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T A B L E O F C O N T E N T S
ABSTRACT............................................................................................. 2
INTRODUCTION................................................................................... 3
1. LITERATURE REVIEW... 4
2. METHODOLOGY.................................. 7
3. RESULTS................................................................................................ 12
4.
CONCLUSIONS AND
RECOMMENDATIONS......................................................................18
5. REFERENCES..................................................................................... 21
6. BIBLIOGRAPHY................................................................................... 24
APPENDIX 1: SAMPLE SIZE CASLCULATOR.............................. 25
APPENDIX 2: SURVEY........................................................ 26
APPENDIX 3: EXPERIMENT.............................................................. 31
APPENDIX 4: RESULTS ANALYSIS (first 2 objectives)................. 35
APPENDIX 5: RESULTS ANALYSIS (3rd and 4th objectives)...... 47
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ABSTRACT
Failure to heed traffic signs is one of the most common causes of road accidents. The success ofeffective communication of traffic sign messages to road users depends not only on driver
characteristics but also on the signs themselves. This paper addresses the effects of driver
characteristics and sign features on the understandability of traffic signs. Driver characteristics
considered here include: income, education, age, marital status and gender. Sign features examined
here contain: presentation model, familiarity, shape and color. The population was sampled from
different districts of Tashkent city.
The study was divided into two stages: survey and experiment. The main purpose of the survey wasto obtain information regarding driver characteristics, whereas experiment was primarily employed
to test the sign comprehension level of the drivers.
The results indicated that income, education and gender have a significant effect on comprehension
of traffic signs, while marital status has only paltry effect. In addition, the differences in
comprehension were revealed between two categories of drivers. Namely older drivers with high
income and level of education comprehend signs better than their young counterpart with low level
of income and education. Furthermore, familiarity, color and shape of the traffic sign were found to
be highly correlated with sign understandability. Finally, the traffic sign system proved to be
effective because it is based mainly on symbolical models of the signs which are found to be more
eye-catching than verbal ones.
These findings are believed to be important for the road sign designers. In addition, they might be
useful for relevant organizations aimed at increasing the effectiveness of traffic education.
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INTRODUCTION
RESEARCH QUESTION:
How do driver factors and design features affect comprehension of traffic signs?
RESEARCH OBJECTIVES
1. to identify general factors that influence comprehension of traffic signs based on the
studies of researchers from US, Europe, Asia and Arabic countries
2. to compare these findings with those indicated by a sample from Tashkent
3. to reveal differences in comprehension among several categories of drivers4. to evaluate the effectiveness of traffic sign system in Tashkent
Traffic signs serve as one of the most common tools for traffic control. Their main purpose is to
regulate, warn and guide road users in a traffic system (Dewar and Olson, cited in Ng, 2007). A
'proper' traffic sign posted in the right place allows the road users to avoid problems on the road,
which could be as simple as traffic slow down or as bad as fatal accidents (Kurniawan and
Zaphiris, 2001). Despite their importance traffic signs are not always applied effectively.
Indeed, seventy percent of traffic signs are ignored by drivers (Shulz, 2006). This evidence shows
an importance of a detailed analysis of key factors influencing the effectiveness of traffic signs.
Conspicuity, reaction time, legibility distance, glance legibility and comprehensibility1 are
considered to be vital in traffic sign design. Among all these factors engineers from Australia, New
Zealand, Canada, and the USA rated comprehensibility as the most important criteria (Dewar et al.,
cited in Shinar et al., 2003). However, in a multi-country research, which included five Arabic
countries, it was found that drivers comprehended only about 56% of the 28 signs presented to them
(Al-Madani and Al-Janahi, 2002). This demonstrates an urgent need for a deep investigation of
factors affecting comprehension performance of road users.
1 Comprehensibility of a sign is a measure of how readily an observer can understand the message intended to be conveyed by thesign. (CIE, cited in Ng, 2007)
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LITERATURE REVIEW
The importance of setting the research project within a conceptual context should not be
underestimated. A lot of previous related studies were found to ensure the credibility of the current
investigation.
Indeed, such driver factors as age, marital status, income, education, driving experience and
nationality were analyzed by prominent researchers from the US, Europe, Asia and Arabic countries
to find how they impact on comprehension performance. There are a lot of similarities as well as
differences in the results of surveys conducted.
Researches on effect of income, nationality, education, marital status and driving experience
demonstrated similar results. For example, Al-Madani and Al-Janahi (2002) found that:
Western drivers comprehend the signs significantly better than drivers of other nationalities.
Drivers, in the various experience categories, holding low educational qualifications and in
low income categories comprehend signs significantly less well than those who are holding
high level of education and with high income.Excluding drivers between 3544 years of age,drivers comprehension of signs is not related with experience for any other age group.
Understanding traffic signs does not change significantly with years of driving experience in
female drivers. Male drivers with over ten years of driving experience are significantly better
than less experienced male drivers. Single and married drivers understand the signs equally
well.
These results comply with the findings of Al Gadhi (1994) who also found that there is a positive
relationship between education and income and comprehension performance, while there is no
correlation between single and married drivers. Ng and Chan (2008) agree with Al Madani and Al-
Janahi on that the driving experience has no effect on sign comprehension.
In some cases the results of the studies contradict rather than comply with each other. For example,
there are continuing debates on how age affects the comprehension performance of the drivers.
Some researchers found that older drivers have poorer understanding of traffic symbols than
younger drivers do. Dewar et al. (1994) studied the comprehension level of 85 US traffic signs fordrivers from Texas, Idaho, Alberta and Canada. It was found that there was no difference for 52
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signs analyzed, meanwhile drivers in the older age group (60 and over) understood less well than
the younger ones for the remaining 33 signs. The supporting evidence was provided by another
survey (Shinar et al., 2003) conducted mainly in European countries. Nevertheless, if Arabian
countries are considered, the results are absolutely different there. Indeed, Al-Madani and Al-Janahi
(2002) found that comprehension of traffic signs is positively correlated with drivers age, whichcontradicts the previously mentioned findings. Interestingly, there is also a third approach to
evaluation of the correlation between age and comprehension. To be more precise, Ng and Chan
(2008) found that driver factors of age group had no effect on comprehension performance. The
most rational explanation for such a split of opinions is the difference in nationalities sampled, time
during which the surveys were conducted, distinctions in design features of traffic signs and some
other specific factors.
Not only driver factors but also the design features of the signs affect the success of effective
communication of traffic sign messages to road users. Some researchers investigated the
relationship between compliance with ergonomic principles2 and comprehensibility of traffic signs.
In 2004, Shinar and Ben-Bassat in their study Are ergonomically designed traffic signs more
comprehensible? tested directly the the relationship between sign comprehension and the extent
that the sign complies with three ergonomic principles: sign-content compatibility, familiarity, and
standardization. The results of this study illustrated strong correlation between sign compliance
with each of three ergonomic principles and signs comprehension probability. A positive
relationship between familiarity and comprehensibility was also identified by Dewar et al. (1994) as
well as Ng and Chan (2008).
