Behaviours of drivers in Serbia: Non-professional versus professional drivers

Transportation Research Part F 52 (2018) 101–111

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

Behaviours of drivers in Serbia: Non-professional versus professional drivers Marko Maslac´ a,⇑, Boris Antic´ b, Krsto Lipovac b, Dalibor Pešic´ b, Nenad Milutinovic´ a a b

The Higher Education Technical School of Professional Studies Kragujevac, Kosovska 8, Kragujevac, Serbia 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 22 December 2016 Received in revised form 24 September 2017 Accepted 23 November 2017

Keywords: Non-professional drivers Professional drivers DBQ Behaviours Serbia

a b s t r a c t DBQ has been confirmed in a large number of countries and represents the basis for examining driver behaviour. However, the results of the previously conducted studies indicate differences in the behaviours between different groups of drivers. So, the results must not be generalized, but individual studies should be carried out and the comparative analysis of their results should be performed. The objective of this paper is to carry out a comparative analysis of the behaviours of non-professional and professional drivers on the basis of the undertaken individual studies. Factor analysis showed that the data best fitted into the five-factor solution with the different ranking of various factors according to the type of behaviour and with different percentages of the explanation of variance. The results showed the correlation between non-professional drivers and ordinary and aggressive violations and errors, while professional drivers were associated with positive behaviours. In addition to the correlation with specific behaviour groups, the comparative analysis of these two different driver categories also stated that the same individual characteristics of drivers appeared as predictors of different behaviour types. Ó 2017 Elsevier Ltd. All rights reserved.

1. Introduction Driver Behaviour Questionnaires – DBQ are used to examine specific driver behaviours. The DBQ has been confirmed in numerous countries and represents the basis for examining driver behaviour (de Winter & Dodou, 2010). DBQ can be a useful tool for predicting drivers’ participation in traffic accidents on the basis of their self-reported driving behaviour. This fact was confirmed in the paper of af Wåhlberg, Barraclough, and Freeman (2015), who reviewed the literature and found more than 200 studies based on the DBQ. He used the results of these studies to prove the correlation between the driver behaviour and participation in traffic accidents. The studies based on the DBQ have been conducted mostly on a general basis, while a smaller number of studies have examined the behaviours of specific groups of drivers. The first target group contains drivers of private cars. Due to the applied DBQ version and social and cultural differences between the stated countries, the authors obtained diverse results. Older versions of the DBQ showed that the structure of the questionnaire best fitted into three-factor solutions (Blockey &

⇑ Corresponding author. E-mail addresses: [email protected] (M. Maslac´), [email protected] (B. Antic´), [email protected] (K. Lipovac), [email protected] (D. Pešic´), [email protected] (N. Milutinovic´). https://doi.org/10.1016/j.trf.2017.11.020 1369-8478/Ó 2017 Elsevier Ltd. All rights reserved.

