THE INFLUENCE OF RISK PERCEPTION AND SELF-ASSESSED DRIVING ABILITIES ON THE BEHAVIOR OF YOUNG DRIVERS

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1 XII International Symposium "ROAD ACCIDENTS PREVENTION 2014" Hotel Jezero, Borsko Jezero, 09 th and 10 th October UDK: THE INFLUENCE OF RISK PERCEPTION AND SELF-ASSESSED DRIVING ABILITIES ON THE BEHAVIOR OF YOUNG DRIVERS Dragan Jovanović a, Predrag Stanojević b, Dragana Jakšić c a Faculty of Technical Sciences, Department of Transport, Trg Dositeja Obradovića 6, Novi Sad, Serbia b Polytechnic School of Vocational Studies Uroševac, 24. Novembar b.b, Leposavić, Serbia c PhD student, Faculty of Technical Sciences, Department of Transport, Trg Dositeja Obradovića 6, Novi Sad, Serbia Abstract: According to the data from the World health organization, traffic accidents are the leading cause of death for young people aged 15 to 29. The aim of this paper is to establish the influence of risk perception and self-assessed driving abilities on making violations and participation in traffic accidents among young drivers. The sample consisted of 255 participants (162 males and 93 females). In order to accomplish the research goals, the following questionnaires were used: questionnaire for acquisition of socio-demographic data, questionnaire on risk perception, questionnaire on self-assessed driving abilities consisting of four subscales (general driving ability, safety orientations, body dimension, and specific task skills) and the questionnaire on behavior of young drivers consisting of two subscales (transient and fixed violations). Correlation analysis has shown the significant correlation of risk perception and self-assessed abilities with violations and traffic accidents. The results of regression analysis have shown that according to risk perception and self-assessed abilities it is possible to predict 22% of transient violations, 11% fixed violations and 6% involvements in traffic accidents. The possible explanations of obtained results and practical implications have been discussed. Keywords: young drivers, behaviour, risk perception 1. INTRODUCTION According to World Health Organization every year 1,3 million people die in road crashes worldwide and million people are injured. Road traffic injuries are the eighth leading cause of death globally, and the leading cause of death for young people aged (WHO, 2013). Young drivers are more frequently involved in traffic accidents compared to other age groups (OECD, 2006; Bjørnskau, 2000; Lotan & Toledo, 2007; Williams, 2003). The researches point to three main problems: inexperience, age and gender. Namely, the universal problem of young, novice drivers is inexperience and, since most people learn to drive while they are young, it greatly explains much of the high levels of risk among young drivers. In addition, it is well known that the extent of the risk decreases with the increasing of age and young men are more likely to have serious crashes than young women (according to OECD, 2006).

2 In terms of safety of the young drivers, it is necessary to emphasize several aggravating circumstances: they are inexperienced in the driving task and have problems with risk perception; they are high risktakers in their life-style (not only in driving) and immature; they often drive at high-risk conditions- such as night, when tired and, sometimes, when under the influence of alcohol and other substances; they are susceptible to peer pressure to assume more risk from their passengers or friends and most of the young, novice drivers consider themselves as safe drivers (Masten, 2004, according to Shinar, 2007). It seems logical that drivers who asses their abilities as good one also asses that their driving is much better than average driver, which can affect their driving style. This claim gain on importance when is taken into account that findings show that drivers overestimate their driving abilities, that is, that they are more skilled than average driver (e.g. Delhomme, 1991; Goszczynska & Roslan, 1989; SARTRE, 2004). Elvik et al. (1997) argue that the overestimation of self-abilities in traffic is an important factor of overinvolvement of young drivers in traffic accidents. In addition, the risk perception can also influence the behavior of young drivers. Deery (1999) defined traffic risk perception as subjective interpretation of the possibility of risk involvement in various traffic situations. Drivers who perceive low crash-risk in an objective high crash-risk will drive recklessly, while those who perceive high crash-risk in the same situation will drive cautiously and will do anything to avoid risks. Rundmo (1999) argued that if behavior is influenced by perceptions of risk, influencing risk perception could change behavioral patterns. Various researches have shown that there is a connection between the risk perception and the behavior of young drivers (e.g. Brown & Groeger, 1988; Harbeck & Glendon 2013; Machin & Sankey, 2008). Perceptions of risk seems also to be of importance, since young drivers, as compared to other age groups, are more likely to underestimate the probability of the specific risks caused by traffic situations (Brown & Groeger, 1988; Deery, 1999). In terms of young drivers, different factors can contribute to their traffic risk behavior (for example, demography, personality, attitudes, environment ). The aim of this study is to estimate to which extent the risk perception and self-assessed driving abilities at young drivers can contribute to their traffic risk behavior. Based on the research goal and results of the previous researches the two hypotheses are established: Lower level of risk perception and overestimation of self-abilities of young drivers is connected to driving violations. Lower level of risk perception and overestimation of self-abilities of young drivers is connected to involvement in traffic accidents. 2. METHOD 2.1. Participants and procedure The sample is constructed using the snowball sampling technique (Goodman, 1961). Students of Polytechnic School of Vocational Studies applied the questionnaire (in paperpencil or electronic format) to at least 5 persons holding a driver s license for at least one year. Students received extra credit for this task. The demographic characteristics of the sample can be found in Table 1.