Other researchers emphasized the importance of sign location, color and shape in comprehensibility
of traffic signs. According to Borowsky et al. (2008) drivers were less likely to identify the traffic
sign when it was located in an unexpected location. The results of the study led to the following
conclusion: to increase their timely probability identification, traffic signs should be posted in
2There are five main ergonomic principles relevant to traffic signs design (Sanders andMcCormick, cited in Ben-Bassat, 2003):1) Spatial compatibility the physical arrangement in space, relative to the position ofinformation and directions2) Conceptual compatibility the extent to which symbols and codes conformto peoples associations3) Physical representation the similarity between the content of the sign and the reality it represents4) Familiarity the extent to which the driver is familiar with the sign from his drivingexperience5) Standardization the extent to which the codes used for different dimensions like color
and shape are consistent for all signs
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expected locations (Borowsky et al., 2008). However, proper location alone is not sufficient unless
it is accompanied by appropriate color and shape. Thus, Gao, Podladchikova and Shaposhnikov
(2003) applying specific computational visual models, found that traffic signs are better recognized
when their color and shape contrast with that of background (e.g. billboards, trees). For example,
commonly used green colored direction sign will be hard to recognize if it is posted near trees. Onthe other hand, the findings of Zakowska (2001) demonstrate that adjustment of traffic signs to the
background may lead to misunderstanding, since in this case the same message will be displayed by
different signs
Before applying the above mentioned sources in our research the strengths and weaknesses should
be critically assessed. The studies of Al-Madani and Al-Janahi(2003), Zakowska (2001), Ng and
Chan (2008), Ng (2007) appear to be the basis for our future research, since they are relevant, up-
to-date and based on large samples from a wide range of countries. These studies helped us to
identify the main factors, which affect comprehension and hence effectiveness of traffic signs.
Furthermore, a lot of researchers, e.g. Ben-Bassat (2003) and Shinar et al.(2003), use findings of the
above mentioned investigators in their studies. However, the results of some of the reviewed
sources can not be generalized due to the following limitations. For example, the findings of Dewar
et al.(1994) and Al-Gadhi et al.(1994) obtained 15 years ago could be obsolete and hence not
relevant in our research. Some results (e.g. Al-Madani, Al-Janahi, Ng ) of the investigations were
distorted as there were a limited number of females in the experiments conducted. In addition, the
questionnaires were not completely filled in by respondents. For example, only 70% of the
questions were answered in Hong Kong (Ng and Chan, 2008).
All in all, a thorough research concerning influence of different factors on traffic sign
comprehension was made in developed countries as well as in developing ones. However, the main
emphasis was placed on American, European and Arabic countries with practically no study of
Central Asia. In the following report, a detailed research is conducted in the capital of Uzbekistan -Tashkent - with the aim of identification the degree of dependence of traffic signs comprehension
on specific driver factors and design features. Furthermore, the weaknesses of the current traffic
signs system will be identified and ways of improvement will be suggested.
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METHODOLOGY
APPROACH AND HYPOTHESES
The research is carried out using deductive approach for several reasons. Firstly, a cause-effect link
between selected variables will be investigated without explaining the nature of such relationship.
For example, it was found that income of the driver is positively correlated with ones
comprehensibility of traffic sign. However, the reasons, why rich people better recognize the signs
will not be mentioned.
Secondly, a lot of theoretical concepts, describing the impact of different factors on traffic signs,
have already been developed. Therefore, in our research we are not going to formulate a theory.
Instead, we are planning to test established theories through corresponding hypotheses with a
purpose of confirming, rejecting or modifying the theories depending on their applicability to
Tashkent. The following hypotheses are formulated:
1. Monthly income, education, and age have a significant effect on comprehension of traffic signs,
while marital status and gender have no or paltry effect.
2. Pictorial sings are more eye-catching than verbal ones ceteris paribus.
3. Familiarity, color and shape of traffic signs are strongly correlated with their understandability.
4. Old drivers with high income and level of education recognize sings better than young ones with
low income and level of education.
SAMPLING
In order to test the above stated hypotheses, first of all, the suitable sample size as well as technique
should be identified. Particularly, sample size of 384 was estimated using Raosoft calculator
(Raosoft sample size calculator, 2004) (Appendix 1). This sample was marked as a recommended
one given 5% margin of error (taken by researchers in most cases), nearly 440 thousand
population3, and normal probability distribution.
3Jalilov, J.R., personal communication with the head of Uchtepa district GIBDD, (01 April 2009).
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Turning to the sampling technique, stratified random sampling seems to be the most appropriate for
the research. The stratification was done proportionally according to the number of cars registered
per each district (Table 1 and Graph 1). This was done to ensure that sample is as close to
population in terms of driver characteristics as possible (choosing only one district may causedistortion of results as the drivers living in this district may not have the same driver characteristics
as all drivers of Tashkent) Later, the desired number of respondents is going to be picked randomly
at the randomly chosen car parks in each of the districts so as to avoid bias in the selection of
samples, thus ensuring reliability and validity of sampling technique.
Table 1:
Proportional stratification according to the number of cars registered per district
Name of the
district
Number of registered cars perdistrict as at 1 January 20094
(in thousands)
Percentage
out of total (%)
Sample size
Mirobad 57.2 13 50
Yakkasaray 30.8 7 27
Mirzo Ulugbek 74.8 17 65
Shayhantour 17.6 4 15
Yunusabad 39.6 9 35
Chilanzar 61.6 14 54
Hamza 39.6 9 35Sergeli 26.4 6 23
Sabir Rahimov 48.4 11 42
Uchtepa 30.8 7 27
Bektemir 13.2 3 11
TOTAL 440 100 384
Graph 1: Sample size in each of the districts
27
65
15355435
23
42 27 11 50
Mirobad YakkasarayMirzo Ulugbek Shayhantour Yunusabad ChilanzarHamza SergeliSabir Rahimov UchtepaBektemir
4 Jalilov, J.R., personal communication with the head of Uchtepa district GIBDD, (01 April 2009).
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STRATEGIES
In our cross-sectional study, we will employ a combination of survey and experiment, because our
enquiry has several different aims, which will be achieved through the use of corresponding
methods. Survey will enable us to gather a considerable amount of data in a timely and efficient
manner. Furthermore, due to the simplicity and familiarity of the survey, a high respondent rate is
expected. On the other hand, experimental strategy will be adopted to test drivers ability to
recognize a wide range of traffic signs within certain time constraints.
Use of multi-methods will enables us to triangulate the data, in other words, it will ensure that the
data obtained will be interpreted in a right way. For instance, the findings obtained through
questionnaires will be supported by data from an experiment. Besides, since data are affected by the
method used, combination of several methods will minimize so called method effect.
