Does accurate self-awareness increase traffic safety behaviour

Does
accurate
self-‐awareness
increase

traffic
safety
behaviour?

Author:
Erik
Sommarström

Supervisor:
Jan
Andersson

Linköpings Universitet 729A64
Erik Sommarström

Erik Sommarström
Abstract

This simulator study aims to examine how self-awareness affects traffic safety
behaviour. Self-awareness (SA) is, in this study, the ability to accurately know one’s
strengths and weaknesses. This was tested using 27 participants in the ages between
65 and 75. SA is measured using the DSI-questionnaire (Warner et al., 2013) and
comparing specific DSI-items to a suitable counterpart in the simulator. The
difference between the self-assessment and the actual performance is then calculated
to become a measure for SA. This measure of SA could then be compared to six
different calculated variables for traffic safety behaviour, which also were taken from
actual performance in the simulator scenario. The main test used a multivariate
ANOVA to test for differences between SA and traffic safety. No difference in SA
could be found between over and under-estimators of SA and participants with good
SA. Given this result it is hypothesized that self-awareness might only affect learning
how to drive and the null-results could therefor be a result of the chosen test group,
which were experienced older drivers.

Erik Sommarström
1 Table
of
Contents

2
Does
accurate
self-‐awareness
increase
traffic
safety
behaviour
...........................
1

2.1
Self-‐awareness
............................................................................................................
1

2.2
Self-‐awareness
and
traffic
safety
behaviour
...............................................................
2

2.3
Operationalization
......................................................................................................
4

3
Method
...................................................................................................................
6

3.1
Participants
.................................................................................................................
6

3.2
Questionnaires
............................................................................................................
6

3.3
Simulator
.....................................................................................................................
7

3.4
Procedure
...................................................................................................................
7

3.4.1
Scenario
1
.............................................................................................................
7

3.4.2
Scenario
2
.............................................................................................................
8

3.5
Analysis
.......................................................................................................................
8

3.5.1
Design
..................................................................................................................
8

3.5.2
Measures
.............................................................................................................
9

3.5.3
Simulator
measures
.............................................................................................
9

3.5.4
Self-‐awareness
and
traffic
safety
behaviour
measures
.....................................
10

3.5.5
Statistical
tests
...................................................................................................
12

4
Results
..................................................................................................................
13

5
Discussion
.............................................................................................................
15

5.1
Result
discussion
.......................................................................................................
15

5.2
Methodological
discussion
........................................................................................
16

5.3
Concluding
remarks
..................................................................................................
18

6
References
............................................................................................................
19

Erik Sommarström
1
2 Does
accurate
self-‐awareness
increase
traffic
safety

behaviour

Every time you get in your car you take a risk of ending up in an accident where you
are involved. But is there a difference between drivers that end up in accidents and
drivers that do not? The importance of a driver having a good understanding of traffic
safety behaviour and its cofounding factors is an important matter for both the
driver’s safe usage of the car but also for the other drivers in the traffic context.
Traffic safety behaviour is dependent on several factors both external (e.g. weather or
technical problems with the car) and internal (e.g. reaction time or alertness) but also
on how well the driver can plan and cooperate with other drivers (Trafikverket.se,
2014). This paper will however focus on metacognition; knowledge about ones own
knowledge and what implications it might have on driving ability (cf. Brown, 1978).
But more specifically on self-awareness and how it affects traffic safety behaviour.
What is meant by self-awareness in this article is the ability to know ones own
weaknesses and limitations and act accordingly (Bandura & Cervone, 1983;
Lundqvist & Alinder, 2007). Metacognitive skills have been shown to be very
important for reaching expert level in a skill (Kolb, 1984; Mezirow, 1990). And thus
it should be equally important for reaching a safe driving skill level; not only in driver
education but also in the continuous experience the driver receives whilst driving
(Hattaka et al., 2002). This study aims to see if there are relations between self-
awareness and traffic safety behaviour. Traffic safety behaviour, in this paper, refers
to avoiding accidents and dangerous situations and having good marginal for avoiding
them.
2.1 Self-‐awareness

There are several studies that show that drivers often over-estimate their driving
ability, when asked to compare themselves to average drivers (Amado et al., 2014;
Groeger & Grande, 1996; Stapleton, Connolly & O’neil, 2012; Svenson, 1981). This
suggests that many drivers drive beyond their actual ability. However, since the term
“average driver” might be seen as negative this might affect the drivers’ rating of
themselves, these results might therefor be unreliable (Groeger & Grande, 1996).