3 Table 1. Demographic variables Gender Male (%) 162 (63,5) Female (%) 93 (36,5) Age Range (the youngest-the oldest participant ) Mean (S.D.) 19,65 (1,874) Driving experience in months Range (the least moths- the most months) 0-36 Mean (S.D.) 12,92 (8,16) Mileage from licensing Mean (S.D.) (15.297) Number of accidents Range 0-6 Mean (S.D.) 0,42 (0,94) SD Standard deviation 2.2. Instruments Demographic variables The questionnaire consisted of 5 items and drivers were asked about their sociodemographic data (gender, age), driving experience (possession of driving license in months, monthly mileage) and traffic accidents (involvement in traffic accidents since licensing) Risk perception questionnaire Risk perception questionnaire (Harbeck & Glendon, 2013; Rosenbloom et al., 2008) consisted of 14 items. The respondents were asked to estimate how risky are the given actions in the traffic (for example driving under influence of alcohol, driving with the speed over 20 km/h of speed limit). The responses were recorded on a 5-point scale of Likert type (from 1 = not risky tо 5 = extremely risky). The reliability of the scale was satisfactory (α = 0.83) Self-assessed driving ability questionnaire The measurement instrument applied to examine self-assessed driving ability consisted of 22 indicators (Tronsmoen, 2008). The indicators fell into four dimensions. The first dimension related to self-assessment of general driving ability, which included skills such as driving fast, anticipating, driving in slippery conditions, and driving in the dark. The second dimension, safety orientation, referred to the driver s perception of his/her own ability to identify risk, danger and his/her perception of their ability to drive with satisfactory safety margins. The third dimension was the body dimension, which measured the feeling of unity with and control of the car. The fourth dimension was specific task skills and items under this dimension consisted of judgment of the ability for precise and effective parallel parking, reversing into a garage, as well as reversing using the rear-view mirrors. The subjects were asked how well do the statements fit the way they deal with and perceive driving a car? The responses were marked on a 5-point scale (1 = does not fit me at all to 5 = fits me perfectly).

4 Behaviour of Young Novice Drivers Scale The Behavior of Young Novice Drivers Scale (BYNDS, Scott-Parker et al., 2010) was used to measure the risky behavior of young novice drivers specifically. Source questionnaire is consisted of 44 items divided into 5 subscales: transient violations, fixed violations, misjudgments, risky exposure and driver mood. For the needs of this research only the subscale transient violations (12 items; α = 0.91) and fixed violations (10 items; α = 0.84) were used. Respondents were asked to indicate how often they have done the following behaviors. Responses were recorded on a five point Likert scale that ranged from 1 to 5 (1 = never to 5 = almost all the time). 3. RESULTS The connection between the risk perception, the self-assessed driving abilities, violations and traffic accidents was examined by Pearson s linear correlation analysis. The results are shown in Table 2. Table 2. Correlation between risk perception, self-assessed driving abilities, violations and accidents Variables Risk perception - 2. General driving ability -,11-3. Safety orientation,06,61 ** - 4. Body dimension,02,67 **,61 ** - 5. Specific task skills -,04,55 **,42 **,63 ** - 6. Transient violations -,43 **,37 **,16 **,24 **,22 ** - 7. Fixed violations -,29 **,29 **,09,21 **,20 **,70 ** - 8. Involvement in accidents -,01,33 **,14 *,21 **,19 **,21 **,16 * - * p <.05; ** p <.01. As it can be seen from the Table above, the risk perception and self-assessed driving abilities have significantly correlated with transient and fixed violations. Only the correlation between safety orientation and fixed violations has not reached the statistical significance. The similar is with traffic accidents. However, there was no significant correlation only between traffic accidents and risk perception. In order to realize the objectives of the research and accurately investigate the relationship between predictor and dependent variables, three regression analyses have been conducted: 1. Dependent variable in the first regression analysis was transient violations and as independent variables in the first block, the demographic variables (gender and mileage) were entered; in the second block, the risk perception and self-assessed abilities were entered. 2. Dependent variable in the second regression analysis was fixed violations, and as independent variable in the first block, the demographic variables (gender and mileage) were entered; in the second block, the risk perception and self-assessed abilities were entered. 3. Dependent variable in the third regression analysis was traffic accidents, and as independent variables in the first block, the demographic variables (gender and mileage) were entered; in the second block, the risk perception and self-assessed abilities were entered. The results of the first and second regression analysis are presented in Table 3.