RESEARCH INSTRUMENTS
Strategy: Survey
Research instrument: Self-administered questionnaire.
The primary purpose of the survey is to collect standardized data necessary for testing the first two
hypotheses. For this reason, self-administered delivery and collection questionnaire appears to be
the most suitable technique. In addition, compared to other research instruments, such as telephone
interview, questionnaires are less expensive in terms of time and money as well as easier to analyze.
The questionnaires mainly concerned with respondents biographical information would be
distributed among sampled at randomly chosen car parks in each of the districts. Respondents are
expected to answer multiple-choice, numerical, open-ended and ranking questions to identify their
driver characteristics (age, gender, etc.), to evaluate their comprehension of signs and to assess the
importance of some features of traffic signs (Appendix 2).
To ensure that the data collected via questionnaires will enable a research question to be answered,a pilot test with 15 volunteers will be conducted. Thereby, questions validity and suitability,
reliability of the data obtained as well as clarity of instructions will be assessed.
Strategy: Experiment
Research instruments: Sign comprehension test
In the next stage of our study, experimental strategy will be employed to test drivers ability for proper sign recognition (Appendix 3). For this reason, sign comprehension test would be
administered. Approximately 11 signs from different categories (regulatory, warning, guide signs)
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would be presented to respondents who are expected to call names of each of the signs or at least
their meaning. In addition, 7 pictorial and verbal signs with the same meaning content are going to
be shown. These signs would be placed far away from respondents and displayed only during 20
seconds after which the respondents are expected to answer which of the sign types (pictorial or
verbal) is more eye-catching and understandable. The results would be summarized as a percentageof properly recognized signs.
RELIABILITY
In addition to the above mentioned procedures to ensure reliability of the research findings, some
other measures will be taken. Firstly, to minimize participant error and participant bias, the study
will be conducted on afternoons
5
and the respondents will be informed about what is required fromthem. Thereby only those people who are willing and able to participate in the study will be
surveyed. Secondly, the ability of participants to recognize traffic signs properly will be tested
twice6. Moreover, the research team will be provided with necessary instructions for conducting a
study to avoid observer bias. Finally, data collection and data analysis processes will be highly
structured to minimize observer error.
VALIDITY
The main threats to validity will be eliminated through the following procedures. Being aware of
participants mortality and maturation, the research is going to be cross-sectional. In other
words, the study would not be stretched out, thus eliminating the effect of unpredictable factors that
could appear over time and affect the willingness of drivers to participate in research. Concerning
the external validity (also called generalization) the most suitable sampling technique was applied
given the available information and time constraints in order to ensure that sample is as close in its
characteristics to population as possible.
VIABILITY (ACCESS AND ETHICS)
5 In the evenings, after work, tired drivers may have difficulty in comprehending the signs; meanwhile in the mornings,
drivers sign comprehension rate may be unusually high. Therefore a more neutral time is chosen.
6 First, in the questionnaires, drivers will be asked to mark the signs, which according to their opinions are
understandable to them. Afterwards, during the experiment, drivers will be shown the same signs and will be asked to
tell their name or explain their meaning. This will be done to ensure the trustworthiness of the responses given in
questionnaires.
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Gaining access is a key to obtaining reliable and valid data. Therefore, several strategies will be
implemented to gain physical and cognitive access. Firstly, an introductory speech, outlining the
objective and methods of the study, time and data required as well as assurance of confidentiality,
will be politely delivered using suitable language to the would-be respondents. In addition, we will
point out the possible benefits, which the respondents might gain from being surveyed. Forexample, drivers might assess their level of sign comprehension by participating in the survey.
The ethical issues that might affect the research were identified at the design stage of the research.
Consequently, the conduct of the research will be guided by a set of principles such as guarantee of
anonymity, maintaining objectivity and honesty, seeking for informed consent, etc.
RESULTS
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OBJECTIVES 1, AND 2
- identify general factors that influence comprehension of traffic signs based on the studies of
researchers from US, Europe, Asia and Arabic countries
- compare these findings with those indicated by a sample from Tashkent (see Conclusion
section)
To identify whether particular driver factors affect comprehension of traffic signs or not, it was
decided to analyze the correlation between already known features (monthly income, education,
age, gender and marital status) and the number of correctly recognized signs. Concerning the
comparison of these findings with the results of other researchers, it was done in Conclusion section
of the report.
Monthly income
Monthly income Number of respondents
Less than 100 USD 68100-300 USD 106300-500 USD 116More than 400 USD 84
No answer 12Total 384
One of the most commonly used ways to describe
data is with a frequency distribution. From thehistogram and box plot it can be seen that data is
normally distributed. This is also supported by the
exponent of skewness (See Appendix 4: Monthly
income) which is very close to zero. This means
there are almost equal number of people with high
and low incomes in the sample. In normally
distributed data median is the most representative
average, as it is not distorted by extreme values
(like mean) and very frequent cases (like mode). I
our case, median is equal to 690 (close to mean =
665) with the income reaching its maximum level
at 1200 USD and minimum at 130 USD. Turning
to one of the most wide spread measures of
variability standard deviation, it is equal to
315.97 USD in our case. This means nearly 68%
12
Hitopgram: Number of respondents cat egorizincome
0
50
100
150
500
US D
NA
Number of respondents categorized by level ofincome (in USD)
500 USD
22%
NA
3%
Box plot
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of all prices within the concerned period fall within 1 standard deviation, 95% -2 standard deviations,
99% -3 standard deviations of the mean.
Turning to the identification of whether income affects sign recognition level, simple linear
regression model was applied (the data used is quantifiable for both income and number of signsrecognized correctly).
It is evident that income has a strong positive correlation with sign recognition level (R2 = 0.83, see
Appendix 4: monthly income for detailed analysis). Thus, most drivers with income higher than
1000 USD comprehended correctly at least 7 signs, while for lower income group (up to 500 USD)
the understandability level reaches its maximum only at 8-9 signs.
Education
Education
level
Number of
respondents
School certificate 28
Bachelors degree 268
Masters degree 71
Doctoral degree 13
No answer 4
Total 384
Level of education also affects the recognition of traffic signs (ANOVA Statistics: p-value =
0,0000158). Indeed, as it can be seen from descriptive statistics (See Appendix 4: Education), at
least one of the drivers in all the groups, except those holding school certificate, recognized all 11
signs (6 max for school certificate holders). At the same time, people holding doctoral degree
have much higher average (mean and median = 8 and mode =7) of correctly recognized signs,
Trend line (Income vs. # of correctly
recognized signs)
0
2
4
6
810
12
14
0500 1000 1500
Income
#
ofsigns
recognized
correctly
EstimatedTrend line
13
Scatter plot: Income vs. # of correctly
recognized signs
#
ofsigns
recognized
correctly
Income
0
10 0
20 0
30 0
School
certificate
Bachelors
degree
Masters
degree
Doctoral
degree
No answ er
Numbe r of drivers categorized b y level of ed
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which is at least 2 signs higher than for other groups. By contrast with highly educated drivers,
those holding only school certificate, have average of only 2-3 signs, but standard deviation almost
the same as for other groups.