Erik Sommarström
2
Studies made where drivers have been rated by an expert instructor whilst driving and
after having to rate themselves the comparison between the two ratings have shown
that drivers who over estimate their driving skills are more likely to fail a driving test
(Lundqvist & Alinder, 2007; Mallon, 2006). To test self-awareness in this present
study the Driver Skill Inventory (DSI) questionnaire will be used. The DSI consists of
eleven items targeting perceptual motor skills and nine items targeting safety skills in
traffic (Warner et al., 2013). This self-rating will then be compared to events in a
simulator scenario corresponding to items in the DSI. This comparison will assess a
measure of self-awareness. The importance of self-awareness for traffic safety
behaviour is also argued in the Goal Driver Education matrix (GDE-matrix, Hattaka
et al., 2002). The GDE-matrix points out that there are three important factors, or
goals, that a safe driver has to have. These goals are “knowledge and skill” (e.g.
motoric and cognitive capabilities necessary for driving, knowledge about traffic
legislation), “Risk-increasing factors” (e.g. Knowledge about potential risks in traffic)
and “self-evaluation” (e.g. learning from mistakes) Hattaka et al., 2002; (Peräaho,
Keskinen & Hatakka, 2003). Self-awareness in this article refers to how accurate ones
self-evaluation is.
2.2 Self-‐awareness
and
traffic
safety
behaviour

A driver with great “Knowledge and skill” is necessarily not a better or safer driver by
default. Adequate cognitive and motoric functions are not enough to be a safe driver
since this could lead to the driver increasing task difficulty (Hattaka et al, 2002;
Evans, 1991; Näätänen & Sumala, 1974). With higher technical skill it is more likely
that the driver would take more chances of, for example, overtaking in heavy traffic
or/and focusing on more secondary tasks, which would lead to more risk for the
driver, instead of less risk (Evans, 1991). This would also be in line with the risk
homeostasis theory, which states that every person has a risk target level that they try
to level with (Hoyes, Stanton & Taylor, 1996). This would however still affect a
driver with good self-awareness; this is only to point out that increasing technical skill
would not affect traffic safety behaviour in general. Although more experience of
driving before acquiring a license has shown a decrease in traffic accidents involving
novice drivers. It is argued that this is not because the novice has an increased
technical ability but rather that the driver becomes more aware of the risks of driving

Erik Sommarström
3
and learns to handle situations that could lead to accidents (Gregersen et al, 2000;
Hattaka et al, 2002). This would also be in line with the GDE-matrix, which includes
“Risk-increasing factors” as one of the three factors of driver education.
In the GDE-matrix self-evaluation is an important aspect of driving because it
regulates the other factors of driving education and it is also the main factor that is
important to continue becoming a better driver after acquiring the driver’s license. It
is shown that metacognitive skills are important for achieving an expert level of a
skill. A driver needs to know the limits of his or hers skill in order to improve them
(Hattaka et al., 2002). Also since car driving is essentially a self-paced action, where
the driver solely makes decisions on risk increasing factors such as speed and distance
to next vehicle. Good self-awareness would effectively lead to avoidance of risky
situations and accidents since the driver would know how fast he or she can drive and
still be safe (Bailey, 2009; Hatakka et al., 2002; Näätänen & Sumala, 1974).
Performance could loosely be divided in to three categories. These would be the three
levels of performance, strategic, tactical and operational according to Michon (1979).
The strategic level would be how the driver plans the trip before driving. Tactical
performance regards the planning of actions, which are executed at the operational
level. Hence the tactical level requires knowledge and awareness of ones own ability
on the operational level (Lundqvist & Alinder, 2007; Michon, 1979). If this is correct,
different accidents could be divided in to these three categories even though some
accidents are the result of a combination of several levels. If a pedestrian would
suddenly walk out onto the road an accident can be avoided with adequate reaction
time, which would correspond to the operational level. However, the driver might
have been able to slow down the car and be ready to break if the driver suspects that
someone would suddenly walk out onto the road, this would correspond to the tactical
level. Here the categories become quite fuzzy since it is difficult to place the accident
into a specific category. Thus it should be reasonable to assume that some accidents
can be caused more or less by inadequate self-awareness but perhaps not solely
because of it. Accidents on the strategic level would refer to bad planning of the
journey, such as driving at night or having to drive faster because of a time constraint.
Thus a line must be drawn on which accidents to focus on and also realize which

