Testing the psychophysical characteristics of professional drivers – Can we identify unsafe drivers?

Transportation Research Part F 42 (2016) 104–116

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Transportation Research Part F journal homepage: www.elsevier.com/locate/trf

Testing the psychophysical characteristics of professional drivers – Can we identify unsafe drivers? Milan Vujanic´, Boris Antic´, Dalibor Pešic´, Milan Savic´evic´ ⇑ University of Belgrade, The Faculty of Transport and Traffic Engineering, Vojvode Stepe 305, Belgrade, Serbia

a r t i c l e

i n f o

Article history: Received 4 March 2015 Received in revised form 30 June 2016 Accepted 3 July 2016 Available online 28 July 2016 Keywords: Testing Psychophysical characteristics Drivers Traffic accidents

a b s t r a c t Most experts in the field of traffic safety agree with the fact that the human factor is the most responsible for the occurrence of traffic accidents. Many authors have demonstrated the influence of psychophysical characteristics on driver’s behavior in traffic. The question arose whether the testing of driver’s psychophysical characteristics can identify unsafe drivers. The aim of this work was to conduct testing of psychophysical characteristics of professional drivers from a transport company and to compare the results of those drivers who have been in a traffic accident with those who have never been in a traffic accident. It turns out that there were no statistical differences between the test results of psychophysical characteristics of the drivers who had been in accidents versus those who hadn’t been in accidents (which means that tests of psychophysical characteristic of drivers can not accurately determine the driver’s risk of accidents). An additional objective was also to examine the effect of the driver’s age and the level of formal education in their psychophysical characteristics. It has been shown that younger drivers and drivers with high levels of education have better psychophysical characteristics in regards to driving safely. Ó 2016 Elsevier Ltd. All rights reserved.

1. Introduction Many authors who have published works on traffic safety in their papers point out that the human factor is the most responsible for crash occurrences, as well as the necessity to have only safe drivers on the roads. (Alvarez & Fierro, 2008; Connor, Whitlock, Norton, & Jackson, 2001; Reason, Manstead, Stradling, Baxter, & Campbell, 1990; Sagberg, 1999; Staubach, 2009). Thus, Taubman-Ben-Ari and Yehiel (2012) have shown a direct link between psychological characteristics and drivers’ behavior in traffic. Fairclough, Tattersall, and Houston (2006) and Cai and Lin (2011) showed that anxious drivers tend to make more mistakes in traffic, and several authors pointed out that drivers with higher levels of anger and aggression tend to drive at higher speeds, as well as show more aggressive behavior towards other commuters (Deffenbacher, Deffenbacher, Lynch, & Richards, 2003; Roidl, Frehse, & Höger, 2014; Shinar & Compton, 2004; Stephens & Groeger, 2009). On the other hand, there are also a certain number of studies which indicate that testing of psychophysical characteristics of drivers cannot clearly identify drivers who pose a high risk of crashing (Transportation research board, 2011; Vaucher et al., 2014). Since many studies have shown that certain elements of the psychophysical characteristics of the drivers affect their ability to safely participate in traffic as well as studies which clearly indicate that the testing of psychophysical characteristics of ⇑ Corresponding author. E-mail addresses: [email protected] (M. Vujanic´), [email protected] (B. Antic´), [email protected] (D. Pešic´), [email protected] (M. Savic´evic´). http://dx.doi.org/10.1016/j.trf.2016.07.003 1369-8478/Ó 2016 Elsevier Ltd. All rights reserved.

