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Abstract This study set out to investigate the proportion of UK drivers who engage in some form of distracting behaviour whilst driving. Data were collected by roadside observation in six urban centres in the South of England. The observations took place on randomly selected roads at three different time periods during two consecutive Tuesdays. The data revealed that 14.4% of the 7168 drivers observed were found to be engaged in a distracting activity. The most frequently observed distraction was talking to a passenger, followed by smoking and using a mobile phone. Younger drivers were significantly more likely to be distracted in general and by talking to passengers, while older drivers were less likely to be distracted by adjusting controls or using a mobile phone.

Introduction Driver distraction is now recognised as a contributing factor in at least a quarter of motor vehicle crashes (McEvoy et al., 2007, Stutts et al., 2001 and Wang et al., 1996). This proportion, however, could increase, in line with the increased use of portable technologies such as smart phones and satellite navigation systems. Therefore it is extremely important that we have a clear understanding of the types of distractions drivers currently engage in, their prevalence and the types of drivers who are more likely to be distracted. Driver distraction can be defined as any secondary activity that draws the driver’s attention away from the main task of driving (Ranney, 1994). Although the research investigating the impact of distractions on driving performance is well advanced, the research measuring exposure to driver distractions is still in its infancy (McEvoy & Stevenson, 2008). McEvoy and Stevenson (2008) identify four broad approaches to investigating this issue; cross sectional surveys, roadside observation, naturalistic observation and epidemiological approaches. Despite roadside observation being one of the four main methods for investigating drivers exposure to driver distraction, surprisingly few studies have investigated driver distraction using roadside observation and most of those that do exist have solely concentrated on the prevalence of one type of distracter, the mobile phone (e.g. Eby et al., 2006, Horberry et al., 2001, Knowles et al., 2008, McEvoy et al., 2007, National Highway Traffic Safety Administration, 2010 and Taylor et al., 2007). Nevertheless, there has also been some research which has observed more general distractions, but these have mostly been conducted amongst professional truck drivers (e.g. Hanwoski et al., 2007 and Hanwoski et al., 2005) or private vehicle drivers (Dingus et al., 2006) in order to look at critical incidents and the distractions which lead up to these critical incidents. There are currently only two peer reviewed studies which have looked at more general distraction amongst the general public. In one of the two peer reviewed studies to look more broadly at the issue of driver distraction in car drivers, Stutts et al. (2005) installed video cameras into 70 cars in America to observe what distracted car drivers. They found that the drivers in their study spent around 30% of the total time the vehicle was moving engaged in some form of distracting activity. The most common distraction was conversing with passengers (15.32%), followed by eating and drinking (4.61%), smoking (1.55%), and manipulating controls (1.35%). They also found that although using a mobile phone while driving was one of the many distracters, it was only observed 1.30% of the time. Unfortunately this research only included 70 drivers, meaning that it would be difficult to generalise these results to the general public and that it was not possible to reliably test for age or gender differences. This type of research is also very expensive, reliant on technology and the good will of volunteers. There is also a strong likelihood that some experimenter effect may have contaminated their data, as the drivers knew that they were being monitored and may have altered their behaviour to some degree. This issue was clearly acknowledged by Stutts et al. when they reported that almost 22% of the drivers in their study reported having the equipment in their car altered their driving. One way of reducing the experimenter effect is to conduct the observations from outside of the vehicle, unobtrusively. This was attempted by Johnson, Voas, Lacey, McKnight, and Lange (2004) who analysed 40,000 high quality digital photographs of drivers passing through a New Jersey turnpike. They found that less than 5% of the 40,000 photographs showed evidence of distraction. However, in contrast to Stutts et al. (2005) they found using a mobile phone was the most commonly observed distraction, with one third of those drivers judged to be distracted observed to be using a mobile phone. Smoking was the second most commonly observed distraction, with eating/drinking and interacting with a passenger accounting for most of the rest. As the research by Johnson et al. (2004) included large numbers of participants they were also able to test for age and gender differences. Surprisingly, they did not find any pattern for general distractions by age or gender. However, looking just at the use of mobile phones, Johnson et al. found that younger drivers (<45) were more likely to be observed using a handheld mobile phone than older drivers. Overall the top five distractions observed were reasonably similar for both studies, but the prevalence and orders were slightly different. These differences may have been for several reasons, such as the angle of view the observers had or the fact that Stutts et al. (2005) had much richer data upon which to base their decisions (e.g. movements, long periods to view their behaviour and the ability to review situations/behaviours). An observational method which would allow the collection of more rich data, than that collected by Johnson et al. (2004), would be through the use of roadside observation in a similar way to which handheld mobile phone use has been observed (e.g. Horberry et al., 2001). As there is very little peer reviewed research investigating the issue of driver distraction using roadside observation and none currently from the UK, the present study set out to investigate the proportion of UK drivers who engage in an observable in-car secondary task whilst driving. The research also investigated the relative frequencies of the secondary tasks or distractions and whether there were any age and gender differences. In order to provide information necessary for the development of interventions (e.g. education, enforcement) it is important to know the type of driver who is more likely to be distracted and the types of distracting behaviours that they are more likely to engage in.