Erik Sommarström
4
accidents are caused by inadequate self-awareness and which are caused by
inadequate reaction time or other factors.
The Swedish statistics of accidents from 2013 (Transportstyrelsen.se, 2014) list the
most usual car accident types and their frequency. Five of the most frequent car
accidents are accidents with pedestrians or bike/moped, accidents where two cars
meet, accidents where one car drives in to another car from the rear and accidents
where a single car crashes. An analysis of the reason for the accidents from this
papers point of view would be that accidents where a single car crashes or when a car
drives in to the rear of another car would be caused by lacking self-awareness. For
example, if the driver has to little space to the car in front or that the driver drives to
fast and looses control of the vehicle. The accidents where you meet a car or hit a
pedestrian or bike/moped could be caused by both lacking self-awareness and
inadequate reaction time. In some cases the driver may be able to plan ahead to avoid
the accident but in some a car/bike/moped might suddenly loose control and drive into
the wrong lane in which case good reaction time would be needed more than good
self-awareness.
2.3 Operationalization

As mentioned earlier this study will measure self-awareness using selected DSI-items
(Warner et al., 2013) to measure a driver’s estimation of their driving ability and
comparing those with their actual ability in a simulator. For example, one DSI-item is;
“Conforming to the speed limits?”. The participant answers if this is a weak or a
strong ability on a scale from one to five, one being definitely weak and five being
definitely strong. In the simulator this exact question will be tested with an event or
stretch in the scenario and then compared to the self-assessment from the DSI. This
will give an estimation of how much the drivers own idea of his or hers ability differs
from ability in the simulator. This is similar to other studies where drivers have had to
rate themselves after a drive with an instructor and the instructor also rates their drive.
The self-assessment and the instructor’s assessment would then be compared to each
other (Lundqvist & Alinder, 2007; Mallon, 2006). This will be repeated for a five of
the DSI-items that are possible to measure in the scenario. The keen reader might
remember that the DSI was split in two parts - perceptual motor skills and safety

Erik Sommarström
5
skills. Theoretically the items that tests perceptual motor skills should be related and
vice versa, because of this the five different self-awareness measures were split into
two groups – perceptual motor skills and safety skills.
As a measure of traffic safety behaviour two different measures will be used. First of
the participants will answer a questionnaire about their history of traffic safety
behaviour (e.g. incidents, accidents, violations). Using Svenssons (1998) research
these answers will give each participant a value of traffic safety. Second, traffic safety
behaviour will be measured in a simulator. Since there is no research that specifically
states how traffic safety behaviour should be measured this will be done using six
different events in the scenario. For each event it was decided what was a safe
behaviour in the given situation. For example, merging in traffic was deemed safe if
the participant held a high time to collision (TTC) to the cars in the front and behind.
Time to collision measures the time in seconds to when both cars will collide. The
calculation needs to account for both the cars speed and trajectory and calculates the
time to the point they will collide. Hence if two cars are driving along side each other
and their trajectory never intersects the TTC will be infinite but if one car changes its
course so that the trajectories intersect there will be a TTC measure in seconds. Six
different events were used to capture different aspects of safe driving behaviour.
Using the self-awareness measure and comparing this to traffic safety behaviour in
the simulator it should be possible to see if self-awareness is related to a traffic safe
behaviour. The hypothesis in this study is that drivers with good self-awareness will
exhibit safer traffic behaviour.

Erik Sommarström
6
3 Method

3.1 Participants

27 participants completed the questionnaires and the two scenarios in the simulator.
The sample consisted of 45.8% women and 54.2% men. Participants were between 55
and 75 years with a mean age of 62.3 (SD = 5.11). Participants that did not finish the
simulator scenario or any of the questionnaires were excluded from the data. The
requirements for a participant to be contacted were that their age should be between
55 and 75. They should have a normal field of vision and also be driving at least 1500
kilometres per year. These requirements were used because the sample group were
made to correspond with a test group from another study. Participants were contacted
via mail through the Swedish vehicle registry. From a list of possible participants a
randomized sample of participants was selected. All participants lived in the
Linköping area in Sweden. The participants all received 500 SEK for participating.
3.2 Questionnaires

The driver skill inventory (DSI) was used to rate self-awareness (Lundqvist &
Alinder, 2007). The DSI consists of eleven items relating to perceptual control skills,
such as car control and nine items relating to safety skills. The participant answers
each question with the participant’s weakest and strongest sides in mind. Each item is
constituted by a question and a five-point scale where 1 is “definitely weak” and 5 is
“definitely strong”.
The participants also answered the Multidimensional locus of control-questionnaire
(T-loc) about their risk taking and their perspective on contextual factors affecting
potentially dangerous situations (i.e. what factors in traffic are responsible for
accidents) (Özkan & Lajunen, 2005). This questionnaire consisted of seventeen items,
which were rated on a five-point scale, 1 being “not at all possible” and 5 being
“definitely possible”.
After driving the simulator the participants answered a questionnaire with questions
regarding driving experience of the simulator and their traffic experience. Participants
also answered a questionnaire regarding their involvement in traffic accidents in the