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the drivers cannot clearly identify unsafe drivers, the question remains whether it is necessary to implement the testing of the psychophysical characteristics of the drivers before they are permitted to start driving or not (i.e., can these tests identify unsafe drivers who are prone to traffic accidents). In several European countries, such as in Austria or Poland, laws regulating the field of traffic safety, have introduced the obligatory testing of the psychological abilities of drivers in certain cases, such as the case of issuing a driver’s license which was previously revoked and also in cases of the issuance of licenses to professional drivers as well as in the regard to the renewal of driving licenses for older drivers if there is an assumption that they are no longer mentally and physically capable to drive safely Chaloupka-Risser, 2005; Luczak & Tarnowski, 2014). Although southeastern European countries have no legally defined obligations for the testing of psychophysical characteristics for professional drivers, some companies independently organize testing their drivers for selecting new drivers (or in the case of testing the existing driver in order to eliminate drivers who are no longer mentally and/or physically capable of driving safely and, thus, reducing damage to the company and it’s vehicles arising from crashes). The main objective of this work was to perform testing of psychophysical characteristics of professional drivers in order to compare the results of the drivers who have been in a traffic accident with those who have never been in a traffic accident. The objective was to also determine the correlation between the drivers’ results and their crash risk. In this way, the aim was to determine whether the tests of drivers’ psychophysical characteristics can identify drivers with an increased risk of traffic accidents. For that purpose, only professional traffic accidents during the past 14 years were taken into account, because these data were only available. When it is about traffic accidents that tested drivers had with their private vehicles, according to the domestic laws, traffic police are forbidden to provide any data about driver’s names and accidents that they had so these data were not available for this research. An additional objective of this work was also to determine whether the age of drivers and their level of education affect their psychophysical characteristics relevant to the safe driving and safe participation in traffic or not. In line with these objectives, 206 drivers were tested. 2. Methods and materials There are different types of psychological tests that can determine drivers’ ability to drive and their personality profile. For purposes of testing a drivers’ psychophysical characteristics, the Vienna Test System was used. This testing system involves testing of drivers on special consoles connected to the computer (Fig. 1). The testing consisted of two sets of tests, as follows:
from the Vienna Test System HR types of tests, a group of tests, SAROAD (Safety Assessment Road), which measures the drivers’ psychophysical abilities for driving safely.
From the Vienna Test System, TRAFFIC types of tests, group of tests PERSROAD (Driver Personality Factors Road), which measures the psychological characteristics of drivers for safe participation in traffic. Within SAROAD tests group, there are five different tests for measuring the different elements of drivers’ psychological and physical characteristics necessary for driving safely, such as (Hajderpašic´, 2014; Schuhfried, 2014): 1. AMT (Adaptive Matrices Test) - This test measures the general intelligence, i.e. ability of non verbal logical reasoning. The results of this test indicate driver’s ability to analyze different traffic situations and to plan and predict his behavior while driving on the basis of those situations. 2. COG (Cognitrone) -This test is performed to estimate selective attention and concentration level through tasks to recognize the similarities between the figures. While driving, COG reflects the ability to quickly identify and distinguish the important from the unimportant details in traffic which is followed by high level of ability to react. 3. DT (Determination Test) - This test measures the reactive stress tolerance in situations requiring continuous, rapid and variable responses to visual and auditory stimuli in quick succession. When driving, DT reflects the ability and willingness to adapt to traffic conditions and overcome conflict and complex, often unpredictable situations. Good ability to react and stress tolerance allows the driver to quickly and appropriately respond to high risk, or high stress situation. 4. RT (Reaction Test) - This test measures the time it takes for a person to respond to a specific stimulus (signal), as well as the time it takes to fully implement this reaction. This gives two measured values: the average reaction speed and the average motor speed. RT indicates readiness for appropriate behavior in traffic, especially in response to unexpected driving situations i.e. in situations when driver needs to brake or maneuver to avoid an obstacle on the road, etc. 5. ATAVT (Adaptive Tachistoscopic Perception Test) - The variable that is measured by this testis obtaining an overview in traffic. The ability of quick and complete obtaining an overview in traffic allows a road user in a short period of time to see all essential elements of the traffic situation and on the basis of them to plan future course of action. Within PERSROAD tests group, there are at least three tests that measure drivers’ personality traits that are considered essential for safe participation in traffic, such as (Hajderpašic´, 2014; Schuhfried, 2014):

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Fig. 1. Vienna Test System console for testing drivers’ psychophysical characteristics (Schuhfried, 2014).

1. VIP (Driving specific Item Pool) – VIP test measures driver’s preferences to specific behaviors that are relevant for safe participation in traffic. Variables that are measured in this test are aggression and non-normal behavior. The results of the test may indicate a tendency toward risky behavior to drive and any unwillingness to adapt to traffic conditions. 2. IVPE (Inventory of Driving-related Personality Traits) – This test measures the personality dimensions that are relevant to driving and traffic behavior, and which are related to emotional stability and willingness to follow the rules. Variables that are measured are: mental stability, a sense of responsibility and self-control. The results of the test may indicate a tendency toward risky, uncontrolled behavior in traffic, lack of sense of responsibility to other road users, as well as a lack of self-control and/or the inability to predict the consequences of behavior. 3. WRBTV (Vienna Risk Taking Test) - This test measures the degree of subjectively acceptable risks in traffic situations. The main variable that is measured is readiness to take risks in traffic. Extremely low scores indicate that a person who has achieved this result, a high-risk situation would judge as neutral or slightly risk, which implies that he will not act in accordance with the seriousness of the situation, which increase the risk of traffic accidents. For this paper’s purposes, the drivers’ test results in the form of percentile scores were used. According to the recommendation of the company Schuhfried, and on the basis of the Vienna Test System software, the results are interpreted in a way that is shown in Table 1. Within SAROAD tests group, the Vienna Test System software calculates a total score called ‘‘Fit-score”, which takes values between 0 and 1000 and shows the total result achieved in the group of tests (i.e., a measure of psychophysical ability of drivers for safe driving). For example, a driver who achieves an overall score in the top 100th percentile within SAROAD test groups will have a Fit score of 1000. For PERSROAD tests group, software does not perform the calculation of the total score and for this study, in order to obtain a single measure of the personality traits of drivers that are essential for safe participation in traffic, calculations were carried out as shown in Eq. (1).

