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Predicting road traffic accidents: the role of social deviance and violations.

Early studies of the psychological correlates of traffic accident risk focused on social deviance. Tilman & Hobbs (1949) carried out a study of taxicab drivers in Canada. They found that, compared with a control group of 100 drivers never involved in an accident, a group of drivers who had been involved in four or more crashes was approximately seven times more likely to have had contact with one or more social agencies. These included adult and juvenile courts, social services, credit bureaux and public health. They concluded that 'we drive as we live'. More recent dies have again begun to raise the issue of social deviance as a general factor related to a large number of aberrant behaviours, including accidents and crime.

Hirschi & Gottfredson (1993) proposed low self-control as the mechanism which triggers deviance. For the majority of the population self-control is maintained, even when there are no immediately apparent legal or social sanctions. Those low on self-control succumb to the immediate benefits of deviant behaviours. The self-control which militates against deviance comes from internalized sanctions which, Hirschi & Gottfredson claim, are much stronger than social and legal sanctions. It is therefore unnecessary to consider the availability and strength of social and legal sanctions in order to explain the conformity of most people.

The argument that a general factor of deviance/problem behaviour underlies a large number of problem behaviours was also advanced by Jessor & Jessor (1977). Problem behaviours identified by these authors included drug use, criminal behaviour, heavy alcohol use and dangerous driving. Hirschi & Gottfredson's (1993) self-control argument can be regarded as one which identifies the factor that is common to all of these behaviours (i.e. lack of self-control). One source of evidence for their theory is that a wide range of deviant acts is underpinned by the same demographic, social and psychological correlates.

Junger & Tremblay (1994) applied Hirschi & Gottfredson's theory to the prediction of road traffic accidents. Their study involved 731 teenage boys, for whom measures of behaviour, delinquency, parenting, family structure and accident involvement were recorded as part of a longitudinal study. A relationship between accidents and crime was identified. The likelihood of being involved in a road traffic accident was 43% for the boys with the lowest score on the delinquency scale measure and 67% for the most delinquent boys.

Junger & Tremblay went on to examine the factors that predict delinquency and accidents. As self-control theory predicts, a number of background variables were associated with accident involvement and delinquent behaviour. Social disadvantage, lack of parental supervision and inattentiveness were all related to both delinquency and accident involvement. There were other variables, however, that were related to only one of the dependent variables. For example, punishment, identification and communication were related only to delinquency, while stringency of home rules was only predictive of accidents.

There are two main limitations with this approach to the study of accidents. First, the relationship between delinquency and accidents is not adequately explained. Deviant acts are found to be related to accident involvement, but the direction of causation is not specified. While most delinquent behaviours are intentional, accidents are generally assumed to be unintentional, although the precursors to them may involve intent, e.g. fast driving. Knowing that a relationship exists between relatively high levels of parental supervision (for example) and accidents, without knowing whether the relationship is a causal one or which way the causality flows, would appear to offer limited scope for remediation. We need to be able to explain the relationship between deviant acts and accidents before such findings can be applied.

Furthermore, there is presently only weak support for the notion that lack of self-control is the explanatory factor. There is no direct evidence that the boys who were delinquent and involved in accidents studied by Junger & Tremblay showed a lack of self-control. Factors such as inattentiveness are assumed to reflect a lack of self-control, but the latter is not measured directly, thereby making the argument a circular one. All that can be concluded is that the idea of lack of self-control as the underlying factor is plausible.

Other researchers have approached the issue of accident liability in a more focused way, by attempting to identify the behavioural predictors of accidents and their underlying motivation. Parker, West, Stradling & Manstead (1995b) surveyed 1656 drivers and used the Manchester Driver Behaviour Questionnaire (Reason, Manstead, Stradling, Baxter & Campbell, 1990) to examine the relationship between driving behaviour and accident involvement. Reason et al. (1990) identified three types of aberrant driving behaviour: lapses, errors and violations. Parker, Reason, Manstead & Stradling (1995a) and (1995b) found that violations, i.e. behaviours that involve deliberate deviations from safe driving practice, correlate with both past (Parker et al., 1995a) and future accident rates (Parker et al., 1995b). By contrast, the self-reported tendency to make errors or to have lapses did not predict accident rates. Violations were found to be a statistically significant, positive predictor of accident involvement, even after the effects of exposure, age and gender had been partialled out. This result has obvious implications for road safety policy. If errors were predictive of accidents then the improvement of skills training and better driver assessment would be of paramount importance. The fact that violations, which are intentional behaviours, are linked with accident involvement suggests that campaigns focusing on changing attitudes by means of education and increased detection are important in changing driver behaviour.