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Hartley, 1995; Reason, Manstead, Stradling, Baxter, & Campbell, 1990) with the small percentage of the explanation of the total variance (33.0%, and 27.7%). More recent and shorter versions of the DBQ (Lajunen, Parker, & Summala, 2004; Martinussen, Lajunen, Møller, & Özkan, 2013) had the structure of the questionnaire which fitted into four-factor solutions with a slightly higher percentage of the explanation of the variance (40.4% and 34.0%). In addition, the largest number of the studies examining the behaviours of the driver of private cars pointed at the impact of gender and age on the behaviour of drivers. The most significant conclusion of most studies was the connection of male drivers and young people with violations (Aberg and Rimmö, 1998; Özkan & Lajunen, 2005a, 2006; de Winter & Dodou, 2010). As opposed to the research on the behaviour of drivers of private cars, there have been a significantly smaller number of studies with the aim of determining specific risky behaviours of professional drivers (af Wåhlberg et al., 2007; Chapman, Roberts, & Underwood, 2000; Davey, Wishart, Freeman, & Watson, 2007; Dimmer & Parker, 1999; Maslac´, Antic´, Pešic´, & Milutinovic´, 2017; Sullman, Meadows, & Pajo, 2002; Wang, Li, Feng, & Peng, 2014; Xie & Parker, 2002). Depending on the vehicle used by drivers, studies showed different number of factor solutions for professional drivers. For example, the three-factor solution was reached regarding taxi drivers, light goods delivery vehicle drivers and bus drivers (Xie & Parker, 2002), the four-factor solution was reached for heavy goods vehicle drivers (Sullman et al., 2002), the five-factor solution was obtained regarding drivers who transport dangerous goods (Maslac´ et al., 2017), and six-factor solutions in the case of drivers using passenger cars at work (Dimmer & Parker, 1999). In the analyses regarding the behaviour of professional drivers, age and mobility of drivers were the most frequent predictors. Young drivers had the highest correlation with errors (Davey et al., 2007), while mobility was correlated with the number of violations (Davey et al., 2007; Sullman et al., 2002). Reviewing the studies examining the behaviour of private vehicle drivers and professional drivers, which were based on similar DBQ versions, we noticed differences in the behaviours of these two categories of drivers. The differences primarily referred to the factor structure, behaviour predictors and the response values on the behaviour scales. The studies dealing with drivers of private vehicles contained the solutions with mostly stable three-factor and four-factor structures, while it ranged from three to six factors in the case of professional drivers. The most frequent predictor of violations of private vehicle drivers were gender and age, while the predictor of violations of professional drivers was the travelled annual mileage. Also, regarding the values of particular items on the behaviour scale, the item ‘‘I exceeded the speed limit on the motorway” had the highest value in all studies of both drivers of private vehicles and professional drivers. However, the values on the scale differed significantly. In the study dealing with drivers of private vehicles conducted by Warner, Özkan, Lajunen, and Tzamaluka (2011), the value on the scale was 3.90 (0.11), while in the study of professional drivers conducted by Davey et al. (2007) it was 2.62 (0.94). The most significant differences between the drivers of private cars and professional drivers which can affect their behaviour are related to the demographic structure, driving skills, attitudes towards other drivers and time spent driving (Sullman et al., 2002). In addition, an important characteristic which might directly affect the driver behaviour is the reason for travelling. While professional drivers have only one reason (performing business duties), drivers of private vehicle have various reasons for travelling. Having in mind the stated differences in the results of the conducted studies, as well as the numerous factors affecting these results, we should not generalize the results and apply them to all drivers. It is requisite to conduct studies of separate driver categories and compare the results. The results of the studies greatly depend on the applied version of the DBQ and the area where the research is conducted. Thus, this paper deals with the research of the behaviour of two groups of drivers (non-professional drivers versus professional drivers) applying the same DBQ version and in the same area (the territory of Serbia). According to the official statistical data, 1011 drivers of private cars and 89 professional drivers lost their lives in the Republic of Serbia in the period 2010–2015. Over the stated period, these two driver categories had the share of 28.72% in the total number of fatalities. The number of traffic accidents in that time period involving drivers of private cars amounted to 171,664, while the number of traffic accidents including professional drivers was 52,077 (Road Traffic Safety Agency, 2016). Since traffic accidents represent a great loss for such a small country, the objective of this paper is: – to conduct a comparative analysis of behaviours of non-professional drivers and professional drivers on the basis of the undertaken separate studies, – to determine individual driver characteristics as the predictors of their behaviour, – to define which types of driver behaviours are related to the participation in traffic accidents in the previous period. In addition to the above mentioned, the aim of the study is also to examine the frequency of mobile phone use while driving by drivers. This can be achieved by adding two items to the applied DBQ version. Namely, previous studies showed that drivers who used mobile phone while driving had a higher risk of participating in traffic accidents than the drivers who did not use mobile phones while driving (Collet, Guillot, & Petit, 2010; McCartt, Hellinga, & Braitman, 2006). Also, McEvoy et al. (2005) determined that the use of hands-free devices increased the risk of participating in traffic accidents by 3.8 times, while the classic use of mobile phones while driving increased the risk by 4.9 times. In 2016, in the Republic of Serbia, the use of mobile phone while driving by drivers amounted to 9.9% (outside of a residential area) and 11.7% (in a residential area) (Road Traffic Safety Agency, 2016).

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2. Material and methods 2.1. Materials The DBQ for non-professional drivers and professional drivers conducted in Serbia is based on the conceptual framework of Reason et al. (1990), as well as on the more recent versions of the DBQ which include ordinary and aggressive violations (Lawton, Parker, Stradling, & Manstead, 1997b), errors (Guého, Granié, & Abric, 2014), lapses (Lajunen and Summala, 2003) and positive behaviours (Özkan & Lajunen, 2005a). The DBQ contained the items which had the highest factor loadings in the previous research (Table 1). The items were translated from the English language into Serbian by a professional translator. Apart from these items, the items regarding mobile phone use while driving were included. In Serbia, the Law on Road Traffic Safety, Article 28 (paragraph 1) states: ‘‘The driver must not use the telephone or other communication devices if he does not possess or does not use the equipment that enables making phone calls without engaging a hand while driving”. With this in mind, the items that are used in the questionnaire, and are related to the use of mobile phones while driving, are classified as ordinary violations. The items refer to mobile phone communication and reading the contents (messages, the Internet) while driving. The questionnaire consisted of two parts. The first part contained items which included socio-demographic characteristics (age), as well as the items related to the years of driving experience, the mobility of participants (the travelled annual mileage) and participation in traffic accidents in the past five years. All the items were closed-type and participants circled one of the given answers. The second part of the questionnaire was related to the driver’s behaviour in traffic, and it consisted of twenty-five items shown in Table 1, which were divided into five groups (ordinary violations, aggressive violations, errors, lapses and positive behaviours). The items were randomly ordered. The first group contained the items about ordinary violations in traffic. An ordinary violation was defined as an intentional deviation from the legal rules (Reason et al., 1990, e.g. ‘‘I overtake slow vehicles on the right side”). An aggressive violation was defined as conflicting behaviour towards other road users, and as such represents a kind of aggressive traffic violations (Lawton, Parker, Stradling, & Manstead, 1997b, e.g. ‘‘I use sound signals in order to show my anger”). Errors were defined as making decisions that put the driver in danger, without disobeying the

Table 1 Items of the DBQ (divided in groups) for drivers, means and standard deviations. Non-professional

Professional

M

S.D.

