ANALYSIS OF DRIVING HABITS
Analysis of the Relationship between Age and Driving Habits
Katniss Everdeen
York College of Pennsylvania
Abstract
In the interest of driving safety, 108 survey responses were analyzed to determine whether factors such as age, gender, color and type of car, type of roads, miles driven, passengers, transmission type, and self-assessed driving ability have any relationship to moving violations (tickets) and self-induced vehicular collisions (crashes). The samples proved too small to be decidedly conclusive, however familiar trends did exist in the data.
Review of Literature
Personal transportation is one of the most pervasive aspects of our modern culture. According to World Bank (2013), the average rate of car ownership in the United States for the year 2012 was 797 cars per 1,000 people. With such a tremendous amount of vehicles in private hands, policies targeting safety are of paramount importance. Accordingly, this study examines numerous factors that affect safe driving habits which include age and gender of the driver, type and color of car, the number of miles driven per week, the nature of roads most frequently traversed, and the number of passengers. With a more thorough understanding of driver demographics, it is likely that roads, laws, and insurance premiums will be more thoughtfully designed.
Age has been well documented as a function of safe driving habits. A study conducted by Constantinou, Panayiotou, Knostantinou, Loutsiou-Ladd, & Kapardis (2011) illustrates the risk factors affecting teenage drivers. Test subjects were found to exhibit fewer signs of poor driving as age increased, most likely the result of increased maturity, emotional balance, and experience on the roads. Comparatively, personality, a relatively stable, unchanging variable, had a minimal correlation with traffic offenses and collisions. Immaturity among teenage drivers proves especially dangerous when young passengers are present. Not only are the young drivers inclined to engage in distracting conversation, but the passengers are more likely to intentionally distract the driver (Heck & Carlos, 2008). In regards to older drivers, a study conducted by Fofanova & Vollrath (2012) discovered that distraction varies by age. Middle aged drivers are more likely to be distracted by external devices or vehicle controls, while older drivers are more likely to be distracted by passengers. This is most likely due to the older drivers’ aversion to unnecessary vehicle functions as well as their more talkative nature, as opposed to middle aged drivers who seem more confident and objective about their driving. Women drivers examined by Krahé (2005) had similar tendencies regarding age: older drivers had a negative correlation with aggressive driving, a propensity that can lead to trouble. Finally, self-reported driving ability by elderly drivers has been found to positively correspond to more miles driven and fairer health, but does not affect the number of accidents or citations as compared to seniors whose self-perception is more negative. Many states do not regulate the driving competency of seniors, despite the fact that those older than 75 years are at significantly higher risk of an accident (Ross, Dodson, Edwards, Ackerman, & Ball, 2012).
Gender is another important issue when assessing driving habits. According to Constantinou et al. (2011), teenage males were involved in more accidents and received more citations than teenage women. This is due to the fact that men were found to drive more aggressively and had fewer inhibitions about taking risks. One aspect of risk taking behavior that seems to be changing is driving under the influence. From 1998 to 2006, arrest rates for female DUI’s increased while decreasing slightly for males; that said, men were still much more likely to be arrested for impaired driving (Currie, 2009). The gender gap continues into late adulthood. Among elderly drivers queried, men were found to be more likely to report being involved in an accident or being pulled over; however, women received suggestions to limit driving at a greater rate (Ross et al., 2012).
In addition to the psychological effects of age and gender, it is worth noting ancillary factors that affect safe driving habits. The first and most obvious is the car. According to an experiment conducted by Guéguen, Jacob, Lourel, & Pascual (2012), red vehicles invoked the swiftest outbursts of frustration from impatient male and female drivers. While the color of one’s car may not necessarily affect the driver, it certainly has the potential to affect surrounding drivers which may include law enforcement officers. A similar study examined the relationship between color and traffic collisions. The results indicated that silver colored cars were least likely to be involved in a crash while black and brown colored cars were most prone to incident. The incidence of red car mishaps was reported to be about average of all colors examined (Furness, Connor, Robinson, Norton, Ameratunga, & Jackson, 2003). Practically speaking, these results are not surprising due to the decreased visibility of dark colors at night. Another interesting aspect of note is the difference between automatic and manual transmission among drivers. A simulator experiment featuring young, male drivers with ADHD concluded that the deliberate action of constantly controlling the clutch and shifting in a manual transmission car helped them to stay more alert than operating a comparable, albeit relatively monotonous, automatic transmission car (Cox, Punja, Powers, Merkel, Burket, Moore, Thorndike, & Kovatchev, 2006).
Finally, the time one spends driving a car is important to consider when assessing driving habits, as are the type of roads most often frequented. Not surprisingly, highway driving is progressively fatiguing due to the absence of external stimuli; in fact, test subjects were found to apply brakes an average of 0.31 seconds later than a mentally acute control group after only 90 minutes of simulated driving. Clearly, the loss of concentration would be extremely dangerous in a hazardous situation (Ting, Hwang, Doong, & Jeng, 2008). Those who primarily traverse city roads have the exact opposite problem. Far from the monotony of highways, city drivers are constantly bombarded with potential hazards such as pedestrians, bicycles, parked cars, traffic congestion, and lack of visibility (Kolman, 2006). In terms of the number of miles, evidence points to, not shockingly, a direct correlation between negative driving outcomes and miles driven. According to Krahé (2005), female drivers who accrued more miles often showed signs of aggressive driving. Additionally, teenager car owners studied by Williams, Leaf, Simons-Morton, & Hartos (2006) tended to drive their cars more often than equivalent teenagers who did not personally own their cars, leading to a greater chance of crashes or violations.