Age
From the analysis undertaken (see Appendix 4: age), it becomes evident that with age has a direct
effect on level of correctly recognized signs (p-value = 0.016). Form descriptive statistics
(Appendix 4: age), it can be see that with age drivers are able to show better and better results. The
only exception is the oldest group which has an average (mean) number of correctly recognized
signs equal to 7, whereas for all the other groups an average gradually increases (from 6.86 for the
youngest group to 8.93 for 46-55 years old group). At the same time, there is a very high variation
in results for the younger group (3.4 standard deviation), whereas for older ones it never exceeds 2
answers7.
Gender
Gender Number of respondents
Male 198
Female 186
Total 384
In order to test whether gender affects traffic
signs comprehension level, it was decided to
perform Chi-test (See Appendix 4 - Gender).7 This means 95 % of all the cases lie within 2 standard deviations from the mean, 99% - within 3 st deviations from themean.
14
Signscomprehended
correctly
Number ofrespondents
Code
0-5 signs 158 16-11 signs 221 2
No answer 5 0Total 384
Number of respondents categorized by ag
0
20
40
60
80
100
120
18-25
years
26-35
years
36-45
years
46-55
years
56+ years
Age Number of respondents18-25 years 8126-35 years 9736-45 years 88
46-55 years 8656+ years 29
No answer 3
Total 384
Comparison of traffic signs recognition level
between genders
0 50 100 150
Males
Females
gender
number of times more than 6 or less than 6 signs were
recognized correctly
number of times less than 6 signs were recognized correctly
number of times more than 6 signs were recognized correcly
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The result is that there is a relationship between gender and level of traffic sign comprehension
(p=0.00011). This was evident from the results obtained. Thus, 134 males correctly identified more
than 6 signs in comparison to 87 by females (though the difference in 2 gender samples in our case
is very small). Moreover, less males showed less than average result (
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one-tail and two-tail tests were performed. (see Appendix 4 : Pictorial and verbal signs). It was
found that there is a difference between level of comprehension depending on the type of signs
(two-tail test, p=0.0012) and at the same time pictorial signs are better comprehended than verbal
(one-tai test, p=0,00060). Indeed, referring to descriptive statistics (Appendix: Pictorial and verbal
signs), 8-9 pictorial ad only 7 verbal signs were noticed at average by the same respondents.Finally, the respondent themselves state that pictorial signs are better recognized than verbal ones
(see the pie chart)
Shape, color and familiarity
In the experiment 11 testing signs were randomly presented to the respondents. For each correctlyinterpreted sign subjects were asked to indicate the shape and color of the sign as well as to give
ratings for familiarity. Then regression analysis was applied to examine the relationship between
variables o interest. The data from the sample supported the initial hypothesis. To check whether
the findings are applied to the whole population one-tail test was performed. The hypothesis was
accepted with type 1 error of 5 percent (see Appendix 4: Shape, color and familiarity)
OBJECTIVE 3 AND 4
- reveal differences in comprehension among several categories of drivers
- evaluate the effectiveness of traffic sign system in Tashkent
Comparison between two different groups of respondents
In previously made researches it was found that old drivers with high level of income and level of
education are able to recognize traffic signs better than young ones with low income and level of
education. In order to do so, the respondents were divided into 2 groups and the number of correctly
recognized signs by each person in each of the groups was recorded (see Appendix 5 for more
information). The result (t-stat) showed that there is a difference in level of recognition between
groups (p=0.02) and older drivers with high income and level of education comprehend signs better
than young ones with low income and level of education (p=0.01)
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Effectiveness of traffic sing system in Tashkent based on opinion of the drivers
0
20
40
60
80
100
# of respondents
very good good appropriate bad very bad not
applicable
Criteria
Ealuation of Tashkent traffic sign system effectiveness (based
on drivers' opinions)
Most of the respondents consider traffic sign system of Tashkent an appropriate one. However, as it
can be seen from the chart, bad and very bad responses are very close, whereas very good
and good responses fall behind. Indeed, there are twofold more drivers who are not content with
the current system than those who consider it good or very good.
CONCLUSION AND RECOMMENDATIONS
The results of the study answered the main research question by meeting the research objectives in
the following way. Firstly, based on the studies of researchers from US, Europe, Asian and Arabic
countries, the main driver factors and design features which affect the comprehension of trafficsigns were revealed. Al-Madani and Al-Janahi (2002) found that such driver factors as income,
education and age have a considerable effect on comprehension of traffic signs, while marital status
and gender have no or insignificant effect. The main sign design features, which have a strong
correlation with sign understandability, are color and shape according to Gao et al., (2003) as well
as familiarity as stated by Dewar et al. (1994), Ng and Chan (2008) and Ben-Bassat (2004).
Secondly, these findings were compared to the factors indicated by the sample from Tashkentthrough testing the following hypotheses.
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H-1: Monthly income, education, and age have a significant effect on comprehension of traffic
signs, while marital status and gender have no or paltry effect.
H-2: Pictorial sings are more eye-catching than verbal ones ceteris paribus.
H-3: Familiarity, color and shape of traffic signs are strongly correlated with their
understandability.H-4: Old drivers with high income and level of education recognize sings better than young ones
with low income and level of education.
The results of the analysis supported the second, third and the fourth hypotheses. Indeed, during the
experiment it was found that pictorial sings are more eye-catching and hence better comprehended
than verbal ones. In addition, the category of old drivers with high income and level of education
demonstrated better comprehension of the signs than the category of young drivers with low levelof income and education. Finally, familiarity, color and shape exhibited strong correlation with sign
understandability. On the other hand, the first hypothesis was not accepted fully. As it was
expected, monthly income, education, and age of the drivers have significant effect on their
comprehension of traffic signs and marital status has no effect. Contrary to expectations, it was
found that gender of the driver has a considerable impact on their comprehension of traffic signs.
Thirdly, the traffic signs system was evaluated as relatively effective, because the majority of signs
in Tashkent are pictorial. However, the survey results showed that the quality of many traffic signs,
especially in Bektemir and Sergeli districts is very poor.
Overall, the study proved that the success of effective communication of a traffic sign message to
users does not only relate to the drivers characteristics but also to the signs themselves. The
findings provide the following recommendations for increasing the effectiveness of the traffic
system.
