The current study used an innovative method to obtain objective measures for driving violations at the individual level by using a self-application that monitored the participant’s driving patterns in situ. The objective measures were coupled with a subjective self-reported assessment of the frequency of violations. In addition, the study examined the relationship between attitudinal and demographic variables and the frequency of self-reported speeding, and explored the potential use of rewards to motivate behavioral change that will reduce such driving behavior. Finally, we explored the relationship between objective driving behavior and individual-level data on attitudes and demographic characteristics.
We found a significant correlation between self-reporting on the tendency to engage in speeding and the objective measures acquired using Get Location. This suggests that studies can rely on self-reported data to identify trends in driver behaviors, including violations. However, given the well-established tendency of drivers to overestimate their driving capabilities [1, 7, 20], data derived from such self-reporting should be used to evaluate driving trends but not the actual magnitude of these phenomena. At the same time the ease at which participants used the application suggests that existing technological platforms have already created self-monitoring services that are in wide use. Thus, research the same type of methodology could be used to better understand driving behavior at the individual level.
The results of the estimation model demonstrate that none of the demographic variables proved to be efficient in trying to distinguish between frequent and non-frequent speeders. This includes gender, which is in contrast to previous studies [14, 22, 33]. However, this finding should be viewed with caution given the low proportion of females in the sample (19%). We did not examine age due to homogeneity of the sample in that dimension. In contrast to previous studies [30], our results show a negative relationship between income and speeding. This result may derive from the fact that the participants were students, whose income distribution is different from those of other population groups.
Attitudinal and interpersonal variables proved efficient in distinguishing between frequent and infrequent speeders. Drivers who viewed speeding as less risky, were more likely to speed, [11, 25]. In addition, friends’ driving behavior, as perceived by the driver, was shown to have a significant and positive correlation with the risk of being a frequent speeder, and so did risk perception in general; in other words, people who believe that their friends tend to speed, and people who tend to take more risks in general, were more likely to become frequent speeders. We found that a higher level of self-control was correlated with speeding, perhaps demonstrating the tendency to overestimate one’s driving capabilities as discussed above. The relationship between attitudinal and interpersonal variables is of significance when viewing it from the complementary qualities of the objective and subjective measures and tools used. Self-monitoring application on personal devices use a similar model and in away normalize the corresponding behaviors of self-monitoring and self-reporting. The strong reliance on interpersonal considerations suggests the possible advantage of using data gathering techniques that already exist in society at large.
Finally, we found evidence that the use of rewards can influence driving behaviors, including speeding. This is consistent with several previous studies that exhibited their usefulness in reducing speeding tendencies [8, 19]. To stress the matter further, this approach follows the model for human behavior used by the deterrence paradigm [21, 23], but utilizes it in order to underlie the potential benefits of using rewards. The re-organization of insurance schemes can prove to be a fruitful way to achieve this goal, for example by reducing the premiums for safe drivers. Furthermore, this study presents a different model, one that relies on self-monitoring, self-reporting and the behavioral response to rewards and fines. These platforms already exist in various forms throughout the Hi-tech industries, suggesting that future studies could pursue collaboration with existing companies that develop and rely on technologies that gather big data on personal behaviors. This would consequently lend itself to incorporating reward/fine principles that rely on scientific research that could help people modify their driving behavior. The introduction of in-vehicle monitoring devices would also promote such goals. However, the former would circumvent the low social acceptability regarding mandatory monitoring technologies [16].
The study had three main limitations. First, the study did not compare the use of rewards and fines to influence behavior in practice, but only in theory. Future studies should address these issues by trying out different schemes that would improve the understanding of fines, rewards, and various possible combinations, on driving behavior.
Second, the study used a relatively small and homogenous sample. Women were significantly underrepresented in the sample. Ideally, future studies should use larger and more diverse samples, which would be followed for a longer period so the effect of experimenter’s desirability would be reduced. In addition, the participants could decide not to turn on the application before certain trips. However, it should be noted that Get Location recorded approximately 88% of the trips (when compared to the results of the online survey, representing an average week of driving), during which drivers committed a considerable number of violations, even regardless of being aware of the use of the monitoring application. Thus, the bias due to intentional misrepresentation of driving patterns would be minimal.
Third, the participants had to turn the application on whenever they began a trip. We included this feature to increase the participants’ willingness to use the application; however, this allowed participants to avoid recording all their trips. It is possible that participants intentionally chose to avoid using Get Location before particular trips in which the drivers planned to commit violations. However, given the high number of violations recorded overall, we believe that most unrecorded trips were due to forgetfulness.
The main strength of this study was the use of an in-vehicle recording system to obtain objective data on individual level driving behavior, including traffic violations. The study demonstrates the need to use objective measures in order to assess the magnitude of particular driving behaviors in general and traffic violations in particular. Furthermore, with a growing population that is technology savvy and familiar with similar methods, makes the application of this method particularly useful. In addition, the study’s results support future explorations of the use of rewards to modify speeding and driving behavior in general. With the advent of new methodologies for the study of big data, the advantage of cellular-application based methodologies is clear. The impact, however, on risky driving behavior has yet to have been comprehensively studied.