5 Table 3. Hierarchical regression analysis for dependent variables transient and fixed violations Transient violations Fixed violations Predictors ΔR 2 β Predictor ΔR 2 β Step *** Step *** Gender *** Gender *** Mileage.223 *** Mileage.143 * Step *** Step *** Risk perception *** Risk perception *** General driving.231 ability General driving ability.193 * Safety orientation.007 Safety orientation.090 Body dimension.027 Body dimension.078 Specific task skills.015 Specific task skills.017 Total R *** Total R *** * p <.05; ** p <.01; *** p <.001. In the first regression analysis, demographic variables (gender and mileage) were entered in the first step, which explained 12% of the variance of committing transient violations. After entry of variables of risk perception and self-assessed ability in the second step, the model as a whole explained 34% of total variance. In the final model, after controlling for the influence of gender and mileage, the largest unique contribution was made by risk perception (β = -.392, p <.001), whereas a statistically significant contribution was also given by self-assessed general driving ability (β =.231, p <.01). In the second regression analysis, demographic variables (gender and mileage) were entered in the first step, which explained 9% of the variance of committing fixed violations. After entry of variables of risk perception and self-assessed abilities in the second step, the model as a whole explained 21% of total variance. In the final model, after controlling for the influence of gender and mileage, the largest unique contribution was made by risk perception (β = -.252, p <.001), whereas a statistically significant contribution was also given by the selfassessed general driving ability (β =.193, p <.05). Table 4. Hierarchical regression analysis for dependent variable of traffic accident Traffic accidents Predictors ΔR 2 β Step1.112 *** Gender ** Mileage.266 *** Step ** Risk perception General driving ability.313 *** Safety orientation Body dimension Specific task skills.001 Total R *** * p <.05; ** p <.01; *** p <.001.

6 The results of the first two regression analyses show that the risk perception and self-assessed abilities are important predictors of committing traffic violations and explain the important part of violation variance. The results of the third regression analysis are given in Table 4. In the third regression analysis, demographic variables (gender and mileage) were entered in the first step, which explained 11% of variances of involvement in traffic accidents. After entry of the variables of risk perception and self-assessed abilities, in the second step, the model as a whole explained 17% of total variance. In the final model, after controlling for the influence of gender and mileage, the largest unique contribution was made by self-assessed general driving abilities (β =.193, p <.001). 4. DISCUSSION The main objective of this study was to establish to which extent the risk perception and selfassessed abilities of young drivers contribute to their risky behavior in traffic. We assumed that the lower level of risk perception and overestimation of self-abilities of young drivers is associated to committing of driving violations and involvement in traffic accidents. The obtained results partially confirm the given hypothesis. The results of correlation and regression analysis show that the drivers with lower risk perception are more prone to transient and fixed violations. However, the influence of risk perception to traffic accident has not been identified. This is surprising taking into consideration the association of risk perception with commitment of violations and the evidences of previous researches that show the strong association between risky behavior and involvement in accidents (e.g. Parker et al., 1995; Taylor et al., 2000). It is possible that young drivers with lower risk perception are more prone to committing violations, but mostly to those less dangerous, and therefore in the small sample was not obtained the significant association with traffic accidents. The correlation analysis has shown the association between all investigated dimensions of selfassessed abilities and committing of violations and involvement in accidents. However, the results of regression analysis show that the only important predictor both at committing violations and traffic accidents was general driving ability. The general driving ability was investigated by several items referring to driver s skills and efficiency (for example, I have excellent driving skills, I am well skilled to drive fast if necessary, I am well skilled to anticipate) and in taking measures he particular attention should be paid to correction of such beliefs. Tronsmoen (2010) also obtained that the self-assessed abilities is associated to risk behavior of young drivers. Moreover, his results indicate that young novice drivers crash involvement is stronger associated with driving skills (manifested as self-assessment of driving ability) than safety attitudes and self-reported driver behavior. Interestingly, the results of correlation analysis have shown that the risk perception did not correlate with either dimension of self-assessed abilities. Although it can be assumed that drivers who overestimate their own abilities are likely to have lower level of risk perception, this is not the case. This indicates that the background of these two factors is of different nature. The future research should investigate this background in order to take appropriate measures, which final result would be the change in the behavior of young drivers. The researches have shown that advanced driving training and traffic safety campaigns can improve the risk (e.g., Rosenbloom et al., 2008; Rundmo & Iversen, 2004) and the application of these measures should be certainly considered. When it comes to self-assessed abilities, education (theoretical courses) during the training can contribute to better understanding of relationship between the abilities of the young driver and driving task. In addition, there are evidences from the studies that driving experience that contributes to the development of driving skills reduces the risk of accidents among young drivers,

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