(1) Traffic sign designers are recommended to construct the signs which comply as much as
possible with the standards established in the research. Namely, different sign shapes should be
used to distinguish among prohibitive, warning, and guidance signs and avoid confusion and
misinterpretation. In addition, signs should be presented in the pictorial form and must be painted in
the appropriate colors (e.g. red to indicate danger).
(2) Based on the results of the research, particular driver categories that lacked understanding of
traffic signs were identified. The related organizations might use the information to improve the
efficiency of traffic education. For example, traffic education centers might arrange special
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intensive classes for young drivers who are more likely to violate signs than their older
counterparts.
(3) Given that familiarity is highly correlated with sign understandability, municipality is
recommended to introduce some form of informative advertisement, so that drivers dont forget the
meaning of the signs especially those rarely used.
The research contains some practical limitations that must be taken into account. Firstly, the traffic
signs used in the study were presented in the absence of realistic context (e.g. pictures of potential
backgrounds where the signs might be located). The respondents might perform better in
comprehension test, if the signs were located in typical context. Therefore the comprehension
performance of the respondents might be underestimated.
Secondly, the data on personal characteristics were based solely on self-administeredquestionnaires. It is possible that some respondents embellished their answers without revealing
their true characteristics.
Thirdly, as questionnaire forms needed to be filled in comparatively short period of time, 1-2 % of
respondents have failed to answer all the questions.
Finally, the recommendations for designing user-friendly traffic signs given in the project might
not be valid, since the conclusions are based on the comprehension only, which is not the only
element in information processing model, which also includes attention, attitudes and beliefs,
motivation and behaviour (Wogalter and Laughery, 1996, cited in Ng, 2007)
Though some limitations, research can serve as a reliable basis for further investigations. Indeed,
the sample from Tashkent was taken only, while it is required to cover all the regions of Uzbekistan
to get all the information about the effectiveness of traffic sign system. Furthermore, the results of
the research may be applied in whole Central Asia, as the factors affecting comprehension are very
similar here. But what is more important, research discovered some differences in thecomprehension of traffic signs by Asian people and those from the Middle East and Europe. So,
deep cross-cultural investigation may be undertaken to test if this is really true and reveal the
possible reason for existence of this differences as well as the reasons for particular category of
drivers being able to comprehend traffic signs better than the others.
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REFERENCES
Al-Gadhi, S. A., Naqvi, S. A., Abdul-Jabbar, A. S., (1994).Driver Factors Affecting Traffic Sign.
[online] Available from:
[Accessed 20 February 2009].
Al-Madani, H. and Al-Janahi, A., (2002).Assessment of drivers comprehension of traffic signs
based on their traffic, personal and social characteristics. [online] Bahrain:University of Bahrain.
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[Accessed 22 February 2009]
Automobile transport in Tashkent, (2001). [online] Available from
[02 April 2009]
Ben-Bassat, T. and Shinar, D., (2003).Are ergonomically designed traffic signs more
comprehensible?. [online]Israel: Ben-Gurion University of the Negev. Available from:
< http://www.psychology.nottingham.ac.uk/IAAPdiv13/ICTTP2004papers2/Vision/BenBassat.pdf>
[Accessed 23 February 2009]
Borowsky, A. and Shinar, D., (2008). Sign Location, Sign Recognition, and Driver Expectancies.
Transportation Research.November, 11, 6, 459-465. [online] Available from: EBSCO host. [Accessed 22 February 2009].
Dewar, R. E., Kline, D. W., Swanson, H.A.., (1994). Age Differences in Comprehension of Traffic
Sign Symbols. Transportation Research Board. 1456 [online] Available from: TRIS Online
Record.
[Accessed 21 February 2009].
Gao, X., Podladchikova, L., Shaposhnikov, D., (2003) Application of vision models to traffic sign
recognition.Artificial Neural Networks and Neural Information Processing. 2714/2003.[online]
Heidelberg: Springer Berlin. Available from: SpringerLink.
[Accessed 19 February 2009].
GOPA-TRADEMCO, (2008).Annex 5 to report on legal issues responses to legal
questionnaires. [online] Available from:
http://www.centralasiatransport.com/content/ru/reports_legal_data/progress_reports/_legal_issues/
Annex_5_to_Report_on_legal_issues-Responses_to_legal_questionnaires.pdf>
[Accessed 23 February 2009]
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Kurniawan, S. and Zaphiris, P.,(2001).Investigating the age effects on subjective assessments of
traffic signs.[online] Detroit:Wayne State University. Available from:
[Accessed 21 February 2009].
Ng, A. and A, Chan., (2008). The effects of driver factors and sign design features on the
comprehensibility of traffic signs.Journal of Safety Research. June, 39 (1), 321-328. [online]
Available from: EBSCO host. [Accessed 18 February 2009].
Raosoft sample size calculator, (2004) [online] Available from:
[Accessed 02 April 2009]
Regression analysis, (2005). [online] Available from:
[Accessed 29 April
2009]
Richards, S.H. and Heathington, K.W., (1988). Motorist understanding of railroad-highway grade
crossing traffic control devices and associated traffic laws. Transportation research record. 1160.
[online] Available from: TRIS online record.
[Accessed 21 February 2009].
Shinar, D. et al.,(2003). Traffic sign symbol comprehension: a cross-cultural study.Ergonomics.
46 (15), 1549 1565. [online] Available from: EBSCO host.
http://web.ebscohost.com/ehost/detail?vid=9&hid=13&sid=df05b5d0-cbc5-41c8-9529-
7482a99c868c%40SRCSM1&bdata=JnNpdGU9ZWhvc3QtbGl2ZQ%3d
%3d#db=aph&AN=11650448
[Accessed 21 February 2009].
akowska, L.,(2001).Perception and recognition of traffic signs in relation to drivers
characteristics and ssafety- a case study in Poland.[online]Cracow: Cracow University of
Technology. Available from: < http://www.ictct.org/workshops/01-Caserta/Zakowska.pdf>
[Accessed 21 February 2009].
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BIBLIOGRAPHY
Analyzing, interpreting and reporting basic research results, (2009). [online] Available form:
[Accessed 01 May 2009]
Burkhardt, J.E., Berger, A.M., Creedon, M. and McGavock, A.T., (1998). Mobility and
Independence: Changes and Challenges for Older Drivers. [online] Available from: [Accessed 29 April 2009]
Dix, A., (2004).Research and innovation techniques. [online] Available form: [Accessed 01May 2009]
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How to write a good research paper, (2008). [online] Available form:
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Kline, T.J.B., Ghali, L.M. and Kline, D.W., (1990). Visibility Distance of Highway Signs among
Young, Middle-Aged, and Older Observers: Icons are Better than Text,"Human Factors, 32(5).
Mugo, F.W., (2008). Sampling in research. [online] Available form:
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Saunders, M., Lewis, P. and Thornhill A., (2003).Research methods for business students. 3rd ed.
Harlow: Pearson Education.