The link between general social deviance as a trait and the commission of some driving violations was examined by West et al. (1993a). They found an association between mild social deviance and accident risk. Part of this association was accounted for by the fact that reported driving speed mediates the effects of social deviance. West et al. suggested a number of explanations for the observed association between social deviance and faster driving. They argued that the most likely explanation is that 'irrespective of attitudes to the law, social deviance may be caused by a stronger focus on immediate needs irrespective of possible future consequences for oneself or others' (p. 217).

However, faster driving did not completely account for the association between social deviance and accident risk in West et al.'s (1993a) study. This may be due to the fact that their fast driving measure was based on only three items. If this was the case, a multi-item measure of driving violations might better account for the variance shared between social deviance and number of accidents. In the study reported in the present paper we examined this possibility by including measures of accident involvement, social deviance and violations as indexed by the Manchester Driver Behaviour Questionnaire (DBQ). If the relationship between social deviance and accidents is fully mediated by driving violations, there would be consequences for remediation. Interventions would be focused specifically on the factors that promote violations on the road. On the other hand, demographic factors, such as family background, socio-economic status and educational level, which are correlates of social deviance, are not readily tackled by interventions concerning road safety. Such information may be useful, however, when deciding which groups should be targeted by a road safety campaign.

To summarize, the purpose of the present investigation was to examine the relationship between violations, social deviance and accidents. The prediction was that the domain-specific measure of violations provided by the DBQ would mediate the relationship between social deviance and accidents.


Self-report measures of mild social deviance, aberrant road behaviour (violations and errors) and accident involvement were obtained in the course of standardized interviews. Data were collected for a quota sample of 830 drivers living in the Reading and Manchester areas of the UK. This was achieved by in-home interviews carried out by trained market researchers, with approximately equal numbers of men and women in each of five age groups: 17 to 20, 21 to 25, 26 to 30, 31 to 35 and 36 to 40 years. The final sample consisted 417 women and 413 men, ranging in age from 17 to 40 years, with an average age of 29 years. The market researchers were required to conduct their interviews in specific areas known to be predominantly populated by members of social class bands A, B or C, D, E. In this way it was ensured that the sample was not restricted in terms of socio-economic groups.

The 10-item social motivation questionnaire developed by West et al. (1993) was included as the measure of mild social deviance (MSD). Participants were asked: 'How likely is it that you would do each of these things if you were completely certain of getting away with it'. Social deviance was scored on a three-point scale labelled 'not at all likely' (1), 'quite likely' (2) and 'very likely' (3). The (MSD) items, together with means and standard deviations, are shown in Table 1.


Self-reported driving violations were assessed by means of a shortened version of the DBQ (Reason et al., 1990), which also measures driving errors. Respondents were asked to report how frequently they found themselves engaging in each of 16 specified driving behaviours (eight violations, eight errors). The 16 items were scored on a six-point scale from 'never' (1) to 'nearly all the time' (6). Brief versions of the items, together with means and standard deviations, are shown in Table 2. Both the DBQ and MSD measures also gave participants the opportunity to score 'don't know' responses that were later coded as missing data. This led to a total of 87 missing data points.

Finally, accident involvement was assessed by asking participants to report how many accidents they had been involved in over the last three years when driving a car or van on public roads, and which resulted in personal injury or damage to the vehicle or other property. They were also asked to describe these accidents. Those respondents with fewer than three years' driving experience were asked to recall and describe the accidents (up to three), in which they had been involved since passing their driving test.