PERSROAD score ¼ MS þ SR þ SC þ RTRT  AG  NNB

ð1Þ

where MS is percentile score that was achieved in a test that measures mental stability SR is percentile score that was achieved in a test that measures sense of responsibility SC is percentile score that was achieved in a test that measures self-control RTRT is percentile score that was achieved in a test that measures readiness to take risks in traffic AG is percentile score that was achieved in a test that measures aggression NNB is percentile score that was achieved in a test that measures non-normal behavior. The values of AG and NNB are subtracted because these are the only test results for which the rule is as higher as negative. In this way calculated, the PERSROAD score may take values between 200 and 400. For the purpose of this research only professional drivers were tested (a total of 206 in all). Some description of the sample group is given in Table 2. In the Table 2 with the drivers’ age, years of driving commercial-type vehicles as well as the number of crashes, the drivers’ risk value is also presented. The risk value for each driver was calculated according to the Eq. (2).

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Table 1 Interpretation of the results in terms of percentile scores of Vienna Test System (Hajderpašic´, 2014; Schuhfried, 2014). Range

Interpretation

0–15 16–24 25–75 76–84 85–100

Below average Lower average Average Higher average Above average

Table 2 Description of the sample group.

Drivers’ age Years of driving Crashes caused by tested drivers Crashes caused by other participants Total number of crashes Risk value AAM (in km)

Minimum

Maximum

Mean

Standard deviation

23 5 0 0 0 0.0678 17458

60 34 1 4 4 1.8954 47954

41.70 20.25 0.05 0.23 0.29 0.2421 39820.26

7.115 7.133 0.218 0.557 0.604 0.2795 4621.57

Table 3 Descriptive statistics for the driver’s test results.

General intelligence Concentration Reactive stress tolerance Average motor speed Average reaction speed Obtaining an overview in traffic SAROAD Fit-score Aggression Non-normal behavior Mental stability Self-control Sense of responsibility Readiness to take risks in traffic PERSROAD score * **

Minimum

Maximum

Mean

Standard deviation

Skewness*

Kurtosis**

2 3 2 7 5 1 189 8 6 2 2 2 1 44

95 100 98 100 99 99 943 99 97 92 97 96 100 325

54.67 58.82 39.87 60.41 51.46 49.22 522.24 42.52 50.94 59.82 68.22 68.97 65.78 168.92

27.015 25.945 19.946 22.732 22.127 25.531 135.483 26.423 27.271 23.972 25.454 26.499 27.445 76.910

0.200 0.286 0.546 0.227 0.070 0.123 0.153 0.317 0.002 0.421 1.012 1.028 0.544 0.523

1.047 1.029 0.101 0.952 0.815 1.150 0.006 1.028 1.357 0.679 0.178 0.166 0.786 0.262

Standard error 0.169. Standard error 0.337.

RV ¼ CC þ TC=ðDY  AAMÞ þ 1=ðDY  AAMÞ

ð2Þ

where RV is the risk value CC is the number of ‘‘culpability crashes” (i.e. crashes which were caused by tested driver) TC is the number of total crashes1 DY is the number of years of driving commercial-type vehicles AAM is the average annual mileage in 100,000 km. 3. Results In this research, 206 drivers were tested and total results that drivers achieved in the tests are described by descriptive statistics and presented in Table 3. Out of the total number of tested drivers, 161 of them have never had a traffic accident while 45 drivers had one or more accidents. In order to compare the results of the drivers who have been in a traffic accident and those who have never been in a traffic accident, the t-test was used for independent samples which compares the mean values of the results of this group of drivers which they have achieved on each test, as well as the mean values of SAROAD Fit-score and PERSROAD score.

1

Both culpability crashes and total crashes are related only on driving commercial-type vehicles.

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Table 4 The result of the Levene’s test of equality of variances and t-test for independent samples. Drivers without TA

Levene’s statistics

t-test

Mean

Standard deviation

Mean

Standard deviation

F

Sig.

t

Sig.

Mean difference

St. error

53.72 57.68 41.12 60.68 51.97 48.87 520.40 43.03 52.12 59.05 68.92 69.73 65.09 166.63

26.336 26.423 20.268 22.879 22.458 25.348 135.275 26.180 27.376 23.928 24.998 26.588 27.372 73.464

58.51 63.41 34.85 59.32 49.39 50.63 525.80 40.46 46.17 62.90 65.41 65.93 68.54 176.15

29.626 23.672 17.958 22.378 20.880 26.528 138.176 27.619 26.643 24.200 27.357 26.242 27.904 94.310

2.447 1.987 0.497 0.007 0.255 0.044 0.015 0.108 0.453 0.135 0.720 0.116 0.116 5.197

0.119 0.160 0.482 0.935 0.614 0.834 0.904 0.743 0.502 0.713 0.397 0.734 0.734 0.024

1.016 1.269 1.811 0.343 0.667 0.395 0.144 0.556 1.252 0.921 0.787 0.821 0.719 0.539

