for six years, calculations were made on single years. About ten percent of culpable crashes in each year were deleted to simulate a safety effect, where after the difference between the original and the simulated variable were calculated, using culpable only and all crashes in parallel. The effects using these two different kinds of datasets could then be compared and the under-estimation effect estimated. Culpable crashes, as compared to all crashes, yielded larger differences in means between time periods, and smaller standard deviations. In between-subjects comparisons resulted in 15–30 percent larger effects for culpable crashes. Within-subjects calculations yielded larger but not as systematic effects. The effect of driver improvement on crash involvement has been systematically under-estimated, as extremely few evaluation studies seem to have taken culpability for crashes into account. Therefore, new evaluations need to be undertaken, and/or old data re-analysed, to calculate a better estimate of the true effect of training and education in driving safety

Experience as a safety factor in driving; Methodological considerations in a sample of bus drivers

Experience is generally seen as an important factor for safe driving, but the exact size and details of this effect has never been meta-analytically described, despite a fair number of published results. However, the available data is heterogeneous concerning the methods used, which could lead to very different results. Such method effects can be difficult to identify in meta-analysis, and a within-study comparison might yield more reliable results. To test for the difference in effects between some different analytical methods, analyses of data on bus driver experience and crash involvement from a British company were conducted. Effects of within- and between-subjects analysis, non-linearity of effects, and direct and induced exposure methods were compared. Furthermore, changes in the environmental risk were investigated. Between-subject designs yielded smaller effects as compared to within-subjects designs, while non-linearity was not found. The type of exposure control applied had a strong influence on effects, as did differences in overall environmental risk between years. Apparently, “the effect of driving experience” means different things depending upon how calculations have been undertaken, at least for bus drivers. A full meta-analysis, taking several effects of methodology into account, is needed before it can be said that the effect of driving experience on crash involvement is well understood

Behavioural culpability for traffic accidents

This study presents a description of the concept of behavioural culpability, a step-by-step manual for using it, and an empirical test of a suspected mis-classification of culpability. Behavioural culpability is defined as whether the driver’s actions contributed to a crash and that non-culpable crashes are not caused by any specific behaviour and can only be predicted from exposure. Drivers with non-culpable crashes are therefore a random sample of the population. However, if the criteria for culpability and/or the individual judgements are not reflective of the principle of behavioural culpability, no fault drivers will not be a random sample of the driving population. To test the predictions from the definition of randomness in a sample assumed to have sub-optimal coding, the categorization of crash involvement undertaken by a British bus company was tested for associations between at fault and no fault crashes, age and experience. As predicted from the low percentage of at fault accidents in the sample, correlations between the variables indicated that a fair percentage of at fault crashes had been coded as no fault of the bus driver, suggesting a too lenient criterion. These results show that within fleet-based companies, culpability for a crash is probably allocated for legal reasons, which means that the predictability of accident involvement taking into account individual differences is not fully utilized. The aim of behavioural culpability coding is to increase effect sizes in individual differences in safety research and to improve our capability of predicting accident involvement

The effects of Electronic Stability Control (ESC) on fatal crash rates in the United States

Problem: Electronic Stability Control (ESC) is believed to be among the most efficient vehicle safety interventions with reported effects around 50% for fatal single and rollover crashes. However, such estimates have used sample data, which have not controlled for the possibilities of self-selection, behavioral adaptation, increased access to the technology by less safe drivers, and the calculation of effects on very specific categories of crashes. Effects of ESC in the population can therefore be expected to be smaller than is currently believed. Method: National U.S. data for fatal crashes, driving exposure and other control factors, and market penetration of ESC over 1991–2021 were used to calculate whether the trends in fatalities over time in crash rates for singles, rollovers, and fatal crashes in general matched projections from estimates of effectiveness. Results: It was found that downward trends in the relevant crash types were generally present before ESC was introduced, and that the trends thereafter were weaker. Although some trends were consistent with effects of ESC, they were markedly smaller than the projected ones, and could be explained by other factors such as the number of vehicles per capita. At best, the effect for rollovers could be up to two-thirds of previous estimates, no effect was detected for singles, while for all fatal crashes results depended upon the type of analysis performed. These results conflict with conclusions in all published ESC crash sample studies, which have compared vehicles with and without ESC. This discrepancy can be explained by methodological errors in the previous studies using induced exposure methods and self-selected samples. Practical applications: Traffic safety may not be as much improved by technological interventions as believed. Alternative approaches to traffic safety are needed, which do not rely on technology that interferes with driver behavior.Engineering and Physical Sciences Research Council: grant number: EP/V026763/

Accident proneness of bus drivers; controlling for exposure

It is argued that the reason that previous evidence apparently did not support the accident proneness hypothesis was faulty methodology and erroneous interpretations of results. Between time periods correlations of traffic accident records actually show an impressive stability over time when restriction of variance is controlled for. However, stability can be caused by stable differences in exposure. Correlations of accident records between time periods were analysed comparing full time and part-time bus drivers. For drivers who worked full time, the amount of exposure was held semi-constant while part-time drivers could be expected to work differing hours. If differential exposure causes stability in crash record, then part-time drivers should yield stronger correlations between time periods for crashes compared with full-time drivers. Between time periods accident correlations for part-time drivers were weaker than the corresponding ones for full time drivers. Correlations increased with increasing variance in the data. Results for all crashes fit in well with other meta-data, while culpable crashes did not, probably due to faulty coding. The current results support the notion of the tendency to cause traffic accidents as a stable trait within individuals as this is apparently not caused by stable differences in exposure

Bus driver accident record; stability over time, exposure and culpability

The tendency for drivers to have a stable accident record over time was tested in a population of bus drivers. Analyses included investigations of the effects of responsibility for the crash, exposure and length of time period, on stability. All associations between numbers of accidents for individuals in different short time periods were found to be weak, but longer time periods increased the size of the correlations. Restricting the analyses to include only those crashes for which the drivers were deemed responsible had a slightly negative effect on correlations. However, this was due to lower means (and thus variance) in these calculations. Similarly, controlling for hours worked decreased the correlations somewhat, but this was due to an outlier problem. The results are consistent with previous research and indicate that stability of accident involvement exists and that the effects can be reliably found under certain methodological circumstances. The sizes of coefficients are determined mainly by the restriction of variance, not by any underlying lack of stability. The stable tendency to cause mishaps within the same environment is a strong factor in traffic safety although this is not apparent when variance in the data is low

Longitudinal jerk and celeration as measures of safety in bus rapid transit drivers in Tehran

Traditionally, analysis of individual differences in road traffic crash risk has relied on after the fact crash data. Nowadays with the help of technologies like GPS, new measures are developed to assess driving risk, inferred from naturalistic driving behavior of drivers. In this study, two main ways of modelling driver behavior in naturalistic driving research were discussed and compared to each other. For this purpose, 176 Bus Rapid Transit (BRT) drivers were investigated during their normal driving on Tehran BRT routes. Their speed was continuously recorded by a smart phone app from which was derived deceleration, jerk, and celeration measures. Analysis showed that all of the proposed measures had positive correlations with culpable crashes and that one of the jerk variables yielded the highest correlation. Measurements of speed derivatives like jerk and celeration can help to identify dangerous driving styles in public transportation and reduce the number of crashes. Although the results of this study are encouraging, further studies for longer periods are needed to improve the reliability of the measures