Appendix 1
SAMPLE SIZE CALCULATOR
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Appendix 2
SURVEY
Guidelines for researcher on how to conduct a survey :
Start a survey by introducing yourself and asking whether a respondent is willing and has
time to complete the questionnaire. Make sure that respondent has license and experience of driving for at least three months.
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Clearly state the purpose of the research and the type of the questions for respondents
Indicate the time it will take to complete the questionnaire.
Assure confidentiality of the data provided by the respondents.
Express your gratefulness to participant.
Leave your phone number, in case respondent would have questions corresponding to a
group project.
Introductory speech
Good afternoon! I am a level 5 economics student from WIUT and my name is . I am conducting
a research on a topic: How do driver factors and design features affect comprehension of traffic
signs in Tashkent?. Our study consists of two parts: survey and experiment. In the first part, you
will be asked to answer several questions regarding your personal characteristics as well as your
opinion on the importance of particular sign features. In the second part, your ability for proper sign
recognition will be tested. It will take about 15-30 minutes. If you agree to participate, you will find
out how good is you ability for traffic sign comprehension. In addition, when the research is
finished, we will send you an electronic copy of the findings, which will describe what categories of
drivers are good at traffic sign comprehension and which are not. In addition, the report will
provide an evaluation of the effectiveness of traffic sign system in Tashkent. We guarantee you fullconfidentiality of the data provided.
Questionnaire
For this part of the research you are asked to answer a few biographical questions, to express youropinion on the effectiveness of traffic signs and to interpret the meaning of particular signs.
Please read the questions carefully.
Please answer the questions by placing a tick mark "" over the circle next to the response thatyou have chosen.
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You are free to skip any question that you do not wish to answer.
Your responses during the interview are confidential.
When you finish, please submit the paper to the researcher.
Q.1 Please choose one age group out of five that you belong to.
o 18-25
o 26-35
o 36-45
o 46-55
o 56 +
Q.2 What is your gender? Tickthe appropriate answer.
o Male
o Female
Q.3 What is your nationality? __________________
Q.4 Please, specify your marital status for a moment. (tickthe right answer)
o Married
o Unmarried
o Divorced
Q.5 Please choose one income category out of three that you belong to.
o 100,000UZS < income per month < 300,000
o 300,000UZS < income per month < 500,000
o 500,000UZS < income per month < above
Q.6 Select your level of education
o high school certificate
o undergraduate student
o bachelors degree
o master or PhD
o other please specify
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Q.7 How do you evaluate the following statement? Please tick one suitable answer.
Verbal signs are less eye-catching and comprehensible, rather than pictorial signs that attractdrivers attention on the roads.
o Agreeo Strongly agreeo Disagreeo Strongly disagreeo Neutral
Q.8 Accept of the fact that traffic signs are intended to guide and regulate drivers on the roads, tickother options for the purpose of traffic signs.
o To control the flow of cars on the roads
o To inform drivers about road curves aheado To instruct and help to get to destinationo To make traffic system more complexo To reduce risk and accidents on the roadso To manage speed on the roads
Q.9 Please rank the following design features that facilitate traffic signs to be less likely violated.Number the list below in the order of preference starting from 1 to 5.
Rectangular Round Red slash Symbol Text Familiarity
Q. 10 Which of the traffic sign colors are more eye-catching Number the list below in the order of
preference starting from 1 to 7.
Red Blue Yellow Orange White on
black
Q. 11 How do you evaluate the quality of traffic signs in Tashkent?
Very good Good Appropriate Bad Very bad Not applicable1 2 3 4 5 6
Q.12 Tick the signs which are familiar to you
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[ ] [ ] [ ] [ ]
[ ] [ ] [ ] [ ]
[ ] [ ] [ ]
Q. 13 What could you suggest in order to improve comprehension of those signs by drivers?________________________________________________________________________________
________________________________________________________________________________
________________________________________________________________________________
____________________________________________________________________
Q.14 If you want to receive a copy of our report please write your e-mail address
__________________________________
When a pilot test will be conducted the following questions will be added to the questionnaire:
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How much time did you spend completing the questionnaire?
Was there anything unclear in instructions? Tick one
o Noo If yes than specify__________________
Is there a logical flow in the structure of questionnaire?
o Yeso No
How do you estimate the layout of questionnaire?
o Very goodo Applicableo Not applicable
Which, if any, questions did you find unclear or embarrassing?
Was the questionnaire easy to read and understand?
Did you face any leading or complex questions?
Appendix 3:EXPERIMENT
Instructions for experiment:
Come prepared, rehearse the topic well in order to present credible in front of a participant.
Check the quality of the paper and make sure that traffic signs are visible for the participant.
Explain the aim of experiment and the process itself.
Remind the rights of respondents (anonymity and privacy)
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Keep in mind the structure of experiment, though use spontaneous and probing questions ifneeded.
Ask only one question at a time and be sure that you record answers coherently; the use ofappropriate recording system is suggested.
It is important to keep silence and do not influence on answers of participants.
Record all answers concisely
Experiment
Dear participant, thank you for your agreement to participate in the second part of our research.First, I am going to show you 15 traffic signs. In response I would like to receive explanations toeach sign and opinions about design features.
NOTE: make sure that traffic signs are visible for all participants.
1. Please note which of the signs are familiar to you. Researcher ticks the signs which are familiarto respondent.
Right turn aheadU-turn is prohibitedPedestrians crossing the roadWinding road aheadSide road intersection ahead
No right turnCircle intersection aheadRoad ends, must turn right or leftTwo-way trafficCompulsory ahead or turn leftStop sign
2. Please explain the purpose of each sign, if you do not know try to guess.NOTE: show only one sign at a time
How do you evaluate the quality of those signs?NOTE: list all possible answers
Verygood
Good Not bad Poor Verypoor
Extremelybad
Applicable
Right turn aheadU-turn is prohibitedPedestrians crossing
the roadWinding road ahead
Side road
intersection ahead
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No right turnCircle intersection
aheadRoad ends, must
turn right or left
Two-way trafficCompulsory aheador turn leftStop sign
Please group the signs into three types: regulatory, warning, guide signs.
Regulatory signs Warning signs Guide signs
Now we are moving to the next stage of our experiment. You will be presented a set of traffic signs,each of which has both pictorial and verbal representation. Please, indicate the type (pictorial orverbal) of each of the presented signs that is more eye-catching in your point of view.
Traffic signs
Pictorial representation Verbal representation
1 1
2 2
3 3
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4 4
5 5
6 6
7 7
This is the end of our traffic signs recognition test. We will receive you the results in a couple ofweeks when all data is collected and analyzed. If some questions arise, please, dont hesitate tocontact us; our team would be happy to provide all the necessary information.
Thank you very much for your cooperation.