Principal component analyses, with oblimin rotation, of the 10-item MSD and 16-item DBQ were conducted to examine the internal structure of these scales. The MSD scale gave rise to one major dimension with an eigenvalue of 3.75. Although a second factor had an eigenvalue slightly greater than unity (1.01), this second dimension was not conceptually distinct. Therefore, the 10-item measure of MSD was treated as a single scale for the purposes of this research. Analysis of the DBQ scale revealed three factors with eigenvalues greater than 1, representing errors (eigenvalue = 4.74), highway code violations (eigenvalue = 1.50) and more inter-personally aggressive violations (eigenvalue = 1.01). For the purposes of the present study, the distinction between the two sorts of violations was disregarded. Reliability statistics were computed for the items comprising the violation, error and social deviance scales. Cronbach's alpha scores were .77, .74 and .80, respectively. Thus, each of the scales was deemed to have a satisfactory degree of internal coherence. Respondents' mean scores for each scale were computed by summing the ratings for the individual items and dividing by the number of items.
Table 2. Means and standard deviations of error and violation items

DBQ items                               Mean         SD

Error items
Fail to see pedestrians crossing        1.65       0.76
Manoeuvre without checking mirror       1.68       0.90
Try to pass vehicle turning right       1.44       0.67
Turning left, nearly hit cyclist        1.38       0.59
Misjudge speed of oncoming vehicle      1.78       0.80
Queuing, nearly hit car in front        1.75       0.81
Miss 'Give Way' signs                   1.31       0.56
Brake too quickly                       1.49       0.67

Violation items
Risky overtaking                        2.05       1.16
Close following                         1.75       1.00
Shoot lights                            1.63       0.86
Angry, give chase                       1.40       0.82
Disregard speed limits                  2.59       1.37
Drink and drive                         1.16       0.52
Aversion, indicate hostility            1.56       0.96
Unofficial races                        1.22       0.62

The mean scores and standard deviations for the self-reported behavioural measures included in the subsequent analyses are reported in Table 3. Neither violation score nor mileage showed normal distributions and therefore appropriate transformations were applied to reduce skewness and kurtosis (Tabachnik & Fidell, 1989). On average, the number of accidents which this sample of drivers reported having been involved in during the three-year reporting period was 0.21 (SD = 0.52). However, not all of the sample had actually been driving for three years. Therefore, for those drivers with between one and three years' experience a three-year accident rate variable was calculated on the basis of actual number of years' driving experience. Participants with less than one year of driving experience (N = 31) were not included in further analyses because this recalculation may have resulted in their appearing to have disproportionately high accident rates (e.g. if they had been driving for six months and had had one accident their three-year accident rate would have been calculated as 6). Zero-order correlations were calculated to assess the relationships among the variables. The resulting correlations are also shown in Table 3.


Accidents were significantly negatively correlated with age and sex, with younger drivers and male drivers being more likely to have been involved in an accident. Significant positive correlations were found between accidents, on the one hand, and social deviance, violations and mileage. Error scores were found not to be significantly correlated with accidents and therefore excluded from further analyses. There was a strong relationship between social deviance and violation scores, consistent with our predictions. Furthermore, age, sex and mileage were all correlated with violation score. The MSD scale includes two items relating to driving violations, namely parking on a double yellow line and driving down the hard shoulder of a motorway when all other lanes are jammed. An eight-item MSD scale was computed omitting these two items. A correlation of .55 (p [less than] .001) between the eight-item MSD and violation score, as against .57 (p [less than] .001) for the 10-item scale, indicates that the inclusion of the driving items made little difference to the relationship between the two scales. Thus, the original scale was retained.

Regression analyses were used to assess the relationship among violations, social deviance and accidents. In order to test whether social deviance was mediated by the violation score, the procedure suggested by Baron & Kenny (1986) was adopted. First, the proposed mediator (violation score) was regressed on the ostensible independent variable (social deviance score). Second, the outcome variable (accident rate) was regressed on social deviance. Third, accident rate was regressed on both social deviance and violation scores. To establish mediation the first two regressions must yield significant beta weights and the effect of the mediator on the dependent variable must be significant in the third regression. Finally, the effect of the independent variable on the dependent variable must be less in the third equation than in the second. If the independent variable has no effect when the mediator is controlled, then perfect mediation has been demonstrated.