M

S.D.

Ordinary violation 1. I exceeded the speed limit 2. Driving after drinking 3. I overtake a slow vehicle on the right side 4. I pass through an intersection although I know that the traffic light is red 5. I use a mobile phone while driving – I talk over the phone 6. I use a mobile phone while driving – I read the contents (text messages, the Internet)

2.39 1.55 1.58 1.46 2.21 2.46

1.48 0.94 1.01 0.93 1.50 1.51

1.98 1.25 1.28 1.26 2.45 1.59

0.95 0.55 0.54 0.60 1.38 0.86

Aggressive violation 7. I change the lane in the last minute 8. I intentionally drive slowly in order to annoy the drivers behind me 9. I use sound signals (the horn) in order to show my anger 10. I use high beam highlights in order to distract the oncoming vehicle

2.01 2.27 2.44 2.36

1.27 1.67 1.55 1.39

2.57 2.07 1.48 2.13

1.75 1.71 0.98 1.25

Error 11. 12.

1.63 1.88

0.93 1.01

1.61 1.59

0.91 0.99

13. 14. 15.

I did not look in the rear mirror while changing the lane I miscalculated the duration of the green light on the traffic lights and I could not stop the vehicle safely I miscalculated the speed of the oncoming vehicle (while overtaking or turning left) I missed the exit from the highway because I was not able to change the lane Forgetting to release the handbrake before pulling out

1.67 1.84 1.68

0.91 1.03 1.01

1.65 1.75 1.59

0.79 0.96 0.86

Lapse 16. 17. 18. 19. 20.

I I I I I

did not notice the traffic sign on the roadside because I was lost in thought misinterpreted the traffic signs so I chose the wrong road drive towards a specific destination, or after some time I realize that I am a mistake road turned on the wrong device of the vehicle (instead of a wiper I turned on the turn signal) did not notice a pedestrian on the pedestrian crossing

1.84 1.68 1.62 1.93 1.81

0.98 0.93 0.98 1.14 0.91

1.89 1.75 1.80 1.95 1.95

0.97 0.93 0.77 0.94 0.93

Positive 21. 22. 23. 24. 25.

behaviour I give priority to pedestrians even though I have the priority I keep a necessary distance behind a vehicle in order not to disturb the driver in front of me I avoid using the fast lane in order not to slow down the traffic flow I adapt my speed in order to help a driver to overtake me Take care when parking not to disturb other vehicles and other road users