Erik Sommarström
7
last three years. This questionnaire was however rejected from the analysis since it
was noticed that almost none of the participants answered more than zero accidents on
the questions. One of the questions that related to near-incidents was also interpreted
differently by many participants and therefor couldn’t be analysed for within group.
3.3 Simulator

The simulator that was used in the study is the “Simulator III” at VTI in Linköping. It
is a motion-based simulator that can simulate lateral and longitudinal forces. The
simulator uses a vibration table under the chassis to simulate contact with the road
and provide a more realistic driving experience. The graphics are PC-based and uses
six projectors to create a 120-degree frontal view and three smaller screens for the
rear-view mirrors. The simulator can be used with either manual or automatic
gearbox. In this study the automatic was used.
3.4 Procedure

When contacting participants via mail they were given the DSI and the T-loc
questionnaire. Participants answered these at home and then handed them in to the
researcher before driving the simulator. The test took approximately 90 minutes and
consisted of driving two simulator scenarios. After the scenarios were finished the
participants answered one questionnaire about accident-involvement and one
questionnaire about the simulator in general.
Before driving the scenarios participants were given seven minutes of practice in the
simulator. During this time participants could ask the researcher questions, which they
were told not to do during the test scenarios. Participants then drove the first scenario
of two.
3.4.1 Scenario
1

The purpose of this scenario was to test the participant’s driving ability and driving
safety skills. The scenario consisted of a two-lane rural road, a four-lane highway and
finally driving in an urban environment. During each stretch the participants were
faced with potentially dangerous events. For example merging in heavy traffic or

Erik Sommarström
8
having to emergency-break before “hard-to-see” pedestrians’ walking/running out
onto the road. These events were scattered throughout the different settings and
environments of the scenario. The scenario lasted for 50 minutes. Once the
participants had completed the scenario, they stopped the car and got ready for
scenario 2.
3.4.2 Scenario
2

The purpose of this scenario was to test the participant’s field of vision. Participants
fitted themselves with two clickers, one on each index finger. The participants had
received instructions on how to use and attach the clickers before starting the first
scenario. During the scenario, if the simulator screen showed a blue/white road sign
the participant was instructed to click the left index finger clicker. If the screen
showed a red/yellow sign they were to click the right index finger clicker.
The scenario lasted for 7 minutes. This data could then be analysed according to
signal detection theory to see the ratio between true hits/misses and false hits/misses
(Solso, 1988). For a further explanation of a similar test see Jenssen (2003).
After the participants were finished driving they filled in a questionnaire about the
simulator and also a questionnaire about their accident involvement the last three
years.
3.5 Analysis

3.5.1 Design

This study uses a within-group design where the independent variable is grouped into
three groups. The independent variable is self-awareness, which is grouped into three
parts: over-estimator, under-estimator and good self-awareness. The dependent
variable is traffic safety behaviour. The different groups of the independent variable
are then compared to the dependent variable in an ANOVA. The statistical tests will
be explained in more detail later.

Erik Sommarström
9
3.5.2 Measures

The control variables that were measured were kilometres driven per year, accident
involvement during the last three years, years of having a driver’s license, age and
gender.
3.5.3 Simulator
measures

To measure how a participant has performed in the simulator each event in the
scenario needs different measures. The reason for using different measures and not a
single on is that each unique measure gives different aspects in the driving behaviour
of the participant. The measures used in the study are the following: Time to collision
(TTC), Time head way (THW), Lane-keeping, Speed-keeping and reaction time.
These will be explained in more detail below.
• TTC, as mentioned earlier, measures the time until the participant’s car and
another car will collide, given the speed and trajectory of both vehicles. The
minimum TTC a participant reached was the TTC-measure for that event.
• THW measures the time until the next vehicle if the vehicle in front would
suddenly stop, this does not take trajectory or speed of the other vehicle into
account. As with the TTC-measure the THW also only uses the minimum
value for an event. TTC can be said to measure cooperation in traffic and
THW measures the safe behaviour of the individual in the traffic context.
• Lane-keeping measures the car’s lateral positioning on the road, in this study
the standard deviation of Lane-keeping is used which gives a measure of how
well the participant has kept the car steady.
• Speed-keeping in this study measures the ratio between how many times the
participant drives within the speed-limit and not. Within the speed-limit is
classed as 2.5 km/h above or below the speed limit.