The dependent variable approximated to a Poisson distribution - that is, relatively few responses of interest (accident rate over three years) are present amongst a very large number of possible responses (Statistics and Epidemiology Research Corporation, 1990). In the raw data, the majority of respondents (655) reported no accident in the three-year period, 118 participants reported one accident, 19 reported two accidents, and a further seven reported more than two accidents. Ordinary least squares regression is unsuitable when the dependent variable is distributed in such a way. Therefore, a Poisson distribution regression was computed for each analysis, using the individually calculated accident rate over three years as the dependent variable. Associations between the predictor variables and accident frequency are expressed as rate ratios, which indicate the change in accident rate for each unit increase in the predictor variable. A ratio of 1 indicates that no change has taken place; a rate ratio of .75 indicates a 25 per cent reduction in the rate; a rate ratio of 2 would indicate a doubling of the rate. An example should help to clarify the interpretation of a rate ratio. Table 4 shows a rate ratio of .93 (p [less than] .001) for age when this variable is entered alone. This indicates a 7 per cent reduction in accident rate for each year that age increases. When age is entered together with violation score, the rate ratio for age is .94 (p [less than] .001), indicating a 6 per cent reduction in accident rate for each year that age increases. Thus, the closer the rate ratio is to 1, the smaller the effect for that particular variable. Poisson regression analyses were computed using the Egret software package (Statistics and Epidemiology Research Corporation, 1990).
Table 4. The mediation of age, sex and mileage by violations in the
prediction of accident rate

Variables entered into regression    Rate ratio   p value

Mileage (000s)                         1.54                  .008
Mileage and                            1.34                  .08
violation score                        3.56      [less than] .001
Age (years)                            0.93      [less than] .001
Age and                                0.94      [less than] .001
violation score                        2.81      [less than] .001
Sex (male = 1, female = 2)             0.65                  .004
Sex and                                0.76                  .09
violation score                        3.60      [less than] .001

The results of the three regressions described earlier were as follows. First, it was established that social deviance was a strong predictor of violation score (beta = .57, t = 19.17, p [less than] .001). Second, it was found that social deviance was a significant predictor of accident rate (rate ratio = 2.21, p [less than] .001). Third, it was established that violation score was a significant predictor of accident rate (rate ratio = 2.56, p [less than] .05) and that social deviance remained a significant predictor (rate ratio = 1.60, p [less than] .05) even when violations were statistically controlled. These results indicate that violation score mediates the effect of mild social deviance to some extent, but that social deviance also has an effect independent of driving violations.

Previous research (Reason et al., 1990) found that both age and gender were related to self-reported violations, such that men reported more violations than women and young drivers reported violating more than older drivers. In addition, people who drive more have more opportunity to violate. Accordingly the potential for violations to act as a mediator of the effects of age, sex and mileage on accident involvement was also assessed. Mileage and sex were not significantly predictive of accidents when violations were entered into the regression on the same step. However, the predictive value of age diminished only slightly when entered together with violation score. The rate ratios and significance levels of these three variables (age, mileage and sex) entered alone and together with violation score are shown in Table 4.

The results of the analyses reported thus far are consistent with a model of factors influencing accident rate in which age, violation score and mild social deviance directly influence accident rate, while the effects of sex and mileage are mediated by violation score. The order of entry of variables in a further Poisson regression was determined by this model. Age was entered on the first step. Violation score and mild social deviance were entered on the second step in stepwise fashion. Neither sex nor mileage was entered as these variables were mediated by violation score. The results of this regression are reported in Table 5. There it can be seen that only age and violation score were significant predictors of accident rate. Younger respondents and those with higher violation scores are more likely to have higher accident rates.
Table 5. Prediction of accident rate from age, violation score and
social deviance score

Variable                 Rate ratio     p value
Step 1: Age                 0.94        [less than] .001
Step 2: Violations          2.81        [less than] .001
and social deviance                     n.s.

Note. Rate ratios are not calculated for non-significant variables
entered stepwise.