0.311 0.206 0.072 0.732 0.505 0.694 0.886 0.579 0.212 0.358 0.432 0.412 0.473 0.592

4.791 5.736 6.268 1.362 2.579 1.761 3.405 2.567 5.950 3.854 3.501 3.800 3.446 8.516

4.714 4.521 3.461 3.975 3.867 4.464 23.706 4.619 4.752 4.185 4.446 4.628 4.795 15.800

95% confidence interval of the difference Lower bound

Lower bound

4.503 3.178 13.092 9.200 10.203 7.041 43.335 11.673 15.320 4.397 12.266 12.925 6.008 23.180

14.085 14.649 0.557 6.476 5.044 10.564 50.145 6.540 3.419 12.105 5.265 5.324 12.899 40.212

0.0050 0.0078 0.0158 0.0005 0.0021 0.0007 0.0001 0.0015 0.0076 0.0041 0.0030 0.0032 0.0025 0.0014

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General intelligence Concentration Reactive stress tolerance Average motor speed Average reaction speed Obtaining an overview in traffic SAROAD Fit-score Aggression Non-normal behavior Mental stability Self-control Sense of responsibility Readiness to take risks in traffic PERSROAD score

g2

Drivers with TA

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Table 5 Correlation between the results of the drivers achieved the SAROAD and PERSROAD tests groups and the drivers’ risk value.

SAROAD Fit-score PERSROAD score

Pearson’s correlation coefficient (R)

Sig. (1-tailed)

Coefficient of determination (R2)

0.096 0.138

0.088 0.026

0.009 0.019

With each t-test, Levene’s test of equality of variances was also conducted, which investigated whether there is equality of variances of the results of two groups of drivers (with and without accidents), while taking into account the appropriate results of the t-test. Based on the results of Levene’s test, it was shown that all the results met the assumption of equal variances (Sig. > 0.05), except in the case of PERSROAD score where Sig. < 0.05. Results of Levene’s test of equality of variances as well as the results of the t-test for independent samples are shown in Table 4. In Table 4 from the results of the t-test it can be seen that when comparing drivers with and without accidents (i.e. the mean values of the results of the two groups of drivers) in all cases Sig. (2-tailed) > 0.05 as clearly indicates that there is no significant difference between the results of drivers who have been in a traffic accident and those who have never been in a traffic accident at all. Table 4 also shows the value of the eta-square (g2), which indicates part of the variance in the dependent variable which is explained by the independent variable. Simply stated, the eta-square shows the size of the difference between the results of the group of drivers who have been in a traffic accident and the group of drivers who have been in a traffic accident. Cohen (1988) provides guidance on the interpretation of this size, so that the value of eta-square is interpreted as follows: 0.01 - weak effect 0.06 - medium impact 0.14 - high effect According to this interpretation it can be stated not only that the difference between the results of all the tests (from SAROAD and from PERSROAD tests groups) is not significant, but that the same is extremely small. The greatest difference was observed in the case of the reactive stress tolerance g2 = 0.0158 but it is also in the domain of small differences. In order to precisely determine the link between the drivers’ test results and their likelihood of being involved in a traffic accident, the correlation between these two parameters was examined. Table 5 shows the Pearson’s correlation coefficient and the Coefficient of determination which explain the link between the calculated drivers’ risk value and the results that drivers achieved in the SAROAD and PERSROAD tests groups. Cohen (1988) provides guidelines for the size of the correlation, so if R takes values between 0.10 and 0.29, it can be considered to have a weak correlation. Any values between 0.30 and 0.49 are considered to be medium correlation, while values between 0.50 and 1 are considered to be high correlations. Based on these guidelines, it can be stated that there is very weak correlation between drivers’ test results and their actual risk value. In Table 5, it can be also found the coefficient of determination (R2) that actually shows which part of the variance of one variable is explained by the variance of the second one. The relationship between the drivers’ age and overall results achieved in tests of SAROAD and PERSROAD tests groups was also examined closely in this work. Drivers’ results (in the form of SAROAD Fit-score and PERSROAD score), depending on their age are shown in Figs. 2 and 3. Table 6 shows the Pearson’s correlation coefficient and Coefficient of determination which explain the link between the age of the drivers and the results that drivers achieved in the SAROAD and PERSROAD tests groups. It can be seen in both cases (SAROAD Fit-score and PERSROAD score) that there is a negative correlation with the age of the drivers. It can be stated that there is a relatively strong correlation between the results of the SAROAD tests group and the age of the drivers, while in the case of the results of the PERSROAD tests group and the age of the drivers, there is a relatively weak negative correlation. By using the t-test for independent samples, the age of the drivers who have been in a traffic accident and those who have never been in a traffic accident were also compared. It turned out that there was no statistically significant difference between the age of the drivers who have been in a traffic accident (M = 41.76, SD = 6.23) and those who have never been in a traffic accident (M = 42.19, SD = 7.47), where t(204) = 0.346; p = 0.729. In addition, the work examined the influence of the level of education of drivers on their psychophysical abilities for safe drive and safe participation in traffic. According to the test requirements, drivers were divided into three categories: 1. drivers who have completed the third level of education (11 years of education) 2. drivers who have completed the fourth level of education (12 years of education) 3. drivers who have completed education level above the fourth (more than 12 years of education) Table 7 shows the percentage of drivers who have been in a traffic accident and those who have never been in a traffic accident in each of the three groups of drivers. The biggest part of drivers who have been in a traffic accident is in the group of drivers with 4th level of education in which 22.5% of drivers have experienced accident. In the group of drivers with an