Erik Sommarström
10
• Reaction time is measured in milliseconds between the time it takes for a
participant to react to an object after it becomes visible (i.e. pedestrian
walking out from behind a bus).
Lane-keeping and Reaction time will have an inverse value compared to the others
since all values need to be the higher the better or vice versa to be able to compare to
each other. This does not affect variance at all.
3.5.4 Self-‐awareness
and
traffic
safety
behaviour
measures

The independent variable was self-awareness (SA) and the dependent variable was
traffic safety behaviour (TS). To measure SA specific DSI items were compared with
the participants’ actual performance in the simulator. For example, one of the items in
the DSI is “Conforming to the speed limits” where the participants answered a
number between one and five (one being definitely bad and five being definitely
good). SA was then calculated using the residual values from the regression line
between a specific DSI item and its simulator counterpart. This method of using
residuals is illustrated with the graph below. The regression line is the optimal SA
compared to the normal distribution of all the participants and the difference between
the regression line and the participants’ actual answer and performance is the self-
awareness measure.
Figure 1 – The regression line is the optimal SA. If a participant answers a four on the DSI and shows a
speed deviation of 1.2 the true SA for the participant would be 0.8475, the difference between the actual and
the optimal SA (i.e. the residual). It should be noted that this is only an example and not actual data.

Erik Sommarström
11
Five variables for SA were created from DSI items 1 (i.e.“Fluent driving”), 5 (i.e.
“Predicting traffic situations ahead”), 7 (i.e.”Fluent lane-changing in heavy traffic”),
11 (i.e. “Keeping a sufficient following distance”) and 16 (i.e. “Conforming to the
speed limits”). These items were compared to suitable simulator measures that
reflected on the nature of the item. The residuals were calculated for each DSI-item.
These five SA-measures were then unified using the categories of the DSI, which
reduced self-awareness to two variables; “Traffic safety skills” (DSI 1, 5 and 7) and
“Perceptual motor skills” (DSI 11 and 16). In the table below the different simulator
measures used for each DSI item is presented.
DSI item Simulator measure
DSI 1 - Fluent driving (Traffic safety
skills)
TTC, Lane-keeping, Speed keeping
DSI 5 - Predicting traffic situations ahead
(Traffic safety skills)
Reaction time to breaking before a
pedestrian walking/running out onto the
road.
DSI 7 - Fluent lane-changing in heavy
traffic (Traffic safety skills)
TTC
DSI 11 - Keeping a sufficient following
distance (Perceptual motor skills)
THW
DSI 16 - Conforming to the speed limits
(Perceptual motor skills)
Speed keeping
Table 1 – A table over what measures was used for each used DSI item
The TS-variable was measured using specific events in the scenario. For each event
different measures were chosen depending on what was deemed as safe traffic
behaviour in that specific event. For example, one event consisted of keeping a good
following distance to the car in front; in this case distance to the car ahead was
measured in seconds with regard to the participants own speed (i.e. Time head way:
THW). For other events in the scenario measurements such as speed keeping, lane
keeping and TTC were measured depending on what was relevant and traffic safe to

Erik Sommarström
12
that event. The different traffic safety measures summed up to six different TS
variables with different measures between them.
3.5.5 Statistical
tests

To test for normality both Shapiro-Wilk and Kolmogorov-Smirnov were used. For the
main statistical test a multivariate ANOVA was used to test for effects between the
SA-measures and the TS-measures. Pearson’s correlation coefficient was used to test
the correlation between the SA-measures. A Linear regression was used to test how
well the SA-measures could predict the TS-measures.

Erik Sommarström
13
4 Results

Below are the descriptive data for the SA measures presented. It should be noted that
both of the measures are normally distributed with a non-significant Shapiro-Wilk and
Kolmogorov-Smirnov score. The closer to zero a SA-value is the better SA the
participant has.
SA-Measure N Min. Value Max. Value Mean (SD)
Perceptual
motor skills
20 -1.02 1.81 0.063 (0.68)
Safety skills 24 -0.94 1.43 0.006 (0.575)
Table 2 – Descriptive data of the self-awareness measures.
There was no significant correlation between the two measures for SA, p = .427.
Because of this, each measure of SA was analysed independently from each other.
Below are the descriptive data for the TS measures. These are also tested for
normality of distribution. The measures that are not normally distributed are marked
with a *. After a logarithmic transformation of all the data TS 3 and TS 6 were still
not normally distributed but TS 1 was. Seeing as the logarithmic transformation didn’t
do much change to the TS-variables they were instead rejected from the analysis. The
higher TS-value is the safer a participant is.
Traffic Safety
measure no.
N Min. Value Max. Value Mean (SD)
TS 1* 27 2.579 12.467 6.458 (2.38)
TS 2 26 1.41 6.14 3.8 (1.53)
TS 3* 27 3.143 74.301 12.927 (14.233)
TS 4 27 0.512 3.691 1.691 (0.854)
TS 5 23 2.57 11.21 5.918 (2.52)
TS 6* 27 -0.10 0.85 0.112 (0.237)
Table 3 – Descriptive data for traffic safety behaviour measures.
Before the analysis the SA measures were split into three groups depending on their
mean-value and standard deviation: Over-estimation, Good SA and Under-estimation.