The extent to which individual differences in violation scores could be predicted by social deviance, age, sex and mileage was examined by means of ordinary least squares analyses. Using the Baron & Kenny (1986) method described above, it was possible to examine the extent to which social deviance mediated the effect of age, sex and mileage in predicting violation score. The results of these ordinary least squares regressions are reported in Table 6. Social deviance did not fully mediate the effect of age, sex or mileage in predicting violation score.

As social deviance did not fully mediate the effect of any of the demographic variables, it was entered alongside them in the prediction of violation score. The results of this analysis are reported in Table 7, where it can be seen that all the predictors were highly significant. Younger respondents and males were more likely to report higher violations. Respondents with a higher annual mileage were also more likely to report violations. Finally, respondents with higher social deviance scores were especially likely to score highly on violations. Together these variables accounted for 38 per cent of variance in violation score.
Table 6. The mediation of age, sex and mileage by social deviance
in the prediction of violation score

Variables entered into regression     Beta     p value
Mileage                                .20     [less than] .001
Mileage and                            .17     [less than] .001
social deviance score                  .56     [less than] .001
Age                                   -.21     [less than] .001
Age and                               -.09                 .003
social deviance score                  .55     [less than] .001
Sex                                   -.26     [less than] .001
Sex and                               -.18     [less than] .001
social deviance score                  .54     [less than] .001
Table 7. Regression of violation score on social deviance, age,
sex and mileage

                     Beta     p value

Social deviance      .53     [less than] .001
Mileage              .15     [less than] .001
Age                 -.11     [less than] .001
Sex                 -.13     [less than] .001


Following West et al. (1993a) a model was constructed to describe the relationships between the various predictor variables and accident rate. Reported violations significantly predicted accidents and this relationship persisted even when the effects of social deviance and age were controlled for. Neither gender nor mileage was found to be independently predictive of accidents when the effects of violation score were partialled out of the regression. This suggests that both variables have only an indirect effect on accident rate. According to this model men violate to a greater extent than women and as a result have more accidents. High violators are also more likely to be those drivers who have a high annual mileage. Given the significant negative correlation between mileage and sex, it is plausible that males commit more violations than females (and therefore have more accidents) in part because they drive more, and thus have more opportunity to do so.

Age is a substantial and significant independent predictor of violation score, even though partially mediated by social deviance. Age is also directly predictive of accident rate, even after the effects of violation score have been partialled out of the regression. Therefore, being young is associated with an increased accident rate both directly and indirectly, via the increased tendency to commit violations. This suggests that there is something about being young that increases the likelihood of accidents that is not simply acting through an increased propensity to violate. This direct relationship may be related to the skill component of driving (Lester, 1991) or to the ability to assess the hazards on the road (McKenna & Crick, 1990), both of which are known to increase with age. The significant positive correlation between age and mileage indicates that the older drivers in this sample of 17-40-year-olds have fewer accidents despite a higher average annual mileage. Therefore, it cannot be argued that accidents are simply the result of greater exposure to danger.

Accident rates were not related to social deviance, once the effects of age and violations had been taken into consideration. However, social deviance was associated with accident rate when entered together with violation score without partialling out the influence of age. It can therefore be inferred that mild social deviance has an effect on driving behaviour independent of violations. However, the prior entry of age into the regression appears to cause the residual relationship between social deviance and accidents to disappear. This would suggest that factors associated with age yet unspecified in the present study are mediating this relationship. It is possible that the relationship between social deviance and accidents is associated with risk-taking or thrill-seeking, both of which have been found to be associated with youth (Evans, 1991; Meadows, 1994). Furthermore, the significant relationship between social deviance and violations suggests that violating behaviour on the roads may be one way in which social deviance manifests itself, such that to some extent the way we drive does indeed reflect the way we live. Of course, the fact that individuals who reported that they would behave in a relatively socially deviant way if the threat of negative consequences was removed also reported committing driving violations more frequently does not imply that all violators are socially deviant.