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Fig. 2. The results of the drivers achieved in SAROAD tests group by the age of the drivers.

Fig. 3. The results of the drivers achieved in PERSROAD tests group by the age of the drivers.

Table 6 Correlation between the results of the drivers achieved the SAROAD and PERSROAD tests groups and the age of the drivers.

SAROAD Fit-score PERSROAD score

Pearson’s correlation coefficient (R)

Sig. (1-tailed)

Coefficient of determination (R2)

0.553 0.105

0.000 0.067

0.306 0.011

Table 7 Percentage of drivers who have been in a traffic accident and those who have never been in a traffic accident by education level groups. Education level

3rd 4th Above 4th

Drivers with TA

Drivers without TA

Number

Percentage (%)

Number

Percentage (%)

9 29 3

14.8 22.5 18.7

52 100 13

85.2 77.5 81.3

education level above the 4th, 18.7% of drivers have been in a traffic accident, while in the group of drivers with the 3rd level of education, 14.8% of them experienced a traffic accident. The results of these three categories of drivers were compared in the form of SAROAD Fit-score and PERSROAD score, which are shown in Table 8, and an illustration is given in Figs. 4 and 5. For SAROAD Fit-score, the widest range of the results was observed in a group of drivers with 4th level of education, and the smallest in the group of drivers above the 4th level of

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Table 8 Descriptive statistics for the driver’s results in a form of SAROAD Fit-score and PERSROAD score by education level groups. Education level

N

Mean

St. deviation

St. error

95% confidence interval of the difference Lower bound

Upper bound

Minimum

Maximum

SAROAD Fit-score

3rd 4th Above 4th All

61 129 16 206

500.48 524.65 609.44 524.08

113.852 142.423 126.604 135.523

14.577 12.540 31.651 9.442

471.32 499.84 541.98 505.46

529.63 549.46 676.90 542.69

243 163 407 163

725 922 878 922

PERSROAD score

3rd 4th Above 4th All

61 129 16 206

178.66 163.20 183.13 169.33

74.318 78.362 86.674 77.876

9.515 6.899 21.669 5.426

159.62 149.55 136.94 158.63

197.69 176.85 229.31 180.02

1 52 10 52

334 302 303 334

Fig. 4. Illustration of descriptive statistics of drivers’ results in a form of SAROAD Fit-score by education level groups.

education, in which also the highest average results in terms of SAROAD Fit-score was recorded (M = 609.44). In the case of PERSROAD score, in the group of drivers over the 4th level of education the highest average result was recorded (M = 183.13) as well as the lowest range of the results, but it also should be noted that in this group of drivers two outliers were recorded (with PERSROAD score 10 and 29). In order to determine the statistical differences between drivers’ results in a form of SAROAD Fit-score and PERSROAD score by level of education factor, a one-way analysis of variance (ANOVA) of different groups with subsequent post hoc tests was conducted. First of all, Levene’s test of homogeneity of variances was conducted in order to test the equality of variances in the drivers’ results in each of the three groups. Table 9 shows the results of Levene’s test, from which can be seen that the level of significance in both cases (SAROAD Fit-score and PERSROAD score) is greater than 0.05, which confirms the assumption of homogeneity of variance. Table 10 shows the sums of the squares of the result’s deviations of their mean values. The significance level for the calculated values indicates whether between mean values of the SAROAD Fit-score and PERSROAD score in all three groups there is a statistically significant difference. In the case of the SAROAD Fit-score, it was determined that the level of significance is 0.016 which is less than the threshold 0.05, so it can be concluded that there are statistically significant differences between the mean values of SAROAD Fit-score of the groups formed according to the driver education levels. On the other hand, in the case of PERSROAD score, it was determined that the level of significance is 0.339 which is greater than the threshold 0.05 and therefore it can be concluded that there are not statistically significant differences between the mean values of PERSROAD score of the groups formed according to the drivers’ education level. Although the differences of mean values of SAROAD Fit-score between groups proved to be significant, the impact that education has on the drivers’ results of SAROAD tests group is not huge given that g2 = 0.04, which is, according to Cohen (1988) between the weak and medium impact. When it comes to PERSROAD score, g2 = 0.01, which is certainly in the field of weak impact. Given that between the results of SAROAD Fit-score there were statistically significant differences by groups and that a greater impact that level of education has on the results of SAROAD tests group wasn’t recorded, it was assumed that there was no statistically significant difference between the mean values of SAROAD Fit-score of all groups, which was confirmed by using Tukey HSD test. The application of this test showed that there are statistically significant differences in the mean

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Fig. 5. Illustration of descriptive statistics of drivers’ results in a form of PERSROAD score by education level groups.