Erik Sommarström
14
“Under-estimation” was defined as the mean plus half the standard deviation of SA.
“Over-estimation” was defined as the mean minus the half the standard deviation of
SA. “Good SA” was the values in between these extremes. This was done since the
variables for SA ranged from, for example -1.02 to +1.8 where the perfect SA would
be zero or close to zero.
One multivariate ANOVA were made as an exploratory analysis to see if there were
any effects between the two grouped SA variables and the three normally distributed
TS variables. There were no significant effects between the groupings of both the SA
variables and the TS variables when doing a pairwise comparison using Bonferroni
correction, p > .1. There was however one significant effect between SA in safety
skills and the second TS variable, F(5,9) = 5.86, p < 0.5, 𝜔= .57. Although this effect
is under much doubt given the null-results of all the other effect but also that there is a
possibility that the variables tested may share some variance. This effect is therefor
rejected.
In order to see if good or bad SA could predict traffic safety an absolute value of each
the SA variables were made. This means that a negative value will become positive
and it will make a more exact comparison possible. Although this takes away possible
effect of over and under estimation of SA a more exact test can be used instead, which
in effect utilize all of the variance. However, the effect of under and over estimators
has already been ruled out by the ANOVA. To see how well self-awareness predicts
traffic safety behaviour regression analysis was used. A regression analysis was made
for each of the SA-variables and each of the TS-variables. They did however reveal
no significant effects at all p> 0.1.

Erik Sommarström
15
5 Discussion

The hypothesis of this study was that participants with good self-awareness would
exhibit better traffic safety behaviour. From the analysis the null-hypothesis could not
be disproven. However, this does not mean the hypothesis has been disproven since
there could still be a number of reasons for why this is. The overall statistical power
for the tests was very low and therefor a complete rejection of the hypothesis is
impossible. The first part of the discussion will mainly circle around the reliability of
the measurements used in this study. The second part will discuss results and what
these results would mean if they would be valid and alsox propositions for future
research and proposed changes to this study. The discussion will finish up with a brief
conclusion.
5.1 Result
discussion

A reason for the null-results might be that self-awareness does not directly affect
traffic safety behaviour as suggested in the hypothesis. It might instead affect traffic
safety indirectly through other cognitive functions, such as learning. A point that is
raised in the introduction is the importance of metacognition for learning new skills
fast and become good at it. It might be that self-awareness is only important when
learning from accidents and how well you learn from feedback. If you receive perfect
feedback on your abilities this would give you a better idea on what you would need
to change in order to improve your ability. If this were the case it would not be
strange that these results point towards the null-hypothesis since self-awareness might
only affect learning the ability to drive. This is actually compared with the GDE-
matrix in the introduction where self-evaluation is pointed out as an important factor
for learning. Self-awareness would, in the context of the GDE-matrix, be the accuracy
of self-awareness.
The difference in self-awareness between participants could in this case be a result of
the method used to calculate the measure. The residuals from the regression will
assume normality since the regression line will take into account every case in the
variance. If the participants are all very self-aware drivers the residuals will show a
difference between participants even though there might not be a big difference in
reality. In light of this the measurement used would be very crude and might only
give a fair result if the group tested is younger and not as experienced. This could in

Erik Sommarström
16
that case be tested in a study where the participant has to learn a new skill and then
measure the participants’ self-awareness in comparison to the performance of the
newly learnt skill.
In an article by Kruger and Dunning (1999) it is stated that a possible way to remedy
incompetence is by teaching the incompetent person in the subject where they have
inadequate knowledge. This was shown through a series of trials where participants
got to assess their own skills their performance, which was then compared to their
actual performance. It could be seen that good performers underestimated their ability
while bad performers often over-estimated themselves. The effect of the under-
estimators was credited to the false-consensus bias that if they perform well their
peers should perform equally well. The over-estimators were explained by the fact
that they might not know how incompetent they actually are since they do not have
this knowledge. This could not be replicated in this study but it might be, as noted
earlier, an effect of the participants’ level of experience with driving.
In the introduction self-awareness is connected with the risk homeostasis theory. The
theory that you always keep the same level of risk no matter what safety functions
there are (e.g. seat-belt, airbags, ABS). If self-awareness would affect the level of risk
for a person or any other trait this might not be reflected in the simulator scenario. It
might very well be that the simulator scenario does not challenge the driver’s ability
and that such effects are never noticed in the data because of this. This would go
against the connection between traffic safety and levels of performance. It is claimed
earlier that self-awareness would be connected with accidents made on the tactical
level of performance, where the driver can plan the execution of the operational level
of performance. The simulator scenario does reflect on both types of accidents but it
might not reflect on them in the correct way or measured in the correct way for that
matter.
5.2 Methodological
discussion