3.46 3.99 4.02 4.72 4.98

1.84 1.64 1.69 1.48 1.44

4.35 4.79 4.57 5.46 4.56

1.44 1.31 1.51 0.93 1.84

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legal rules (Parker, Reason, Manstead, & Stradling, 1995b, e.g. ‘‘I did not look in the rear mirror while changing the lane”). Lapses were defined as ill-suited behaviours related to the lack of concentration on the task (Parker et al., 1995b, e.g. ‘‘I turned on the wrong device of the vehicle (instead of a wiper I turned on the turn signal)”). Positive behaviours were defined as behaviours that appeased social interactions (Özkan & Lajunen, 2005a, e.g. ‘‘I avoid the use of the fast lane in order not to slow down the traffic flow”). In this part we used the Likert scale with answers ranging from 1 to 6, 1 being ‘‘Never” and 6 ‘‘Very often”. 2.2. Participants 918 non-professional drivers and 504 professional drivers filled in the questionnaire. Non-professional drivers are drivers who use the vehicle for private purposes and who possess a driving licence for B category vehicles (motor vehicles whose greatest permitted mass is no greater than 3500 kg, with no more than eight seats other than the driver’s seat). Professional drivers are defined as motor vehicle drivers whose main profession is driving a vehicle and who possess the Certificate of Professional Competence – CPC according to the Directive EC/2003/59. The research included 918 non-professional drivers. The interviewees were aged 19–63 (M = 35.12, SD = 10.15), with the years of driving experience between 1 and 45 years (M = 14.16, SD = 9.11). The drivers reported that their travelled annual mileage most frequently ranged up to 5000 km and 5000–10,000 km (37.8% and 34.9%, respectively). As for the participation in traffic accidents in the previous five years, 83% of the drivers were not involved in a traffic accident. 504 professional drivers participated in the research. The total sample of 504 professional drivers consisted of heavy goods vehicle drivers (396), light goods delivery vehicle drivers (43) and bus drivers (65). They were aged 21–64 (M = 40.11, SD = 12.15), with the years of driving experience between 3 and 44 years (M = 17.15, SD = 10.11). The drivers reported that their travelled annual mileage most frequently ranged up to 30,000–50,000 km and 10,000–30,000 km (33.9% and 29.1%, respectively). When it comes to participating in traffic accidents in the previous five years, 86.6% of the drivers were not involved in traffic accidents. 2.3. Procedure The period of data collection lasted for four months. The questionnaires for non-professional drivers were collected in two manners. The first manner was an online questionnaire, sent to the drivers via social networks. The total number of 625 copies of the questionnaire was sent, 511 copies were returned, which represents the rate of 81.7% of the successfully completed questionnaires. The other manner was polling the drivers on the field in several towns in Serbia. The data were collected by trained poll-takers (students of The Higher Education Technical School of Professional Studies Kragujevac). In this manner, 407 of successfully filled questionnaires were collected. The questionnaires for professional drivers were sent to the addresses of 35 transport companies. 22 companies accepted to participate in the research. Following their acceptance, the trained poll-takers were sent to the companies to conduct the survey. All professional drivers who were at their workplace at the moment were surveyed. Professional drivers filled in the questionnaires independently. 2.4. Study limitations This study, as well as the other studies based on self-reported behaviours, has the limitation in terms of providing socially desirable responses. Thus, in order to check the consistency of responses, the study included the control item ‘‘I drive at a speed higher than the speed limit”. This item has the same meaning as the item ‘‘I disregarded (exceeded) the speed limit”. The correlation between the stated items was determined using the Pearson’s r correlation (for non-professional drivers – 0.898; for professional drivers – 0.799), which means that the consistency of the respondents’ responses was good. The item (‘‘I drive at a speed higher than the speed limit”) was excluded from further processing of the results. 2.5. Analysis strategy The data were analyzed in the statistical software package IBM SPSS v. 22. The normality of distribution was tested regarding the data and the items on the scales. In both cases, data distribution largely deviated from the normal distribution, so nonparametric methods were used in the following analyses. The internal consistency of the questionnaire was assessed using Cronbach’s alpha statistic. Principal Component Analysis (PCA) using the Kaiser’s criterion for factor extraction and the orthogonal Varimax rotation method were performed to investigate the underlying structure of the questionnaire and showed the association of different driver behaviours according to groups. The application of multiple linear regression analyses identified some predictors of driver behaviour, and the application of binary logistic regression determined the association of behaviour with the participation in traffic accidents in the previous period. Prediction models were formed on the basis of these tests. The threshold of the statistical significance was set to the conventional level of p  .05.

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3. Results 3.1. Factor analysis The statistical norms were tested by the author. Internal consistency was calculated for all the items, as well as for the scales or behaviour groups. The results are presented in Table 2. All groups had excellent internal consistency except ordinary violations of non-professional drivers and aggressive violations of professional drivers. The analysis of the basic components using Varimax rotation method was conducted for all the items in both questionnaires. The Kaiser–Meyer–Olkin measure of sampling adequacy was satisfactory (0.811/0.809) and Bartlett’s test of sphericity was significant (0.0001/0.0001). The criterion for assigning items to factors was the biggest factor loading. The factor loadings >0.33 were taken into account. The data best fitted into a five-factor solution, with the percentage of the explanation of the variance of 50.5% for non-professional drivers and 44.8% for professional drivers. The analysis of the basic components confirmed the five-factor solution, as presented in Table 3. Factor loadings corresponded very well to all the items in the questionnaire. Factor 1 was represented by errors in both cases. Errors were defined by five items. Factor 1 explained 15.1% of the variance for non-professional drivers, while for professional drivers it explained 11.4% of the variance. Factor 2 was represented by ordinary violations with 9.7% of the explanation of the variance for non-professional drivers. Ordinary violations were defined by six items. Factor 2 was represented by positive behaviours for professional drivers with 9.4% of the explanation of the variance. Positive behaviours were defined by five items. For non-professional drivers factor 3 was represented by positive behaviours with 9.1% of the explanation of the variance, while for professional drivers factor 3 was represented by ordinary violations with 8.5% of the variance explained. Factor 4 was represented by aggressive violations in both cases. Aggressive violations were defined by four items. For non-professional drivers factor 4 explained 8.9% of the variance, while for professional drivers it explained 8.3% of the variance. Factor 5 was represented by lapses in both cases. Lapses were defined by five items. For non-professional drivers factor 5 explained 7.7% of the variance, while for professional drivers it explained 7.2% of the variance. Total results for errors, ordinary violations, positive behaviours, lapses and aggressive violations have been calculated and are used as composite scales in the following analyses. 3.2. Correlations between the obtained behaviour types and drivers Multiple correlations of coefficients were conducted in the study in order to examine the correlation between the different types of behaviours obtained by analyzing the basic components. Table 4 provides the results of the correlation between the five types of behaviours using Pearson’s r correlation. When it comes to the behaviours obtained in the research on non-professional drivers, errors were positively associated with ordinary and aggressive violations and lapses, while negatively correlated with positive behaviours. Ordinary violations were positively associated with aggressive violations and lapses, while negatively associated with positive behaviours. Positive behaviours were negatively associated with lapses, and they were not in correlation with aggressive behaviours. Aggressive behaviours were positively correlated with lapses. As for professional drivers, errors were positively associated with ordinary and aggressive violations and lapses, while they were not in correlation with positive behaviours. Ordinary violations were positively associated with aggressive violations, and negatively correlated with positive behaviours and lapses. Positive behaviours were positively associated with lapses, while they were not in correlation with aggressive violations. Aggressive violations were not in correlation with lapses. However, the correlations were weak. The strongest correlation was noticed between errors and lapses in both cases. These results confirm the result of the analysis of basic components stating that five behaviour types can be observed as separate scales. In addition to the correlations between the obtained behaviour types in Table 4, the correlation of non-professional drivers and professional drivers with different behaviour types is presented. Following the formation of the unique basis and addition of a new variable (1-non-professional drivers, 2-professional drivers), a correlation was obtained which showed that non-professional drivers were related to ordinary and aggressive violations and errors, while professional drivers were associated with positive behaviours. Lapses were correlated neither with amateur drivers nor with professional drivers. Table 2 The results of the Cronbach’s alpha test. Cronbach’s alpha