Table 9 Results of the Levene’s test of homogeneity of variance.

SAROAD Fit-score PERSROAD score

Levene’s statistics

df1

df2

Sig.

2.085 0.155

2 2

203 203

0.127 0.857

Table 10 Sums of squared deviations of the results of the groups formed according to the driver’s education level (SAROAD Fit-score and PERSROAD score) from their mean values. Sum of squares

df

Mean square

F

Sig.

g2

SAROAD Fit-score

Between groups Within groups All

150604.304 3614552.453 3765156.757

2 203 205

75302.152 17805.677

4.229

0.016

0.04

PERSROAD score

Between groups Within groups All

13194.929 1230074.280 1243269.209

2 203 205

6597.464 6059.479

1.089

0.339

0.01

values of SAROAD Fit-score between a group of drivers above the 4th level of education and those with 3rd level of education, as well as between group of drivers above the 4th level of education and those with 4th level of education (which is in the Table 11 marked with an asterisk). Between the results of the group of drivers with 3rd level of education and those with 4th level of education there wasn’t statistically significant difference. 4. Discussion By application of the Vienna Test System, 206 professional drivers were tested. In this way, different elements of drivers psychophysical characteristics were quantified, and these elements were divided into two groups: SAROAD – drivers’ psychophysical abilities for safe driving and PERSROAD - psychological characteristics of drivers for safe participation in traffic. It should be mentioned that the dimensions for the test sets have been selected on the basis of validation studies or legal requirements. The assignment of tests to the dimensions has been carefully carried out by experts working in both theoretical and practical fields. The criteria used in selecting suitable tests were testing time, appropriateness of the test material to the assessment issue, appropriateness of the test material to the person being tested, and appropriateness for international use (Schuhfried, 2014). SAROAD test set is made up of a combination of ability dimensions that are considered from a psychological perspective to be crucial to safe driving behavior and that have been found in a number of validation studies to be particularly relevant. Tests from the SAROAD tests group have the highest reliability coefficients (Determination Test – 0.99; Reaction Test – 0.98; Cognitrone – 0.95 etc.). Development of the PERSROAD test set has drawn heavily on recent studies that demonstrate the importance of personality characteristics for driving behavior. The personality dimensions selected for this test set are ones that have been found in scientific studies to have a significant bearing on fitness to drive (Schuhfried, 2014).

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Table 11 The results of the post hoc Tukey HSD test. (I) Level of education

SAROAD Fit-score

3rd 4th Above 4th

PERSROAD score

3rd 4th Above 4th

*

(J) Level of education

Mean difference (I  J)

St. error

Sig.

95% confidence interval Lower bound

Upper bound

4th Above 4th 3rd Above 4th 3rd 4th

24.176 108.962* 24.176 84.786* 108.962* 84.786*

20.735 37.480 20.735 35.368 37.480 35.368

0.475 0.011 0.475 0.046 0.011 0.046

73.13 197.45 24.78 168.29 20.47 1.28

24.78 20.47 73.13 1.28 197.45 168.29

4th Above 4th 3rd Above 4th 3rd 4th

15.454 4.469 15.454 19.923 4.469 19.923

12.096 21.864 12.096 20.632 21.864 20.632

0.409 0.977 0.409 0.599 0.977 0.599

13.10 56.09 44.01 68.64 47.15 28.79

44.01 47.15 13.10 28.79 56.09 68.64

The mean difference is significant at the 0.05 level.

