Running head: ANALYSIS OF DRIVING HABITS
Analysis of the Relationship between Age and Driving Habits
Katniss Everdeen
York College of Pennsylvania
1
ANALYSIS OF DRIVING HABITS
2
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,
ANALYSIS OF DRIVING HABITS
3
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 selfperception 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
ANALYSIS OF DRIVING HABITS
4
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
ANALYSIS OF DRIVING HABITS
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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, SimonsMorton, & 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.
ANALYSIS OF DRIVING HABITS
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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
Figure 1: Age
17-25
26-35
36-45
46-55
56-65
N
28
10
16
36
18
Mean Tickets
P Score Tickets T Score Tickets Mean Crashes P Score Crashes T Score Crashes
0.2678
0.05
0.1429
0.05
0.3
0.8898
-0.1409
0.1
0.747
0.3265
0.5
0.4052
-0.8494
0.0625
0.9003
0.1273
0.2083
0.6749
0.4217
0.0556
0.3546
0.937
0.1666
0.5281
0.6364
0
0.1037
1.687
ANALYSIS OF DRIVING HABITS
Figure 2: Gender
Male
Female
N
Figure 3: Car Type
Compact Sedan
Mid Size/Large Sedan
Sport Utility Vehicle
High Performance Coupe
N
Figure 4: Car Color
Black
Blue
Brown
Red
Silver
White
N
Figure 5: Weekly Miles
1-100
101-200
201-300
>300
N
Figure 6: Road Type
City
Country
Highway
N
Figure 7: Passengers
0
1
>1
N
7
42
66
Mean Tickets
P Score Tickets T Score Tickets Mean Crashes P Score Crashes T Score Crashes
0.369
0.05
0.0714
0.05
0.2045
0.2286
1.2154
0.0758
0.9392
-0.0765
31
37
15
9
Mean Tickets
P Score Tickets T Score Tickets Mean Crashes P Score Crashes T Score Crashes
0.2903
0.05
0.129
0.05
0.2838
0.9641
0.0452
0.0541
0.3853
0.8769
0.1
0.2038
1.2917
0.0667
0.5428
0.6137
0.5
0.4574
-0.7698
0.1111
0.8962
0.1325
12
29
10
10
28
10
Mean Tickets
P Score Tickets T Score Tickets Mean Crashes P Score Crashes T Score Crashes
0.375
0.05
0.1666
0.05
0.25862
0.6084
0.5215
0.13793
0.8383
0.2064
0.3
0.7915
0.268
0
0.1685
1.4739
0
0.0826
1.9097
0
0.1685
1.4739
0.2678
0.6392
0.4767
0.07143
0.4499
0.7753
0.15
0.3728
0.9121
0
0.1685
1.4739
53
25
16
14
Mean Tickets
P Score Tickets T Score Tickets Mean Crashes P Score Crashes T Score Crashes
0.19811
0.05
0.07547
0.05
0.36
0.2825
-1.0901
0.12
0.5817
-0.5547
0.3125
0.643
-0.4715
0
0.1078
1.6364
0.32143
0.5129
-0.6677
0.0714
0.9627
0.0472
45
24
39
Mean Tickets
P Score Tickets T Score Tickets Mean Crashes P Score Crashes T Score Crashes
0.2333
0.05
0.04444
0.05
0.1875
0.73
0.3471
0
0.1694
1.397
0.35897
0.2793
-1.0892
0.15385
0.1546
-1.4441
76
23
9
Mean Tickets
P Score Tickets T Score Tickets Mean Crashes P Score Crashes T Score Crashes
0.27632
0.05
0.07895
0.05
0.21739
0.74
0.335
0.08696
0.9099
-0.114
0.3333
0.8099
-0.2473
0
0.0353
2.1443
Figure 8: Transmission Type N
Automatic
Manual
88
20
Mean Tickets
P Score Tickets T Score Tickets Mean Crashes P Score Crashes T Score Crashes
0.27841
0.05
0.0454
0.05
0.225
0.7076
0.3783
0.2
0.2087
-1.2983
Figure 9: Self-Assessment
Average (4)
Good (5)
Very Good (6)
Excellent (7)
17
31
35
25
Mean Tickets
P Score Tickets T Score Tickets Mean Crashes P Score Crashes T Score Crashes
0.52941
0.05
0.23529
0.05
0.24194
0.1723
1.403
0.03226
0.0827
1.8316
0.21429
0.1289
1.5719
0.08571
0.2359
1.2115
0
0.0094
2.9489
0
0.0421
2.2087
N
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
ANALYSIS OF DRIVING HABITS
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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
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ANALYSIS OF DRIVING HABITS
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
Guéguen, N., Jacob, C., Lourel, M., & Pascual, A. (2012). When drivers see red: car color
frustrators and drivers' aggressiveness. Aggressive Behavior, 38, 166-169. Retrieved from
ebscohost.com
Fofanova, J. & Vollrath, M. (2012). Distraction in older drivers – a face to face interview study.