Non-professional drivers

Professional drivers

All Items Ordinary violations Aggressive violations Errors Lapses Positive behaviours

0.713 0.680 0.794 0.717 0.727 0.779

0.702 0.711 0.684 0.784 0.701 0.750

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Table 3 Principal axis factor analysis of the 25 items of the scale (Rotated Component Matrix). Varimax rotation Component (factor) Factor 1 N-p./Pro. 11. 12. 13. 14. 15. 1. 2. 3. 4. 5. 6. 21. 22. 23. 24. 25. 7. 8. 9. 10. 16. 17. 18. 19. 20.

Factor 2 N-p./Pro.

I did not look in the rear mirror while changing the lane I miscalculated the duration of the green light on the traffic lights and I could not stop the vehicle safely I miscalculated the speed of the oncoming vehicle (while overtaking or turning left) I missed the exit from the highway because I was not able to change the lane Forgetting to release the handbrake before pulling out I exceeded the speed limit Driving after drinking I overtake a slow vehicle on the right side I pass through an intersection although I know that the traffic light is red I use a mobile phone while driving – I talk over the phone I use a mobile phone while driving – I read the contents (text messages, the Internet) I give priority to pedestrians even though I have the priority I keep a necessary distance behind a vehicle in order not to disturb the driver in front of me I avoid using the fast lane in order not to slow down the traffic flow I adapt my speed in order to help a driver to overtake me Take care when parking not to disturb other vehicles and other road users I change the lane in the last minute I intentionally drive slowly in order to annoy the drivers behind me I use sound signals (the horn) in order to show my anger I use high beam highlights in order to distract the oncoming vehicle I did not notice the traffic sign on the roadside because I was lost in thought I misinterpreted the traffic signs so I chose the wrong road I drive towards a specific destination, or after some time I realize that I am a mistake road I turned on the wrong device of the vehicle (instead of a wiper I turned on the turn signal) I did not notice a pedestrian on the pedestrian crossing

0.490/0.612 0.456/0.557

Percent of explained variance in Rotated Sums of Squared Loadings

15.1/11.4

Factor 3 N-p./Pro.

Factor 4 N-p./Pro.

0.597/0.790

Factor 5 N-p./Pro.

/0.380

0.674/0.380 0.600/0.708 0.434/ 0.515/ 0.334/ 0.399/

/0.652 /0.652 /0.531 /0.430

0.729/

/0.583

0.757/

/0.589

/0.546

0.460/

/0.693

0.689/

/0.690

0.664/

/0.601 /0.381

0.758/ 0.697/

0.388/ /0.452

/ 0.385

0.604/0.501 0.842/0.682

0.565/

0.435/

0.715/0.602 0.869/0.794 0.648/0.375 /0.364

0.657/0.384

/0.350

0.696/0.459 0.360/0.476

0.331/

0.507/0.467 9.7/9.4

9.1/8.5

8.9/8.3

7.7/7.2

*

N-p. = Non-professional drivers; Pro. = Professional drivers.

Table 4 The correlation of coefficients on the DBQ scale containing 25 items and the association with driver categories (non-professional drivers (N-p.)/professional drivers (Pro.)). 1 N-p./Pro. Errors Ordinary violations Positive behaviours Aggressive violations Lapses Driver categories (N-p./Pro.) * **

p < .05. p < .01.