As is often found, more participants are needed to replicate a result. In the case of this
study the statistical power becomes weak since the independent variable is grouped
into three sub-groups. If a replication study were to be made there should however be
a number of changes made. Mainly the simulator scenario should be much more

Erik Sommarström
17
specific as to what it tests and therefor it could be much shorter. A bigger difference
that would be made is to have a questionnaire before the driving test that asks about
general driving ability, as it was in this study, but also have a questionnaire after that
asks how the simulator driving went. Even though previous research has shown that
these self-assessments’ would be very similar (Kruger & Dunning, 1999). This
questionnaire would of course have the same questions so that they could be
compared. In this study this might be a big problem, since driving in a car simulator is
a different task than driving a real car, even if it seems like a similar task.
The outcome of the analysis could be the result of bad reliability in the study. Data
from a simulator study needs a lot of work before it can be analysed. This can result
in a lot of reliability problems where the researcher thinks that a measure measures
what is sought after when in reality it does not. The traffic safety measures are made
from hypothesis’ that certain behaviour is more traffic safe than other behaviour. In
this study the minimum value of time to collision (TTC) and the minimum value of
time head way (THW) was measured. This was in line with the assumption that a
lower THW and TTC would be a safer behaviour than a high TTC and THW. This
assumption might well still be true but in the specific events of this study it might not
have been the most correct solution. This could be validated using other measures of
traffic safety behaviour or having an outside driving instructor to assess the traffic
safety of a specific participant.
For the self-awareness measures there might be a similar problem with reliability.
Partly because it is difficult to know if the DSI-item was compared to the correct
simulator measure since this is also built upon assumptions that the measure is
measuring the correct thing, for example speed keeping in an adequate way.
Moreover it might also be a problem with participants interpreting the scale of the
subjective measure in the same way; a three for one participant might be very high
compared to what another participant thinks. It was shown in the analysis that the two
measures for self-awareness were not correlated. This might seem like a problem but
it should also be emphasized that according to the research done on the DSI
questionnaire these measure different categories of weak versus strong driving, the
non-correlated self-awareness measures might only be a reflection of different kinds
of self-awareness. It should also be noted that the residuals of the regression would

Erik Sommarström
18
naturally be normally distributed. This might in effect give a perfect self-awareness
score to a participant that might not be perfectly self-aware. This would however not
affect the within-group comparisons’ since it would have the same variance.
5.3 Concluding
remarks

The analysis of the data gave results pointing towards the null-hypothesis, that people
with good self-awareness do not exhibit safer traffic behaviour. Given this unexpected
result it was hypothesized that self-awareness might not affect traffic safety at all but
rather it might affect learning how to become a safe driver. This would also imply that
the null-result might depend on the older age of the drivers. If the study would be
replicated using a more diverse sample with both young and old drivers this might
affect the result. However, since the statistical power of the result is not enough a
rejection of the hypothesis is not possible.

Erik Sommarström
19
6 References

Amado, S., Arıkan, E., Kaça, G., Koyuncu, M., & Turkan, B. N. (2014). How
accurately do drivers evaluate their own driving behavior? An on-road observational
study. Accident; Analysis and Prevention, 63, 65–73. doi:10.1016/j.aap.2013.10.022
Bailey, T. (2009). Self-awareness and self-monitoring: important components of best
educational practice for novice drivers. In Infatns, Children and Young People and
Road Safety 2007.
Brown, A. L. (1978). "Knowing when, where, and how to remember: A problem of
metacognition" in R.Glaser (Ed.) Advances in instructional psychology Vol.1 pp.77-
165 (Mahwah, NJ: Erlbaum) [0]
Bandura, A. & Cervone, D. (1983). Self-evaluative and self-efficacy mechanisms
governing the motivational effects of goal systems. Journal of Personality and Social
Psychology, [online] 45(5), pp.1017-1028. Available at:
http://dx.doi.org/10.1037/0022-3514.45.5.1017 [Accessed 23 Oct. 2014].
Endsley, M. R. (1995). Toward a theory of situation awareness in dynamic
systems.pdf. Human Factors, 37(1), 32–64.
Evans, L. (1991). Traffic safety and the driver. NY: Van Nostrand Reinhold
Gregersen, N. P., Berg, H. Y., Engström, I., Nolén, S., Nyberg, A. & Rimmö, P. A.
(2000). Sixteen years age limit for learner drivers in Sweden--an evaluation of safety
effects. Accident; Analysis and Prevention, 32(1), 25–35. Retrieved from
http://www.ncbi.nlm.nih.gov/pubmed/10576673
Groeger, J. A. & Grande, G. E. (1996). Self-preserving assessments of skill? British
Journal of Psychology, 87, 61–79.
Hatakka, M., Keskinen, E., Gregersen, N. P., Glad, A. & Hernetkoski, K. (2002).
From control of the vehicle to personal self-control; broadening the perspectives to