Risser et al. (2008) investigated validity of Schuhfried’s test batteries using logistic regression analysis as well as artificial neural networks. Theoretical considerations relating to the measurement of traffic safety and global evaluations of the respondents’ driving performance in a standardized driving test as criterion measure were used. The results of their research indicate that for normal healthy respondents the predictive models of the Schuhfried’s test batteries with respect to fitness to drive are very satisfying. Anitei, Charaif, Schuhfried, and Sommer (2011) in their validation study on the sample of 352 drivers in Romania have shown that the independent variables of SAROAD tests group are predictor variables for driving performances in traffic. They have found a high and statistically significant correlation between the predictors and the criterion (r = 0.741, p < 0.05). Amado, Koyuncu, and Kaça (2015) compared three different psychotechnical test batteries used in Turkey (Act and React Test system – ART2020, TRAFIKENT, and Vienna Test System), and they have found similarity between the results of these tests, and that similarity was especially higher in tests that measure logical reasoning, visual memory and concentration (which are part of SAROAD tests group of the Vienna Test System). Many authors recommend the usage of the Vienna Test System. Anitei, Charaif, and Sandu (2014) used Adaptive Tachistoscopic Perception Test and Vienna Risk Taking Test in their study for determining gender differences in traffic risk assuming and short term memory related to traffic situations. Chraif, Anitei, and Alex (2013) as well as Vingilis et al. (2015) used Vienna Risk Taking Test for determining the effects that exposure to the different campaigns had on driver’s willingness to take risks in traffic and theirs risk-positive attitudes. Chraif et al. (2013) especially recommends the Vienna Risk Taking Test because it is based on Wilde’s theoretical model of risk homeostasis. The main goal of this paper was to compare the results of the drivers who have been in a traffic accident and those who have never been in a traffic accident (while maneuvering heavy vehicles). The assumption was that drivers who have never been in a traffic accident have better results than those drivers who have been in a traffic accident, because drivers who have good psychophysical abilities have more chances to avoid an accident in suddenly created dangers on the road. However, by analysis of the results obtained in this paper this assumption has not been demonstrated. The results of the t-test for independent samples, which compared the mean results of drivers who have been in a traffic accident and those who have never been in a traffic accident were conducted for each test and showed that between these two groups of drivers there are no statistically significant differences. Calculated values of eta-square (g2) also didn’t show any significant differences between the results of the two groups of drivers in all tests. These t-test results actually show that testing the drivers’ psychophysical characteristics cannot accurately identify drivers that could potentially be involved in a traffic accident. But to fill the gaps of such results (that some drivers were driving less than others and thus were less likely to have a traffic accident) drivers’ risk values were calculated and were then compared with the actual test results. The risk value was calculated in such a way so that the dominant part of it would more accurately reflect the number of traffic accidents for which a driver was culpable, but where the total number of traffic accidents could also be taken into account. The model for risk value calculation was set in such a way that drivers who had more years of driving and who had a greater overall annual mileage had a lower risk value as when compared to their more inexperienced counterparts. It was shown that there is very weak correlation between drivers’ test results and their actual risk value. These results indicate that there is no direct link between test results and drivers’ risk of being involved in a traffic accident (which has proven previous statements that testing a drivers’ psychophysical characteristics cannot accurately identify drivers who could potentially be involved in a traffic accidents). When analyzing these results, it should be stated that on one hand they are in accordance with several studies which have found that different factors have a major impact on drivers’ safety in traffic but drivers’ psychophysical capabilities are not among them. For example, the Transportation research board (2011) has concluded that safe driving appears to primarily reflect behavioral habits, choices and temporary states rather than drivers’ performance capabilities. Leka, Wassenhove, and Jain (2014) concluded that putting workers on pressure to perform may significantly increase safety risks. The study of Mooren, Grzebieta, Williamson, Olivier, and Friswell (2014) indicates several characteristics that have demonstrated an