– 0.496**/0.133** 0.148**/0.071 0.245**/0.090* 0.591**/0.397** 0.074**

2 N-p./Pro.

3 N-p./Pro.

4 N-p./Pro.

5 N-p./Pro.

– 0.192**/0.049

–

6 N-p./Pro.

– 0.233*/ 0.206** 0.214**/0.181** 0.387*/ 0.171** 0.183*

– 0.036/0.085 0.123**/0.218* 0.224**

0.097**

0.041

–

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3.3. Correlation of the variables with the behaviours on the DBQ scale In order to compare the results of the DBQ applied in Serbia with the previous DBQ versions, the following part of the study analyzed the correlation of independent variables (individual characteristics of drivers) with behaviour types obtained as separate behaviour scales in the previous part of the study. The following independent variables were used: age, years of driving experience and travelled annual mileage. On the basis of the conducted single linear regression analysis, the standardized regression coefficient b and significance levels p applied in the multiple linear regression analyses were obtained. The multiple linear regression analyses showed the extent to which the mentioned independent variables appeared as predictors of the five different behaviour types. The results are presented in Table 5. In relation to non-professional drivers, the multiple regression model showed that the predictors of errors and positive behaviours were age, years of driving experience and travelled annual mileage. These three variables explained 5.3% of the variance in errors, and 8.5% in positive behaviours. Age and travelled annual mileage appeared as the predictors of ordinary violations, with 6.2% of the explanation of the variance. The predictors of aggressive violations and lapses were age and years of driving experience. These two variables explained 4% of the variance in aggressive violations and 3.2% in lapses. As for professional drivers, the predictors of errors, positive behaviours and lapses were age, years of driving experience and travelled annual mileage. These three variables explained 3.3% of the variance in errors, 5.9% in positive behaviours and 13.4% in lapses. Years of driving experience and travelled annual mileage appeared as predictors of ordinary violations, with 9.9% of the explanation of the variance. Years of driving experience also appeared as the predictor of aggressive violations with 2.2% of the explanation of the variance. 3.4. Correlation of the behaviours on the DBQ scale with participation in traffic accidents The correlation of the variables with participation in traffic accidents was conducted applying binary logistic regression analysis. Participation in traffic accidents had the Poisson distribution, and the number of accidents was converted into dichotomous variables (where 0 = without accidents in the last five years and 1 = at least one traffic accident in the last five years). Independent variables, i.e. accident predictors, were age, years of driving experience and travelled annual mileage, as well as the obtained behaviour groups in the analysis of the basic components (error, ordinary violation, positive behaviour, aggressive violation and lapses). For non-professional drivers, the full model was significantly reliable in the Omnibus tests of Model Coefficients (chi-s quare = 43.308, df = 8, p < .0001), as well as in The Hosmer–Lemeshow test (chi-square = 2.514, df = 8, p = .921). The model showed 83.1% of the successful predicting of the correlation between the predictors and participation in traffic accidents. The model showed the dependence of age (OR = 0.820), travelled annual mileage (OR = 1.344), errors (OR = 1.562), ordinary violations (OR = 1.250), aggressive violations (OR = 1.401) and lapses (OR = 0.705) on participation in traffic accidents. For professional drivers, the full model was significantly reliable in the Omnibus tests of Model Coefficients (chi-square = 45.238, df = 8, p < .0001), as well as in The Hosmer–Lemeshow test (chi-square = 7.420, df = 8, p = .310). The model showed 86.6% of the successful predicting of the correlation between the predictors and participation in traffic accidents. The model showed the dependence of age (OR = 0.735), years of driving experience (OR = 0.912), positive behaviours (OR = 0.865) and lapses (OR = 1.389) on participation in traffic accidents. The detected differences in the applied models are reflected in the number of predictors related to the participation in traffic accidents. There are six predictors regarding non-professional drivers and four predictors regarding professional drivers. The same eight predictors were tested within each model.

Table 5 Multiple linear regression analyses for five behaviour types and independent variables as predictors.

* **

Error

Ordinary violations

Positive behaviours

Aggressive violations

Lapses

Non-professional drivers Age Years of driving experience Travelled annual mileage R2 Adjusted R2

0.163* 0.102** 0.148** 0.055 0.053**

0.188* 0.020 0.284** 0.064 0.062**

0.169* 0.086* 0.185** 0.087 0.085**

0.189** 0.252** 0.006 0.042 0.040**

0.121* 0.177** 0.035 0.034 0.032**

Professional drivers Age Years of driving experience Travelled annual mileage R2 Adjusted R2

0.168** 0.132* 0.154** 0.039 0.033**

0.021 0.161* 0.144* 0.104 0.099**

0.145** 0.283** 0.145* 0.065 0.059**

0.011 0.214** 0.125 0.044 0.022**

0.222** 0.178** 0.028* 0.139 0.134**

p < .05. p < .01.