Before discussing the implications of these results for road safety policy, it is necessary to acknowledge some of the methodological limitations of the present study. The first point relates to the MSD scale. Respondents are asked to rate how likely it is that they would commit each of 10 behaviours if they were completely certain of getting away with it. It could be argued that people who score highly on an item do so, not because they are more deviant, but simply because they engage in the behaviour, e.g. riding on public transport, more frequently. However, the way the items are phrased means that the task is one in which the respondent asks him/herself: 'If I were to ride on public transport, how likely is it that I would do so without paying?' Another criticism that could be levelled at both the MSD scale and the violation scale concerns the fact that both might be measures of social desirability, and that this shared variance with social desirability accounts for the high correlation between social deviance and violations. The impact of social desirability on response to the MSD and DBQ measures is unknown. It may be that some respondents would provide exaggerated estimates of the frequency with which they commit 'deviant' acts or violations for reasons of social desirability, because they take pride in being deviant. These respondents are at the same time unlikely to take much pride in reporting involvement in accidents. On the other hand, some respondents may provide underestimates of the frequency with which they perform such acts again for reasons of social desirability. In the absence of further data it is impossible to settle this issue, but it should be noted that whatever the impact of social desirability on responses to the MSD and DBQ items, it could not account for the findings of this study that DBQ violations partially mediate the relationship between MSD and accident rate. Furthermore, respondents were asked not only to enumerate, but also to describe the accidents in which they had been involved during the last three years. It is possible that this procedure has the effect of enhancing the accuracy of self-reported accidents, thereby weakening the impact of social desirability bias on the relationship between accidents and the MSD and DBQ measures.

This leads us to a consideration of the more general limitations of self-report measures of driver behaviour and accident involvement. There is evidence that such self-reports are reasonably valid reflections of actual behaviour. For example, West, French, Kemp & Elander (1993b) reported a correlation of .65 between observed driving speed and responses on the driving speed subscale of their Driving Style Questionnaire. With regard to the self-report of accident data, there is an additional possible concern about the accuracy of the memory of participants when asked to recall accidents over a three-year period. Maycock, Lockwood & Lester (1991) reported that there is indeed a memory loss effect of 28 per cent of accidents each year. However, they found that age had no relationship with memory loss for road traffic accidents. Thus memory loss effects cannot account for the observed association between age and accidents. As West (in press) has argued, interpretation of self-report data only becomes problematic when there is reason to suggest that any bias or error could plausibly account for the reported findings. In fact, the underreporting of accidents that is likely to arise from self-presentation bias means that the relationships found in the present research between self-reported violations, mild social deviance and accidents are more likely to be underestimates than overestimates of real associations.

In trying to understand the observed relationship between mild social deviance and driving violations, it is worth considering carefully the behaviours included in the two scales measuring these constructs. First, all the items included in both the social deviance and violation scales represent behaviours that can have negative consequences for society as a whole. While these deviant acts are performed by a very small minority of the population, there is only a negligible effect on society. However, if a large number of people consistently committed such acts the cumulative effect would be huge. For the individual these same behaviours are likely to be associated with immediate benefits, especially if the wrongdoing goes undetected. For example, making or saving money, getting to your destination more quickly and having time off work are all immediate benefits to the individual. Second, the short-term benefits associated with all the behaviours are predictable, whereas the potential costs are less predictable. Finally, all the behaviours involve noncompliance with the rules, laws or norms of society.

Tendencies to behave in a manner that brings short-term gains to the individual at the expense of possible longer-term costs to society, and failure to comply with rules of society may in the individual be driven by a common psychological factor. Hirschi & Gottfredson (1994) suggest that lack of self-control may be such an underlying factor. According to their argument self-control is the barrier that stands between the actor and the immediate benefits that crime provides. However, their argument has mainly been applied to more seriously deviant behaviours such as delinquency and crime. The majority of behaviours included in the social deviance and violation scales are much less serious, albeit still socially undesirable. A large proportion of the population will, at some stage in their lives, speed on the road, find a [pounds]20 pound note and not hand it in, or travel on public transport without paying the fare, etc. An alternative explanation draws on Strathman, Gleicher, Boninger & Scott Edwards' (1994) construct 'consideration of future consequences', i.e. the extent to which people consider distal versus proximal consequences. This concept was found by Strathman et al. to predict the extent to which an individual reported carrying out behaviours relating to concern with the environment and with health. This concept has a wider focus than self-control and the authors suggest that 'individuals might experience events that influence the extent to which they consider future consequences' (p. 750). In these terms, the evidence that driving violations decrease with age would be the result of an increase in the consideration of future consequences, presumably as a result of such learning experiences. A possible direction for future research, then, is to try to identify the nature of the events that enhance consideration of future consequences.