Safety Science, 50(3), 502-509. Retrieved from sciencedirect.com
ANALYSIS OF DRIVING HABITS
10
Furness, S., Connor, J., Robinson, E., Norton, R., Ameratunga, S., & Jackson, R. (2003). Car
colour and risk of car crash injury: Population based case control study. BMJ, 327, 14551456. Retrieved from www.ncbi.nlm.nih.gov
Krahé, B. (2005). Predictors of women's aggressive driving behavior. Aggressive Behavior,
31(6), 537-546. Retrieved from ebscohost.com
Constantinou, E., Panayiotou, G., Konstantinou, N., Loutsiou-Ladd, A., & Kapardis, A. (2011).
Risky and aggressive driving in young adults: Personality matters. Accident Analysis and
Prevention, 43(4), 1323-1331. Retrieved from sciencedirect.com
Ting, P. H., Hwang, J. R., Doong, J. L., & Jeng, M. C. (2008). Driver fatigue and highway
driving: A simulator study. Psychology & Behavior, 94(3), 448-453. Retrieved from
sciencedirect.com
Ross, L. A., Dodson, J. E., Edwards, J. D., Ackerman, M. L., & Ball, K. (2012). Self-rated
driving and driving safety in older adults. Accident Analysis and Prevention, 48, 523-527.
Retrieved from sciencedirect.com
Williams, A., Leaf, W., Simons-Morton, B., & Hartos, J. (2006). Vehicles driven by teenagers in
their first year of licensure. Traffic Injury Prevention, 7(1), 23-30. Retrieved from
ebscohost.com
Cox, D. J., Punja, M., Powers, K., Merkel, R. L., Burket, R., Moore, M., Thorndike, F., &
Kovatchev, B. (2006). Manual transmission enhances attention and driving performance
of ADHD adolescent males: Pilot study. Journal of Attention Disorders, 10(2), 212-216.
Retrieved from online.sagepub.com
10 Heck, K. E., & Carlos, R. M. (2008). Passenger distractions among adolescent drivers.
Journal of Safety Research, 39(4), 437-443. Retrieved from sciencedirect.com
ANALYSIS OF DRIVING HABITS
11
Currie, D. (2009). Nation in brief: More women driving while impaired. Nation's Health, 39(9),
7. Retrieved from ebscohost.com
Kolman, D. (2006). Safe city driving practices. Beverage Industry, 97(9), 70. Retrieved from
ebscohost.com
Motor vehicles (per 1,000 people). (2013). The World Bank. Retrieved from data.worldbank.org
ANALYSIS OF DRIVING HABITS
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Appendix
*Disclaimer: Information submitted via this survey is completely anonymous. Survey results will
be used for statistical purposes only. Participation is strictly voluntary.*








What is your age in years?
o 17-25
o 26-35
o 36-45
o 46-55
o 56-65
o 66-75
o 76-85
o >85
What is your gender?
o Male
o Female
Which of the following best describes your car? If you own multiple cars, please
categorize the one you drive most often.
o Hybrid or electric coupe/sedan
o Compact coupe/sedan
o Mid-size or large coupe/sedan/wagon
o Sport utility vehicle
o Mini-van or van
o Pick-up truck
o High performance coupe/sedan
What is the color of your car?
o [Fill in blank]
Approximately how many miles do you drive per week?
o [Fill in blank]
Are these miles primarily driven on city roads, country roads, or highways?
o City roads
o Country roads
o Highways
How many additional passengers ride in your car for the majority of these miles?
o 0
o 1
o 2
o 3
o 4
o >4
What is your nearest large town or city?
o [Fill in blank]
ANALYSIS OF DRIVING HABITS





On a scale of 1 to 7, how do you rate yourself as a safe and courteous driver?
o 7 (Safe, courteous)
o 6
o 5
o 4 (Average)
o 3
o 2
o 1 (Unsafe, easily provoked)
Who is your car insurance provider?
o Erie
o Farmer’s
o State Farm
o Nationwide
o Geico
o SafeAuto
o Liberty Mutual
o AARP
o Other [Fill in blank]
Does your vehicle have manual or automatic transmission?
o Manual
o Automatic
How many moving violations have you been cited for over the past three years?
o 0
o 1-2
o 3-4
o 5-6
o 7-8
o >8
How many accidents have you been involved in over the past three years where your
vehicle needed repair or replacement and you were found to be at fault?
o 0
o 1-2
o 3-4
o 5-6
o >6
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