Accident Analysis and Prevention 80 (2015) 172–177

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Assessing dangerous driving behavior during driving inattention: Psychometric adaptation and validation of the Attention-Related Driving Errors Scale in China Weina Qu, Yan Ge * , Qian Zhang, Wenguo Zhao, Kan Zhang Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China

A R T I C L E I N F O

A B S T R A C T

Article history: Received 2 December 2014 Received in revised form 8 April 2015 Accepted 13 April 2015 Available online xxx

Driver inattention is a significant cause of motor vehicle collisions and incidents. The purpose of this study was to translate the Attention-Related Driving Error Scale (ARDES) into Chinese and to verify its reliability and validity. A total of 317 drivers completed the Chinese version of the ARDES, the Dula Dangerous Driving Index (DDDI), the Attention-Related Cognitive Errors Scale (ARCES) and the Mindful Attention Awareness Scale (MAAS) questionnaires. Specific sociodemographic variables and traffic violations were also measured. Psychometric results confirm that the ARDES-China has adequate psychometric properties (Cronbach’s alpha = 0.88) to be a useful tool for evaluating proneness to attentional errors in the Chinese driving population. First, ARDES-China scores were positively correlated with both DDDI scores and number of accidents in the prior year; in addition, ARDES-China scores were a significant predictor of dangerous driving behavior as measured by DDDI. Second, we found that ARDESChina scores were strongly correlated with ARCES scores and negatively correlated with MAAS scores. Finally, different demographic groups exhibited significant differences in ARDES scores; in particular, ARDES scores varied with years of driving experience. ã 2015 Elsevier Ltd. All rights reserved.

Keywords: Attention-Related Driving Errors Scale Driver inattention Dangerous driving Reliability and validity

1. Introduction Driver inattention is widely discussed in the literature. After a detailed analysis of definitions and taxonomies of the phrase “driver inattention," Regan et al. (2011) concluded that driver inattention can be defined as insufficient or no attention to activities critical for safe driving. Driver inattention occurs when, for example, a driver does not realize that the vehicle in front of him has slowed down; he then must brake abruptly to avoid a crash. Driving is a complex behavior that requires multiple tasks to be performed simultaneously. Driver inattention produces errors and can cause failures in performance while driving (Hole, 2007). Evidence is increasingly emerging that driver inattention is the primary cause of motor vehicle collisions and incidents (Dingus et al., 2006; Klauer et al., 2006). According to the most recent Chinese Road Traffic Accident Statistics (CRTAS, 2012), 4.727 million traffic accidents occurred in 2012. Inattentive

* Corresponding author at: 16 Lincui Road, Chaoyang District, Beijing 100101, China. Tel.: +86 10 64836956; fax: +86 10 64836047. E-mail address: [email protected] (Y. Ge). http://dx.doi.org/10.1016/j.aap.2015.04.009 0001-4575/ ã 2015 Elsevier Ltd. All rights reserved.

behaviors by drivers (e.g., failure to yield the right of way to others, driving in the wrong direction) accounted for 89.31% of these accidents. Considering the extremely negative influence of driver inattention on driving safety and the special traffic environment in China (for example, streets are often filled with pedestrians and bicycles, and traffic signs are often perplexing in China; Zhang et al., 2006), there is an urgent need to develop an effective instrument to explore attention-related driving errors in China. Driver inattention has an influence on driving safety. Previous studies have shown that inattention impairs driver performance and is a significant risk factor for crash involvement (Farmer et al., 2010; Klauer et al., 2006; Lemercier et al., 2014; Stutts et al., 2001). According to one study, inattention is involved in between 10% and 33% of all accidents in the United States (Ranney, 2008). Harbluk et al. (2002) investigated the impact of cognitive distraction on driver behavior in an on-road experiment. Drivers drove an 8 km city route while performing three different secondary tasks as distractors. The experiment found that inattentive drivers checked their mirrors less often, had reduced eye-scanning behavior, and tended to brake more abruptly and more strongly. Another study asked drivers to report their inattention while completing a driving