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4. Discussion The DBQ was validated for drivers in Serbia. Two separate studies were conducted. The first study analyzed the behaviour of non-professional drivers, while the second dealt with the behaviours of professional drivers. The results showed that the data best fitted into the five-factor solution, which explains 50.5% of the variance regarding non-professional drivers and 44.8% concerning professional drivers. Factor analysis confirmed the five axes (groups): errors, ordinary violations, positive behaviours, aggressive violations and lapses. The percentages of the explanation of the variance for both studies are in accordance with the values of the previous studies which have applied similar versions of the DBQ. As opposed to most studies observing only risky driver behaviours, the study also included behaviour types which define the positive interaction with other participants in traffic. Özkan and Lajunen (2005a) suggested this behaviour type. In this way we can obtain an extensive impression of driver behaviour in traffic. The items which were added to this DBQ version had high values and confirmed the hypothesis that drivers in Serbia used mobile phone while driving to a great extent. Namely, in the case of non-professional drivers the item ‘‘I use a mobile phone while driving – I read the contents (text messages, the Internet)” had the highest value on the scale of ordinary violations, while the item ‘‘I use a mobile phone while driving – I talk over the phone” had the highest value for professional drivers. The adequacy of using these items in the following DBQ versions was thus shown, which had not been the case previously. In addition, these items were now the most frequently self-reported ordinary violations. In most of the previous studies, both non-professional and professional drivers had reported exceeding the speed limit as the most frequent behaviour in the field of ordinary violations (Özkan & Lajunen, 2005a; Davey et al., 2007; de Winter & Dodou, 2010; Guého et al., 2014; Martinussen et al., 2013; Maslac´ et al., 2017; Sullman et al., 2002; Xie & Parker, 2002). The comparative analysis of the behaviours of non-professional drivers and professional drivers determined that nonprofessional drivers were correlated with all types of aberrant behaviour, i.e. they were correlated with errors, ordinary and aggressive violations, while professional drivers were correlated with positive behaviours. Lapses were not associated with these driver categories. Within errors, ordinary and aggressive violations, the response values of individual items were higher for 13 out of 15 items for non-professional drivers than for professional drivers. This result shows that professional drivers make fewer violations and errors than non-professional drivers. This can be accounted for the fact that professional drivers are more noticeable on the road while showing these behaviours due to the specific characteristics of their vehicles (mass and dimensions), and they have a smaller space for manoeuvring so they perform risky behaviours less frequently. Furthermore, the consequences for the sanctioned violations are not the same for non-professional and professional drivers. For professional drivers, the consequences are significantly more serious since the loss of a licence can result in losing the job and compromising the existence. The paper examined the correlation between different behaviour types of both categories of drivers, obtained by analyzing the basic components with several independent variables. The following predictors of specific behaviour types were used: age, years of driving experience and travelled annual mileage. Age appears as the predictor of all five mentioned types of behaviour regarding non-professional drivers, while in the case of professional drivers it appears as the predictor of three behaviour types. The obtained results for non-professional drivers are in accordance with the previously conducted studies which show that younger drivers make more violations than older drivers (Aberg and Rimmö, 1998; Blockey & Hartley, 1995; Özkan & Lajunen, 2005a, 2006; de Winter & Dodou, 2010; Parker, McDonald, Rabbitt, & Sutcliffe, 2000; Parker, Reason, Manstead, & Stradling, 1995a). It is particularly interesting that older drivers are more inclined to make errors and lapses. This result is different from the study (Guého et al., 2014) carried out in France, where several types of errors were analyzed (lapses were defined within these errors), and where most error types were related to young drivers. The results related to professional drivers indicate that older driver make more lapses than younger drivers, but they make fewer errors, which was also confirmed in previous research (Davey et al., 2007; Xie & Parker, 2002). Violations and aggressive behaviours are not correlated with the age of professional drivers. Xie and Parker (2002) believed that older professional drivers showed more responsibility and more patience when compared to younger drivers, which is directly related to the higher rate of expressing positive behaviours towards other traffic participants. Years of driving experience of non-professional drivers are the predictor of errors, positive behaviours, lapses and aggressive violations. Years of driving experience are not associated with ordinary violations. Drivers with shorter driving experience are prone to making greater number of errors than drivers with longer driving experience. This result can be justified by the fact that longer driving experience increases the driver’s self-confidence, which reduces the number of errors on the one hand and increases the number of lapses on the other hand. Regarding professional drivers, years of driving experience are positively associated with lapses, positive behaviours and ordinary violations. On the contrary, years of driving experience are negatively associated with errors and aggressive violations. Drivers with shorter driving experience are prone to making greater number of errors than drivers with longer driving experience. The results obtained in Serbia, concerning the relationship between driving experience and risky behaviours, confirm the research on professional drivers carried out in Australia (Davey et al., 2007), China (Xie & Parker, 2002) and New Zealand (Sullman et al., 2002). Travelled annual mileage as the predictor of non-professional driver behaviours is associated with errors, ordinary violations and positive behaviours. The aberrant behaviours of drivers grow with the travelled annual mileage in all studies