Erik Sommarström
20
driver education. Transportation Research Part F: Traffic Psychology and Behaviour,
5(3), 201–215. doi:10.1016/S1369-8478(02)00018-9
Hoyes, T. W., Stanton, N. A. & Taylor, R. G. (1996). Risk homeostasis theory: A
study of intrinsic compensation. Safety Science, 22(1-3), 77–86. doi:10.1016/0925-
7535(96)00007-0
Jenssen, G., Stene, T., Bjørkli, C., Fosse, P. and Arnljot, H. (2003). Effects of visual
impairment on driver performance and accident risk. Trondheim.
Kolb, D. (1984). Experiential learning. Experience as the source of learning and
development. Englewood Hills, NJ: Prentice-Hall.
Kruger, J., & Dunning, D. (1999). Unskilled and unaware of it: how difficulties in
recognizing one’s own incompetence lead to inflated self-assessments. Journal of
Personality and Social Psychology, 77(6), 1121–1134. doi:10.1037/0022-
3514.77.6.1121
Lundqvist, A. & Alinder, J. (2007). Driving after brain injury: self-awareness and
coping at the tactical level of control. Brain Injury  : [BI], 21(11), 1109–17.
doi:10.1080/02699050701651660
Mallon, K. L. (2006). The influence of self-awareness of driving ability on on-road
performance of persons with acquired brain injury.
Mezirow, J. (1990). Fostering critical reflection in adulthood. San Francisco: Jossey-
Bass Publishers.
Michon, J. (1979). Dealing with danger. Retrieved from
http://www.jamichon.nl/jam_writings/1979_dealing_with_danger.pdf
Näätänen, R. & Summala, H. (1974). A model for the role of motivational factors in
drivers’ decision-making. Accident Analysis & Prevention, 6, 243–261. Retrieved
from http://www.sciencedirect.com/science/article/pii/0001457574900037

Erik Sommarström
21
Peräaho, M., Keskinen, E. and Hatakka, M. (2003). Driver competence in a
hiearchical perspective; implications for driver education. Swedish Road
Administration.
Solso, R. (1988). Cognitive psychology. Boston: Allyn and Bacon.
Svenson, O. (1981). Are we all less risky and more skillful than our fellow drivers?
Acta Psychologica, 47, 143–148.
Svensson, Å. (1998). A method for analysing the traffic process in a safety
perspective. Ph.D. University of Lund.
Stapleton, T., Connolly, D. & O’Neill, D. (2012). Exploring the relationship between
self-awareness of driving efficacy and that of a proxy when determining fitness to
drive after stroke. Australian Occupational Therapy Journal, 59(1), 63–70.
doi:10.1111/j.1440-1630.2011.00980.x
Trafikverket.se, (2014). Har du koll på vad du ska kunna vid körprov B? -
Trafikverket. [online] Available at:
http://www.trafikverket.se/Privat/Korkortsprov/Har-du-koll-pa-vad-du-ska-kunna-
vid-korprov-B/ [Accessed 20 Oct. 2014].
Transportstyrelsen.se, (2014). Dödade och svårt skadade efter olyckstyp -
Transportstyrelsen. [online] Available at:
https://www.transportstyrelsen.se/sv/Press/Statistik/Vag/Olycksstatistik-
gammal/Olycksstatistik-vag/Nationell-statistik1/Manadsvis-statistik/Aktuell-
olyckstyp/ [Accessed 20 Oct. 2014].
Warner, H., Özkan, T., Lajunen, T. and Tzamaloukas, G. (2013). Cross-cultural
comparison of driving skills among students in four different countries. Safety
Science, [online] 57, pp.69-74. Available at:
http://dx.doi.org/10.1016/j.ssci.2013.01.003 [Accessed 23 Oct. 2014].

Does accurate self-awareness increase traffic safety behaviour

More Related Content

Similar to Does accurate self-awareness increase traffic safety behaviour

Does accurate self-awareness increase traffic safety behaviour