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task in a simulated driving environment. The results indicated that inattentive driving entails a failure to monitor the environment and a decrease in the standard deviation (SD) of speed (He et al., 2011). Many studies have shown a reduction in both lateral and longitudinal SD when drivers were in an inattentive state (Kubose et al., 2006; Reimer, 2009), and a reduction in lateral variation could be considered to reflect a decline in performance (Reimer, 2009). Previous studies measured inattention in a specific scenario in an on-road or simulated driving environment. However, inattention occurs more often in actual driving than in experimental situations. Therefore, instead of studying inattention in specific laboratory scenarios, the questionnaire provides an alternative method to measure inattention while driving. The Attention-Related Driving Error Scale (ARDES) is a 19-item self-reported questionnaire developed by Ledesma et al. (2010) to assess individual differences in the tendency to make attentional errors in specific driving contexts (e.g., “On approaching a corner, I do not realize that a pedestrian is crossing the street”). The original ARDES was constructed based on the culture and language in Argentina. Its items specifically refer to non-deliberate errors in driving behavior resulting from an attentional failure, such as failing to notice a traffic light due to inattention (Ledesma et al., 2010). These items were taken from the lapses scale of the Driving Behavior Questionnaire (DBQ; Reason et al., 1990) and from the Multidimensional Driving Style Inventory (MDSI; Taubman-BenAri et al., 2004). In the DBQ, some items do not clearly refer to attention-related errors; for example, the “I plan my route badly, so that I hit the traffic that I could have avoided” item refers to an error in trip planning rather than inattentive driving. In the MDSI, the same applies to the “I misjudge the speed of an oncoming vehicle when passing” item, which instead reflects an error related to a lack of expertise. In comparison with these questionnaires, the ARDES specifically includes items referring only to attentionrelated errors due to attentional failures while driving. In addition, the ARDES was also constructed to avoid overlapping with other psychological constructs (such as daydreaming, absorption, or dissociation). Furthermore, the internal consistency of the original Argentinean version of the ARDES has been reported to be higher (Cronbach’s alpha = 0.86) than that of the attentional lapse subscale of the DBQ (Cronbach’s alpha values ranged from 0.64 to 0.69). An exploratory factor analysis suggested that all 19 items belong to a single factor that accounted for 30% of the total variance in the proneness to attentional errors while driving (Ledesma et al., 2010). The ARDES has also been validated in Spain, and the resulting ARDES-Spain scores have exhibited good internal consistency (Cronbach’s alpha = 0.88). A factor analysis suggested that a single factor accounted for 32.70% of the total variance in ARDES-Spain scores (Roca et al., 2013a,b). Overall, the Cronbach’s alpha coefficient values and the factor structure of the ARDESSpain and ARDES-Argentina demonstrated that these scales exhibit good validity and reliability; thus, the ARDES can be considered as a simple and useful measure of individual differences in attention-related driving errors. Lopez-Ramon et al. (2011) found that the drivers with higher ARDES scores exhibited a general slowness in performance and less endogenous preparation for high-priority warning signs (Lopez-Ramon et al., 2011). However, because language, culture, traffic regulations and driving habits vary across countries, Roca et al. (2013a,b) study suggested that future studies that adapt this questionnaire to other countries would help to expand the cross-cultural equivalence of the ARDES. To our knowledge, the ARDES has not previously been validated in China. The relationships between the ARDES and a variety of cognitive and psychological variables have been analyzed to provide further evidence of the validity of this scale. First, Ledesma et al. (2010) found significant correlations between ARDES scores and a general