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which include risky behaviours of non-professional drivers. As for professional drivers, travelled annual mileage is positively associated with errors, ordinary violations and lapses, while negatively correlated with positive behaviours. Longer annual mileage results in the higher risk of participating in traffic accidents, as confirmed in numerous studies (Davey et al., 2007; Lawton, Parker, Stradling, & Manstead, 1997a; Parker et al., 2000; Stradling, Parker, Lajunen, Meadows, & Xie, 1998). The tested prediction model for non-professional drivers showed the dependence of age, travelled annual mileage, errors, ordinary violations, aggressive violations and lapses on participation in traffic accidents. The model showed that non-professional drivers had lower chances of participating in traffic accidents by 18% as the age rose, while the longer travelled annual mileage resulted in higher chances of participating in traffic accidents by 34.4%. So, older drivers and drivers who travel smaller annual mileage have lower chances of participating in traffic accidents than younger drivers and drivers who travel longer annual mileage. This result regarding the dependence of age and participation in traffic accidents is in accordance with the study of Guého et al. (2014), while it differs from the findings of Özkan and Lajunen (2005a) who found out that the rise of age increased the chance of participating in traffic accidents. Travelled annual mileage, as the predictor in this model, is in concordance with previous research. When it comes to the changes of response values on the behaviour scales related to errors, ordinary and aggressive violations, the change by one unit increases the chances of participating in traffic accidents (56.2%, 25% and 40.1%, respectively). The correlation of errors and traffic accidents was also confirmed by de Winter and Dodou (2010), Parker et al. (2000) and Guého et al. (2014). On the other hand, the decrease by one unit in the response values on the scale regarding lapses increases the chances of participating in traffic accidents by 29.5%. As for professional drivers, participation in traffic accidents was correlated with age, years of driving experience, positive behaviours and lapses. The model showed that the increase of professional drivers’ age decreased the chance of participating in traffic accidents by 26.5%, while the rise of years of driving experience lowered the chance of participating in traffic accidents by 8.8%.These results are in accordance with the study of Sullman et al. (2002), while they differ from the study of Davey et al. (2007), which did not determine the correlation between participation in traffic accidents and these two predictors. However, the reason for not determining the correlation can be found in the fact that this study observed an extremely short time period (12 months) of participating in traffic accidents. It is interesting that, contrary to non-professional drivers, travelled annual mileage is not a predictor of participating in traffic accidents when it comes to professional drivers. This results can be explained in two ways. Sullman et al. (2002) believed that professional drivers had relatively homogenous travelled annual mileage in comparison to drivers of private vehicles, while Dimmer and Parker (1999) thought that there was the upper limit of mobility which affected the increase of the probability of participating in traffic accidents and that the mileage which was above this limit had no impact on any further rise of the probability of participating in traffic accidents. The combination of these two findings can account for the obtained results, i.e. the differences between the analyzed driver groups. When it comes to the changes in the values of behaviour scales, the rise of the response value by one unit on the scale regarding lapses increases the chance of participating in traffic accidents by 38.9%, while the decrease of the response value by one unit on the scale regarding positive behaviour increases the chances of participating in traffic accidents by 13.5%. Two predictors recur in both applied models: age and lapses. While age has been proven as the predictor of participating in traffic accidents in numerous studies (in most cases, the correlation between young drivers and traffic accidents has been determined) for both observed groups of drivers, the change of the values on the scale regarding lapses shows one significant difference. Namely, while the rise of the values on the scale regarding lapses increases the chance of participating in traffic accidents for professional drivers, this chance decreases when it comes to non-professional drivers. This result can be explained by the fact that professional drivers are conscious of their lapses, while non-professional drivers are not prepared to admit making real lapses while driving. Finally, the strongest predictor related to participation in traffic accidents is ordinary violations for non-professional drivers, and lapses for professional drivers. 5. Conclusion Examining behaviour using a questionnaire has been accepted as a valid measurement in social sciences (Corbett, 2001). It is particularly true in case of studying risky behaviour, when it is requisite to examine psychological factors which can explain this behaviour. The possibility of measuring positive and aggressive behaviour, along with the aberrant behaviour of drivers, provides a more thorough understanding of their behaviour and associating psychological factors with mobility factors. The behaviours measured in this questionnaire can represent a suitable approach to objective measuring of risky behaviours. In Serbia the previous period investigated risky behaviour of pedestrians (Antic´, Pešic´, Milutinovic´, & Maslac´, 2016), but this is the first time to examine the behaviour of drivers. This paper provides the validation of the DBQ for two groups of drivers in Serbia. The comparative analysis of the behaviours has confirmed that non-professional drivers show risky behaviours to a greater measure than professional drivers. Differences in the behaviour of non-professional and professional drivers can be explained in two ways. First, reasons for travelling are different. The reasons of non-professional drivers can be both the need and pleasure, while professional drivers travel exclusively because of the need or realization of business duties. Second, these two groups of drivers have a different level of education and training. Namely, non-professional drivers have only the basic training related to driving a vehicle, but professional drivers also possess the Certificate of Professional

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Competence, apart from the regular training. In addition, professional drivers are obliged to perform periodic training and attend safe driving courses, and this certainly affects their behaviour. However, future studies should tend to provide additional and firm evidence which explain the differences in the behaviour of these two groups of drivers. The comparative analysis of two different driver categories can indicate the differences in their behaviour, and the fact that the different individual characteristics of drivers can be predictors of different behaviour types. Moreover, this analysis can demonstrate the correlation of different types of behaviour with participation in traffic accidents in the previous period. Comparative analysis provides the knowledge about the behaviour of different driver categories at the same territory and represents the basis for defining the actual safety conditions in the specific area. The future validations of the DBQ should tend to apply the same DBQ versions in the same area in order to examine the behaviours of different groups of drivers and thus obtain valid differences regarding their behaviours. Furthermore, researchers should add new items for the behaviours found in those areas, as it has been done in this study regarding mobile phones. Although the DBQ based studies can have a limitation related to providing socially desirable responses, they still offer fast, simple and comprehensive results as opposed to observational studies.

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