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association with safety behaviors (for example, safety training, scheduling and detailed route planning, vehicle and environmental conditions etc.), but drivers’ psychophysical characteristics are not among them. Chang and Chien (2013) in their study were analyzed accident occurrence and injury severity from different aspects, and when it is about drivers’ aspect they have found that drunk driving is the major cause of truck-involved accidents and injury severity. None of these studies highlight drivers’ psychophysical characteristics as a key factor for the occurrence of traffic accidents. On the other hand, these results can be explained with the fact that professional drivers are generally part of a population that has better psychophysical preconditions than the ordinary car drivers. Professional drivers usually have some kind of pre-tests in their companies during the selection process, which means that the best ones are selected, which also could explain very small differences in the results of drivers who have been in a traffic accident and those who have never been in a traffic accident. This issue can be a subject of further researches where results of ordinary car drivers and professional drivers can be compared. In order to determine the influence of a drivers’ age on the results of testing a drivers’ psychophysical characteristics, a correlation between drivers’ age and the results in a form of SAROAD Fit-score and PERSROAD score was examined. It turns out that between the drivers’ age and SAROAD Fit-score, there is a relatively strong negative correlation (R = 0.553), while between the drivers’ age and PERSROAD score a small negative correlation (R = 0.105) was recorded. These correlation results suggest that drivers’ psychophysical abilities for safe driving are weaker by years (for example motor speed, reaction speed, concentration), while psychological characteristics of drivers for safe participation in traffic (drivers’ personality) do not change significantly. Although the results of some research like the ones by Guest, Boggess, and Dukea (2014) and Clarke, Ward, and Truman (2005) suggest that older drivers are less likely to be involved in a traffic accident than younger drivers. In this paper, in the given sample by using the t-test for independent samples, it was shown that there are not significant differences between the age of the drivers who have been in a traffic accident and those who have never been in a traffic accident. It could be explained with the fact that young drivers in general have the best physical conditions but they are the group are frequently involved in risky behavior (Evans, 1991). Risky behavior of young drivers is usually explained by a lack of experience (McKnight & McKnight, 2003; Williams, 2003), high levels of sensation seeking (Bachoo, Bhagwanjee, & Govender, 2013; Dahlen & White, 2006; Delhomme, Chaurand, & Paran, 2012; Scott-Parker, Watson, King, & Hyde, 2013), the desire to test driving skills (Rolls & Ingham, 1992), normative influence from peers (Allen & Brown, 2008; Cestac, Paran, & Delhomme, 2014), feeling of invulnerability (Chan, Wu, & Hung, 2010) or by brain maturation (Kwon, Vorobyev, Moe, Parkkola, & Hämäläinen, 2014). On the other hand older, drivers often show worse or insufficient results in the Vienna Test System but they are, as a group, not involved in accidents more than younger drivers because they have compensatory skills such as a more adequate time planning or calmness and several studies indicates that older drivers modify their driving to compensate for perceived changes in psychophysical abilities (Braitman & Williams, 2011; O’Connor, Edwards, Wadley, & Crowe, 2010). Research in the field of traffic psychology has indicated that general motives as well as the developmental stage of the person are influential factors in determining traffic behavior. Several studies have shown that, on a general level, deliberate risk-taking, violation of rules, underestimation of risks and overestimation of personal abilities are common features of young drivers, especially young males (e.g. Katila, Keskinen, & Hatakka, 1996), and that such behavior lessens with age. Hatakka, Keskinen, Gregersen, Glad, and Hernetkoski (2002) described in their GDE-framework (Goals for Driver Education) the importance of an adequate self-evaluation and drivers’ training and education. Self-evaluation is important because it provides an opportunity to give the driver a realistic picture of his or her personal strengths and weaknesses regarding different aspects of driving. Older drivers are often aware of their psychophysical impairment and that is why they self-regulate their driving more than younger drivers (Charlton et al., 2006; Devlin & McGillivray, 2016). To examine the effects that education level has on drivers’ psychophysical characteristics (i.e., on the test results in the form of SAROAD Fit-score and PERSROAD score, one-way analysis of variance (ANOVA) and post hoc Tukey HSD test were conducted). In the case of SAROAD Fit-score, it has been shown that there are significant differences between the mean values of SAROAD Fit-score by groups of drivers formed by education level. Tukey HSD test then, on one hand, showed significant differences between the results of drivers with a level of education above 4th and the results of the drivers from the other two groups. Within group of drivers with an education level above 4th the highest average SAROAD Fit-score was recorded (M = 609.44). When it comes to PERSROAD score, the impact of drivers’ education level was very weak, and between the groups of drivers there weren’t statistically significant difference. Although in the case of PERSROAD score statistically the groups do not differ significantly, it should be noted that the mean is also highest among drivers with an education level above 4th (M = 183.13). These results need to be examined with caution because the impact on these results may directly correlate with the fact that drivers with a higher level of education may better understand the tasks or that they may possibly be more familiar with these kinds of tests because they are more likely to have some familiarity with them already. 5. Conclusion The results of testing of psychophysical characteristics of professional drivers and the analysis of these results clearly indicates that between the results of drivers who have been in a traffic accident and those who have never been in a traffic

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accident there were no statistically significant differences. It was also shown that there is no direct link between drivers’ psychophysical characteristics and drivers’ risk of being involved in a traffic accident. These results suggest that a high score on tests of drivers’ psychophysical characteristics is not a guarantee that these drivers will not be involved in a traffic accident. Although these results are in accordance with different studies which suggest that several factors have a greater impact on drivers’ safety in traffic than drivers’ psychophysical capabilities do, the fact that professional drivers are usually part of a population that has better psychophysical preconditions than the ordinary car drivers should be also taken into account. After some further research, results of ordinary car drivers and professional drivers can be compared to see are there any differences between them. The results of that kind of research can help to better understanding the results of this research. Although in this study it was shown that tests of drivers’ psychophysical characteristics cannot accurately identify drivers who could potentially experience a traffic accident, it is certainly necessary to perform some form of testing and driver selection. Many authors emphasize the necessity of selecting drivers based on their psychophysical characteristics for safe driving and safe participation in traffic (Anitei et al., 2014; Bucchi, Sangiorgi, & Vignali, 2012). Chaloupka-Risser (2005) points out that some kind of selection of drivers still need to be implemented, and that any selection will be worse than one conducted by psychologists on the basis of tests of psychophysical characteristics of candidates for drivers. In this case, it is necessary to use proven tests (especially in the case of a testing of professional drivers). The results of this study show that younger drivers have better psychophysical characteristics for safe driving (which include motor speed, reaction speed, concentration level, reactive stress tolerance). However, it was shown that there are not significant differences between the age of the drivers who have been in a traffic accident and those who have never been in a traffic accident. The reasons are that younger drivers in general have better physical conditions but they are frequently involved in risky behavior (Evans, 1991), and on the other hand older drivers are often aware of their psychophysical impairment and because of that they self-regulate their driving (Charlton et al., 2006; Devlin & McGillivray, 2016). 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