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tendency to make attentional errors in everyday life, as measured using the Attention-Related Cognitive Errors Scale (ARCES). Inattentive driving errors may not only arise from triggering events but can also be affected by a given psychological state. Individual differences in cognitive abilities, such as the ability to maintain attention, exist, and certain psychological traits can lead to greater error-proneness. Individuals who are prone to inattention in their daily lives may also be more likely to be inattentive while driving. Second, driving attention errors are related to individuals’ levels of awareness in the performance of daily life activities. Some studies have suggested that absent-mindedness is related to attentional failures in daily life (Herndon, 2008; Wallace and Vodanovich, 2003; Walsh et al., 2009). Ledesma et al. (2010) found significant negative correlations between ARDES scores and a lack of awareness in everyday life (Mindful Attention Awareness Scale, MAAS). Driver inattention is also dependent on many intrinsic factors. These intrinsic variables may include the driver’s age and years of driving experience (Young et al., 2008). Studies that have examined the relationship between age and inattention have yielded inconsistent results. Some studies have shown that older drivers have a lower attentional error propensity than do younger drivers (Roca et al., 2013a,b; Roca et al., 2013b; Smallwood et al., 2004). However, other research failed to find a correlation between age and inattention (Einstein and McDaniel, 1997; Ledesma et al., 2010). The results of studies that have investigated the relationship between years of driving experience and inattention have also been inconsistent. Klauer et al. (2006) showed that drivers who had more years of driving experience were more often involved in inattention-related accidents and near-accidents. One explanation of this finding could be that as a result of experience, older drivers may require fewer attentional resources for vehicle control and may make more inattention errors (Triggs and Regan, 1998). Another study found that the numbers of inattentive errors did not vary with the years of driving experience (Ledesma et al., 2010). The aims of the current study were as follows: (1) To adapt the Argentinean and Spanish versions of the ARDES to

the culture, language, traffic environment and regulations of China and thus to provide a Chinese version of the ARDES; (2) To verify the criterion validity of the ARDES by examining the relationships between the ARDES, dangerous driving behavior (as measured by a self-reported questionnaire, the Dula Dangerous Driving Index, DDDI) and self-reported traffic accidents and violations; (3) To further verify the relationship between the ARDES and experiences in daily life by investigating the relationships between the ARDES, the ARCES, and the MAAS; and (4) To investigate the relationships between driver inattention and sociodemographic characteristics (e.g., age, driving years).

2. Methods 2.1. Participants A total of 317 participants (215 males and 102 females) completed the questionnaire voluntarily and anonymously. The participants were recruited by a research company through interviewing individual drivers encountered in or around parking lots or residential areas. The ages of the participants ranged from 20 to 60 years (mean = 38.41, SD = 10.09); 24.61% of the participants were of age 20–30 years, 60.25% were of age 31–50 years, and 15.14% were of age 51 years or older. The subjects who had completed high school accounted for 84.22% of the study sample. All of the participants were licensed drivers with more than one

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year driving experience. The average participant had been driving for 6.86 years (SD = 5.54) since obtaining a driver’s license. 2.2. Measures 2.2.1. ARDES The ARDES was developed by Ledesma et al. (2010) to assess attention-related driving errors. The ARDES includes 19 items describing driving errors resulting from attention. Participants were requested to read each item and indicate the frequency with which they commit that type of error on a 5-point scale that ranged from never or nearly never (1) to always or nearly always (5). Total scores ranged from 19 to 95. In our study, a Chinese version of the ARDES was developed. The English version of the ARDES (Ledesma et al., 2010) was translated into Chinese using the following procedure. First, two students in psychology translated the English version of the ARDES into Chinese. Second, two professors checked and discussed the translation and created a revised draft of the questionnaire. Third, we invited three drivers to check the draft to ensure that the items in the questionnaire were clear and unambiguous. Fourth, a professional translator who was proficient in both English and Chinese back-translated the scale into English to evaluate whether the translation was correct. Finally, we modified the scale via a group discussion and finalized the scale based on the feedback of the three experienced drivers who had been recruited to pre-test the draft translation. These participants marked the items that they did not initially understand when they completed the questionnaire. 2.2.2. DDDI The DDDI is a 28-item self-reporting instrument developed by Dula and Balard (2003) to measure dangerous driving behaviors. For this scale, the study participants rate the frequency with which they engage in each item using a 5-point Likert scale ranging from 1 (never) to 5 (always). Total scores ranged from 28 to 140. The original scale had three components: risky driving (RD; twelve items, a = 0.83), negative cognitive/emotional driving (NCED; nine items, a = 0.85) and aggressive driving (AD; seven items, a = 0.84; Dula and Balard, 2003). Two items were removed from the RD component and considered separately to define a drunk driving factor (DD, a = 0.79) in the Flemish version of the DDDI (Willemsen et al., 2008). The Chinese version of the DDDI (Qu et al., 2014) was used in our study. This version of the DDDI was derived from the DDDI (Dula and Balard, 2003). A total score is calculated for each subscale, and the overall DDDI score is calculated by summing the score for each item. Higher mean scores indicate more dangerous driving behavior. The Chinese version of the DDDI has four