3. Results A total of 7168 drivers were observed during the study. Table 1 shows the number and percentage of drivers observed engaged in each type of distraction, overall and by age, sex and time of day. The table also presents a conglomeration of separate chi-square contingency tables, which were combined into one table in the interests of parsimony. Where significant differences were found, the standardised residuals were examined to identify which cells were responsible for the difference (those larger than 1.96 indicate that the observed frequency was significantly different from that which would have been expected if there were no association between the variables in question). Of the 7168 drivers observed, 14.4% were seen to be involved in a distracting activity whilst driving. The most frequently observed distractions were talking to a passenger (7.4%), using a mobile phone (2.2%) and smoking (2.2%). Eating and/or drinking were observed in 1.1% of drivers, followed by adjusting controls (1.1%), with the “other” category containing the remaining 0.9%. Table 1. Type of distraction by sex, age and time of day. Mobile phone use Eating/drinking Smoking Talking to passenger Adjusting controls Other All distractions Not distracted Sex Male n (%) 92 (2.3) 40 (1.0) 91 (2.2) 302 (7.4) 41 (1.0) 34 (0.8) 585 (14.4) 3472 (85.6) Std. residual +0.5 −0.5 +0.1 0 −0.4 −0.4 0 0 Female n (%) 63 (2.0) 36 (1.2) 68 (2.2) 231 (7.4) 36 (1.2) 30 (1.0) 449 (14.4) 2661 (85.6) Std. residual −0.5 +0.5 −0.1 0 +0.4 +0.4 0 0 Age <30 n (%) 40 (2.7) 14 (0.9) 35 (2.4) 145 (9.8) 21 (1.4) 18 (1.2) 262 (17.7) 1221 (82.3) Std. Residual +1.4 −0.5 +0.4 +3.3 +1.3 +1.3 +3.3 −1.3 30–50 n (%) 94 (2.3) 46 (1.1) 87 (2.1) 265 (6.5) 49 (1.2) 30 (0.7) 555 (13.7) 3497 (86.3) Std. residual +0.7 +0.4 −0.3 −2.1 +0.8 −1.0 −1.2 +0.5 >50 n (%) 21 (1.3) 17 (1.0) 37 (2.3) 123 (7.5) 7 (0.4) 16 (1.0) 218 (13.3) 1415 (86.7) Std. residual −2.4 −0.1 +0.1 +0.1 −2.5 +0.4 −1.2 +0.5 Time 10–11 n (%) 44 (2.2) 28 (1.4) 47 (2.3) 177 (8.7) 28 (1.4) 14 (0.7) 326 (16.0) 1712 (84.0) Std. residual +0.0 +1.3 +0.3 +2.1 +1.3 −1.0 +1.8 −0.8 14–15 n (%) 50 (2.1) 21 (0.9) 54 (2.3) 168 (7.2) 23 (1.2) 30 (1.3) 334 (14.3) 2008 (85.7) Std. residual −0.1 −0.8 +0.3 −0.5 −0.4 +2.0 −0.2 +0.1 17–18 n (%) 61 (2.2) 28 (1.0) 58 (2.1) 188 (6.7) 26 (0.9) 20 (0.7) 375 (13.5) 2413 (86.5) Std. residual +1.0 −0.4 −0.5 −1.3 −0.7 −1.0 −1.4 +0.6 Total 155 (2.2) 77 (1.1) 159 (2.2) 533 (7.4) 77 (1.1) 64 (0.9) 1035 (14.4) 6133 (85.6) Table options 3.1. Driver distraction by gender As illustrated in Table 1, the rates for participation in a secondary activity were exactly the same for males and females (14.4%). In addition, the most frequently observed distraction for both genders was talking to a passenger. The second most commonly observed distraction among males was using a mobile phone (2.3%), while for females it was smoking (2.2%). However, overall there were very strong similarities between female and male drivers for each identified distracter and consequently no significant differences were observed. 3.2. Driver distraction by age group In all age groups most of the drivers were not engaged in a secondary activity (Table 1). There were, however, some apparent differences by age group. Engagement in secondary activities was found to be 17.7% for motorists under the age of 30, 13.7% for drivers aged 30–50 and 13.3% for drivers over the age of 50 years old. This shows a relatively clear decrease in distraction by age, which was statistically significant (χ2(2, 7167) = 15.88, p < 0.001). An examination of the standardised residuals shows that those younger than 30 years old engaged in more distractions than would have been expected if age were unrelated to distraction. Table 1 also shows that the most frequently observed distraction in all three age groups was talking to a passenger. The second most frequently observed distraction for the two younger age groups was using a mobile phone, while it was smoking for the oldest age group. Using a mobile phone was the third most commonly observed distraction for the oldest age group, while smoking was third for the two younger age groups. There were no significant differences by age for eating, smoking or “other”. However, there were apparent differences in the use of a mobile phone (χ2(2, 7167) = 8.41, p < 0.05). The standardised residuals indicate that those older than 50 years old were observed using a handheld mobile phone less often than expected. There was also a statistically significant difference, by age group, for talking to passengers (χ2(2, 7167) = 16.56, p < 0.001), with the youngest age group being observed talking to passengers relatively more often than expected and the middle age group (30–50 years old) being observed less often than expected. There was also less adjusting controls in the older age group than expected (χ2(2, 7167) = 8.73, p < 0.05). 3.3. Driver distraction by time of day The proportion of drivers who were driving only, compared with distracted drivers, during the three time periods measured are also presented in Table 1. There was a significant difference for the three time periods (χ2(2, 7167) = 6.26, p < 0.05), which appeared to be due to more distractions in the morning, but the standardised residual was just under 1.96 (+1.8). Table 1 also shows the type of driver distraction by time of day. There were no significant time of day differences in the use of a mobile phone, eating/drinking, smoking, adjusting controls or other. However, there was a significant difference for interacting with passengers (χ2(2, 7167) = 6.80, p < 0.05), which was due to more interacting with passengers in the morning (10–11 am) observation period.