Since most car-related research has already been done, the main goal of this study is to challenge existing data; however, there were several specific aspects that did not seem well covered. These include driving behavior versus car type and driving behavior versus road types excluding highways. Thankfully, due to the comprehensive nature of the survey questions, a very complete picture of each respondent can be determined for assessing trends, including ones unknown to the literature.
Participants
Test subjects for this study were adult drivers of any age, race, gender, or location, and were recruited via Facebook and email. 11 responses were immediately eliminated due to incomplete information which brought the total number of participants to 108. For some calculations, however, outstanding responses were discarded for the sake of overall accuracy.
Instrument
The subjects completed a survey which posed questions regarding age, gender, type of car, color of car, weekly mileage, type of roads, passengers, transmission type, self-reported driving ability, moving violations, and accidents (see appendix for survey questions). The survey was hosted online by the website surveymonkey.com and was active from Tuesday, 7/30 to Tuesday, 8/6.
Procedure
Responses were assembled into a spreadsheet and sorted based on factor. Each response category under each factor was recorded for number of responses, average numbers of tickets and crashes, T scores for tickets and crashes relative to the first category, and P scores for tickets and crashes relative to the first category. These data are located in figures 1-9 under the ‘Results’ subtitle. In the case of figure 4, car color, respondents were prompted to type in the color of their car, not to choose from a list. As a result, many different specific colors were later categorized as a few broad colors. For example, champagne was considered brown and grey was considered silver. Figure 4 reports the six most common car colors among the respondents. A similar manual simplification of responses was necessary for figure 5, weekly miles.
Results
Discussion
Regrettably, with all but a few exceptions, T scores were too low and P scores were too high to be considered significant. This is primarily due to small categorical samples; however, trends corroborating secondary research did exist in the results. Age (figure 1) was somewhat anomalous with drivers aged 26-35 and 36-45 reporting more tickets (-0.1409 and -0.8494 respectively) than 17-25 year olds: a most unexpected result. Crashes did appear more accurate, with older drivers reporting fewer than the youngest drivers. Female drivers as a whole received fewer citations (1.2154), as was expected (figure 2). Unfortunately, a difference between genders in regards to crashes was slight and not beyond contestation (-0.0765). Compact and mid-size sedan drivers (figure 3) reported about the same tendency to receive tickets (0.0452) while drivers of sport utility vehicles and high performance coupes had fewer and slightly more tickets, respectively (1.2917 and -0.7698). Compact sedans reported the highest rate of collisions with high performance coupes following closely behind (0.1325). Of all the most numerous car colors reported by respondents (figure 4), black and brown were most at risk of receiving a moving violation (0.268). Black, alone, was also most at risk of causing a traffic accident. Red cars, most shockingly and unlike any other color, reported neither a single ticket nor crash. Transmission type (figure 8) did not significantly change the rate at which drivers receive tickets, but manual transmission cars were involved in notably more crashes (-1.2983). The results for accrued tickets as a function of mileage (figure 5) made perfect sense. As mileage increases, drivers tend to receive more tickets (-1.0901, -0.4715, and -0.6677); however, it cannot be safely concluded that crashes occur more often as a result of increased mileage. Those who primarily drive on highways (figure 6) were found to receive more tickets (-1.0892) and involve themselves in more accidents (-1.4441) than those who drive on city roads, although country roads did show a marked decrease in the rate of accidents over city roads (1.397). This is most likely due to the tediousness of constant, frequent highway driving. The danger posed by passengers remains inconclusive (figure 7). Although one passenger seemed to help the driver avoid tickets (0.335), the chances of an accident were increased as a result (-0.114). Conversely, multiple passengers negatively affected the risk of receiving tickets (-0.2473) while positively affecting the risk of crashing (2.1443). Unfortunately for this last category, the results were far from concrete. Finally, self-reported driving ability did appear to correspond to actual driving habits (figure 9). Those who reported their driving as 5 (good) or better showed a marked decrease in both tickets (1.403, 1.5719, and 2.9489) and crashes (1.8316, 1.2115, and 2.2087). In fact, all 25 who rated themselves as 7 (excellent) reported a total absence of tickets and crashes.
Conclusion
For such a universal subject, the sample sizes were disappointingly low. In order to accurately determine how these factors affect driving habits, this same study should be conducted again, but on the national level with tens of thousands of respondents. Those who completed the survey, as a whole, were very good drivers, evidenced by 88 participants (81%) reporting no tickets and no crashes. Unfortunately, due to the nature of a convenience sample, it is impossible to say whether this rate is universal or merely proprietary to the results of this study. That being said, the results do contribute to existing research by providing unprecedented insight on driving behavior versus road type and type of vehicle.
References
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