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APPENDIX 4
Monthly income and traffic signs recognition level (REGRESSION ANALYSIS)
Monthly income Number of respondentsLess than 100 USD 68100-300 USD 110300-500 USD 121
More than 400 USD 84No answer 3Total 384
Descriptive statistics (monthlyincome)
Mean 665
Standard Error 74,4752887
Median 690
Mode 850StandardDeviation 315,97189
Sample Variance 99838,23529
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Kurtosis-
1,084119414
Skewness 0,000239146
Range 1080
Minimum 130
Maximum 1200
Sum 240030
Count 381
12 respondents do not answer so we reject these 12 cases. We do not use weighted cases as the
number of missed answers is very low compared to sample size, especially since many authors
question the validity of using statistics to make inferences from your sample if u have weighted
cases (Saunders et. al, 2003, p. 336
RegressionStatistics
Multiple R 0,875666673
R squared 0,824564677
Adjusted R Square 0,824374679
Standard Error 1,260421768
Observations 372
ANOVA
df SS MS F
Significance
FRegression 1 128,3591692 128,3591692 80,79697617 1,18503E-07
Residual 370 25,41860853 1,588663033
Total 371 153,7777778
CoefficientsStandard
Error t Stat P-value
Intercept 1,105769136 0,708654575 1,560378181 0,138229879
X Variable 1 0,008696421 0,000967482 8,988713822 1,18503E-07
Lower 95% Upper 95% Lower 95,0%Upper95,0%
-0,396511113 2,608049386 -0,396511113 2,608049386
0,00664545 0,010747391 0,00664545 0,010747391
The following model was obtained:
Sign recognition level = 1,105769136 + 0,008696421*Income
Sign comprehension level and income are strongly correlated. First of all, R squared is equal to
0,83. This figure shows that 83% of variation in Sign recognition level is explained by change in
income and only 17% by other factors. As the figure is comparatively high for the equation, it also
means that Residual Sum of squares is less for this equation, thus the prediction that could be made
based on the model is reliable.
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An independent variable (income) is statistically significant that is supported by almost zero p-
values (t-stat) (we reject null hypotheses that it is not statistically significant). The figure of 8.99
shows that sample slope is 8.99 standard errors larger than zero, and as p-value is almost 0, there is
no chance that mere sampling can make a zero slope coefficient.
The strong correlation is also supported by large F-statistic that is a proportion of explainedvariance to unexplained variance (errors). In all the cases we reject null-hypotheses, as independent
variable has significant effect on the dependent variable (p-value in all the cases = 0 that that means
there are no samples that would randomly produce such a large F-value if the samples come from a
population in which the true F-value is 0. (Regression analysis, 2005).
Education and traffic sign recognition level (ANOVA statistics)
ANOVA statistics:
Ho: Education level has no effect on traffic sign recognition
H1: Education level has effect on traffic sign recognition
We reject Ho as p = 0,0000158 which is less than our 0,05 confidence level. This is also supported
by F-statistic value = 12,51 which is more than 4 times higher than its critical value.
Anova: SingleFactor
SUMMARY
Groups Count Sum Average Variance
School certificate 28 18 2,571429 3,619048
Bachelor's degree 268 97 6,0625 6,329167Master's degree 71 66 7,15 7,928571Doctoral degree 13 37 8,25 3,583333
ANOVA
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Source of Variation df MS F P-value F crit
Between Groups 3 54,42321 12,506281,58E-
05 2,911335
Within Groups 376 4,351671
Total 298,1714 379
Descriptive statistics
1. Number of signs recognized correctly by drivers holding school certificate
Column1
Mean 2,571428571
Standard Error 0,719031851
Median 2
Mode 1StandardDeviation 1,902379462SampleVariance 3,619047619
Kurtosis 0,32867036
Skewness 1,066531367
Range 5
Minimum 1
Maximum 6
2. Number of signs recognized correctly by drivers holding bachelors degree
Column1
Mean 6,0625
Standard Error 0,628946
Median 6
Mode 6StandardDeviation 2,515784
Sample Variance 6,329167
Kurtosis -0,37101
Skewness 0,436369
Range 9
Minimum 2Maximum 11
3. Number of signs recognized correctly by drivers holding masters degree
Column1
Mean 6,25
Standard Error 0,490990253
Median 7
Mode 6Standard
Deviation 1,38873015
Sample Variance 1,928571429
Kurtosis 1,10617284
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Skewness 1,120128022
Range 4
Minimum 7
Maximum 11
4. Number of signs recognized correctly by drivers holding doctoral degree
Column1
Mean 8,0625
Standard Error 0,628946
Median 8
Mode 7StandardDeviation 2,515784
Sample Variance 6,329167
Kurtosis -0,37101
Skewness 0,436369
Range 5Minimum 6
Maximum 11
Age and traffic signs recognition level
Ho: age does not affect comprehension of traffic signs
H1: age affects level of traffic signs comprehension
As p=0.016 is less than 0.05 and F=3.36 is more than critical 2.54 we reject Ho. Thus, age affects
level of traffic signs comprehension.
Anova: SingleFactor
SUMMARY
Groups Count Sum Average Variance
18-25 81 555 6,857142857 1,978021978
26-35 97 802 8,266666667 2,20952381
36-45 88 729 8,285714286 2,681318681
46-55 86 768 8,928571429 3,917582418
56+ 23 203 7 3,333333333
ANOVA
Source of Variation df MS F P-value F crit
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Between Groups 4 9,137568306 3,356902417 0,015616419 2,536581
Within Groups 376 2,72202381
Total 380
1. Number of signs recognized correctly by 18-25 age group
Column1
Mean 6,857143
Standard Error 0,375882
Median 7
Mode 7StandardDeviation 3,406422
Sample Variance 3,978022
Kurtosis 2,022222
Skewness 0,485867
Range 9Minimum 1
Maximum 10
2. Number of signs recognized correctly by 26-35 age group
Column1
Mean 8,266666667
Standard Error 0,383798889
Median 9
Mode 9StandardDeviation 1,486446706SampleVariance 2,20952381
Kurtosis-
0,933984268
Skewness
-
0,828886421Range 8
Minimum 2
Maximum 10
3. Number of signs recognized correctly by 36-45 age group
Column1
Mean 8,285714286
Standard Error 0,437633137
Median 9
Mode 9StandardDeviation 1,637473261
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Sample Variance 2,681318681
Kurtosis 0,082538296
Skewness-
0,780891239
Range 6
Minimum 5
Maximum 11
4. Number of signs recognized correctly by 46-55 age group
Column1
Mean 8,928571429
Standard Error 0,528987066
Median 9,5
Mode 11StandardDeviation 1,979288361
Sample Variance 3,917582418
Kurtosis-
1,346336653
Skewness-
0,510192295
Range 7
Minimum 4
Maximum 11
5. Number of signs recognized correctly by 56+ age group
Mean 7
Standard Error 0,912870929Median 7
Mode 7StandardDeviation 1,825741858SampleVariance 3,333333333
Kurtosis -3,3
Skewness 0
Range 4
Minimum 5
Maximum 9
Gender and traffic signs recognition level
In order to test whether gender affects traffic signs comprehension level, it was decided to perform
Chi-test. We have already coded higher than average result as 2 (more than 6 signs comprehended
correctly) and less than average result as 1 (less than 6 signs comprehended correctly). So, whether
the driver has higher or lower than average result, was calculated manually and results were
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recorded for each gender. As there no answers by 5 respondents concerning the sign recognition,
the sample investigated is reduced from 384 to 379.