The most immediate characteristic that the behaviours considered in the present study have in common is non-compliance. Although it cannot be demonstrated that those people who do comply with the law have internalized the appropriate attitudes, beliefs and values, they are seen outwardly to be complying with the rules of society. This may be because they fear the consequences of not following the rules, because they respect authority or because they hold moral beliefs that this is the correct way to behave. It is obviously an important task for future research to identify the factors that increase compliance. Such knowledge would clearly benefit remediation strategies, with respect to both deviant driving in particular and social deviance in general. Even in the absence of this knowledge it is possible to make some suggestions regarding intervention strategies. As we have seen, it is often the case that for the individual the immediate benefits of non-compliance with rules greatly outweigh the potential costs. If the costs to the individual of speeding or driving through a red light were made more explicit, this should reduce violations and thereby accidents. Improved enforcement via enhanced rates of detection (and therefore fines and charges), either by means of camera surveillance or greater police presence would increase the perceived potential costs to the individual of such behaviour.

Rothengatter, de Bruin & Rooijers (1989) tested various types of publicity and enforcement measures to bring about changes in drivers' speed choice on 80 km roads in The Netherlands. The results of these interventions showed that increasing surveillance (patrolling police vehicles) without increasing the objective risk of apprehension (costs) had no effect on speed choice, even though subjective probability of detection was increased. This is consistent with earlier findings showing that, in order to increase compliance, police enforcement must increase the objective probability of detection (Shinar & McKnight, 1985). When surveillance was obtrusive and radar was used, a marked reduction in the percentage of drivers speeding was achieved by increasing the objective level of risk of apprehension. This suggests that in order to achieve optimal behaviour change police enforcement strategies need to increase both the objective and subjective probability of detection. Furthermore, neither police enforcement strategy was found to change the attitudes, beliefs or evaluations relating to driving fast, suggesting that police enforcement can influence behaviour without affecting attitudes. Road traffic accidents are rare from an individual point of view and therefore the costs associated with violating on the road are often perceived to be small. By increasing the likelihood of detection and thereby increasing the costs of violating on the road, it should be possible to encourage road users to treat the traffic regulations with greater respect.

The findings of this study also have implications for the design of road safety campaigns. To reduce the number of accidents on the road, the number of violations that are committed also need to be reduced. To reduce violations, social deviance and mileage need to be reduced, which may not be a practical proposition. More amenable to change are the factors that mediate the influence of age and sex on violation scores. From other research (e.g. Parker, Manstead, Stradling, Reason & Baxter, 1992) we know that these factors include attitudes, beliefs and values. In addition, the findings of this study suggest that targeting specific high risk groups of drivers may impact on accident rates. Young male drivers, particularly those with a high annual mileage, are the highest risk group identified in this study. Designing interventions that focus on the attitudes, beliefs and values of this group of drivers in particular would, it appears, be a manageable and effective remediation strategy.

The emphasis of most safety campaigns is accident avoidance, but the 'it won't happen to me' mentality (cf. McKenna, 1993) can hinder the impact of such campaigns. Moreover, given the rarity of accidents, particularly those involving serious injury, this is in fact a rational response for most individuals. Although compliance with traffic laws may not be associated with benefits at an individual level, the benefits for society would be enormous, not only with respect to monetary savings, but also in terms of improving the problem of road congestion. Campaigns that promote this 'common good' philosophy may have an effect by making the benefits of traffic law compliance more salient. Together with police enforcement campaigns designed to increase the costs of violating behaviour, safety campaigns of this sort should help to maintain traffic law compliance.


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Author:Lawton, Rebecca; Parker, Dianne; Stradling, Stephen G.; Manstead, Anthony S.R.
Publication:British Journal of Psychology
Date:May 1, 1997
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