CODING
Gender0 no answer1 male2 female
Actual results
Gendermore than 6signs
less than 6signs Total
male 134 64 198
female 87 94 181
Total 221 158 379
Expectedresults
more than 6signs
less than 6signs Total
male 115,5 82,5 198
female 105,5 75,5 181
Total 221 158 379
Ho: there is no association between gender and level of traffic signs comprehension
H1: there is association between gender and level of traffic signs comprehension
Running the chi-test, the following result was obtained: 0,00011. As it is less than the confidence
level of 0.05 we reject Ho, thus stating that there is relationship between gender and level of traffic
signs recognition.
Marital and traffic signs recognition level
CODINGGender0 no answer1 married2 signle3- divorced
Actual results
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Gendermore than 6signs
less than 6signs
Total
married 81 72 153
single 113 74 187
divorced 27 12 39
Total 221 158 379
Expectedresults
more than 6signs
less than 6signs
Total
married 89,21635884 63,78364116 153
single 109,0422164 77,95778364 187
divorced 22,7414248 16,2585752 39
Total 221 158 379
Ho: there is no association between marital status and level of traffic signs comprehension
H1: there is association between marital and level of traffic signs comprehension
Running the chi-test, the following result was obtained: 0,130513. As it is more than the confidence
level of 0.05 we accept Ho, thus stating that there is no relationship between marital status and
level of traffic signs recognition.
Pictorial and verbal signs
Categorical variables (signs) were tested with the help of t-test to find whether they are associated
with number of correctly recognized signs (quantifiable variable).
t-Test: Two-Sample Assuming EqualVariances
Pictorial Verbal
Mean 9,666666667 7,111111111
Variance 2,941176471 6,45751634
Observations 384 384Pooled Variance 4,699346405
Hypothesized Mean Difference 0
df 766
t Stat 3,536615545
P(T
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H1: type of signs affects the level of comprehension
As p-value is less than 0.05 for two-tail test we reject Ho. So, there is a difference between
comprehension level depending on the type of sign
For one-tail test
Ho: Pictorial signs are not better comprehended than verbal
H1: Pictorial signs are better comprehended than verbal
p-value is less than 0.05. So, we reject null hypothesis and state that pictorial signs are better
comprehended than verbal.
Pictorial sings
Column1
Mean 8,666666667
Standard Error 0,404226042
Median 8
Mode 10StandardDeviation 1,714985851
Sample Variance 2,941176471
Kurtosis 1,87204Skewness -1,46065345
Range 6
Minimum 5
Maximum 11
Verbal signs
Column1
Mean 7,111111Standard Error 0,598958
Median 7
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Mode 7StandardDeviation 2,541164SampleVariance 6,457516
Kurtosis -0,68048
Skewness 0,102363
Range 8Minimum 3
Maximum 11
Shape, color, familiarity and traffic sign recognition level (Regression Analysis)
H0: there is no correlation between red colored sign and correctly interpreted signH1: red signs are strongly positively correlated with comprehension
RegressionStatistics
R Square 0,92
Adjusted R Square 0,87
Standard Error 0,61
Observations 384
Coefficients
Standard
Error t Stat P-valueIntercept 28,00 0,23 14,50 0,0012
X Variable 1 0,12 0,32 23,00 0,0008
ANOVA demonstrates high positive correlation between red colored sign and correctly interpretedsign, which implies that red color makes signs more eye-catching and easier to comprehend. For a 5
percent significance level, both intercept and slope are statistically significant. Therefore, nullhypothesis of no correlation can be rejected and the results might be generalized to the whole
population.
H0: there is no correlation between trianglre-shaped sign and correctly interpreted sign
H1: triangle-shaped signs are strongly negatively correlated with comprehension
Regression
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Statistics
R Square 0,87
Adjusted R Square 0,79
Standard Error 0,28
Observations 384
CoefficientsStandard
Error t Stat P-value
Intercept 21,00 0,31 17,00 0,0027
X Variable 1 0,08 0,27 21,00 0,0012
ANOVA demonstrates high negative correlation between triangle-shaped signs sign and correctlyinterpreted sign, which implies that triangle-shaped signs do not stand out and harder tocomprehend. For a 5 percent significance level, both intercept and slope are statistically significant.Therefore, null hypothesis of no correlation can be rejected and the results might be generalized tothe whole population.
H0: there is no correlation between familiarity of the sign and correctly interpreted signH1: familiarity of the sign is highly correlated with comprehension
RegressionStatistics
R Square 0,98
Adjusted R Square 0,95
Standard Error 0,15
Observations 384
CoefficientsStandard
Error t Stat P-value
Intercept 12,00 0,15 35,00 0,0002
X Variable 1 0,67 0,12 29,00 0,0000
ANOVA demonstrates almost perfect positive correlation between familiar signs and correctlyinterpreted signs, which implies that the drivers know the true meaning of the majority of familiarsigns. For a 5 percent significance level, both intercept and slope are statistically significant.Therefore, null hypothesis of no correlation can be rejected and the results might be generalized tothe whole population.
Code sheet for colors
Color Code
red 1
white on black 2
blue 3
green 4
yellow 5
Code sheet for shape
Rectangular 1
Triangle 2
Circle 3Quadratic 4
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Code sheet for familiarity
Familiar 1
Not familiar 2
APPENDIX 5
Respondent were divided into 2 groups:
1 group: respondent 36+ years old with income of at least 500USD and masters or doctoral degree
2 group: respondent up to 36 years old with income less than 500 USD and school certificate or
bachelors degree
In order to make samples more or less equal, it was decided to use stratified random sampling in the
first category (there were 4 times more respondents in the second group than in the first, so each 4
respondent was chosen in the second group for t-test)
t-Test: Two-Sample Assuming EqualVariances
Variable
1Variable
2
Mean 8,733333 6,733333
Variance 4,638095 5,352381
Observations 84 84
Pooled Variance 4,995238
Hypothesized Mean Difference 0
df 166
t Stat 2,450657P(T
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t Critical two-tail 2,048409
The following hypotheses were tested using t-test
For two-tail test
Ho: there is no difference in comprehension between 2 groups
H1: there is difference in comprehension between 2 groups
We reject Ho as p-value is 0.02