components: RD (ten items, a = 0.78), NCED (nine items, a = 0.80), AD (seven items, a = 0.78) and DD (two items, a = 0.63). 2.2.3. ARCES The ARCES was used to assess daily performance failures that were caused by attention lapses and memory failures (Carriere et al., 2008; Cheyne et al., 2006; Smilek et al., 2010). It includes 12 items that are scored from 1 (never) to 5 (very often). Total scores ranged from 12 to 60. The total ARCES score represents the frequency with which an individual makes cognitive errors. More attention-related cognitive errors are indicated by higher scores. The Chinese version of the ARCES translated by Carciofo et al. (2014) was used in this study; this version has been shown to have sufficient internal consistency (a = 0.92). 2.2.4. MAAS The MAAS (Brown and Ryan, 2003), which includes 15 items, was used to assess individuals’ general levels of awareness and attention to present events and experiences. All items are negatively worded (e.g., “I find it difficult to stay focused on what’s happening in the present”), and the scores for each item were reversed for analysis. In this study, we used the Chinese version of the MAAS constructed by Deng et al. (2011). The items of this scale were answered based on a 6-point scale from nearly always (1) to nearly never (6). Total scores ranged from 15 to 80. Higher scores on this scale reflect higher levels of dispositional mindfulness. In Deng et al. (2011), the scale’s Cronbach’s alpha was 0.85. 2.2.5. Sociodemographic variables Several sociodemographic variables were measured using driver self-reports, including age, gender, level of education, occupation, number of years of driving experience, number of accidents, and penalty points and fines during the past year. In China, one receives six penalty points when he or she is caught driving through a red light. Driver’s licenses are suspended if 12 penalty points are received in one year. The study participants were also asked to provide information about traffic accidents they were in and penalty fines they received in the previous year, as well as information about the following driving violations: speeding, ignoring traffic signs or markings, driving through red lights, not yielding the right of way to other drivers in accordance with regulations, and driving on the wrong side of the road. 2.3. Procedure The survey was conducted by a research company in Beijing, China. All of the participants were randomly chosen in and around

Table 1 Descriptive statistics for all variables.

Age Driving years Annual mileage (10,000 km) Accidents Points Fines ARDES ARCES MAAS DDDI NCED AD RD DD

Mean

SD

Range

Number of items

Cronbach’s alpha

38.41 6.86 1.095 0.35 1.02 113.88 1.76 1.91 4.56 1.88 2.06 1.70 1.92 1.44

10.09 5.54 0.68 0.87 2.30 326.17 0.49 0.53 1.12 0.45 0.54 0.55 0.51 0.62

20–40 1–30 0.05–3.4 0–6 0–12 0–3,300 1–3.47 1–3.5 1.13–6 1–3.18 1–3.78 1–3.43 1–3.5 1–4

19 12 15 28 9 7 10 2

0.90 0.86 0.96 0.89 0.73 0.78 0.73 0.54

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parking lots, shopping malls, or residential areas. After the research assistant introduced the survey, the participant could voluntarily agree to participate in the study. All of the participants were informed that their information would be maintained strictly confidential and used only for scientific research. After each participant completed a consent form, he or she was given a packet of questionnaires. The packet contained each of the previously mentioned surveys. The participants completed the questionnaires individually and anonymously within a period of approximately 20 min. After completing the survey, each participant received a gift. This study was approved by the Institutional Review Board of the Institute of Psychology of the Chinese Academy of Sciences.

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addition, the Cronbach’s alpha coefficient was 0.90, suggesting that the ARDES scores exhibited good internal consistency. Each case was subjected to principle components analysis (PCA). The KaiserMeyer-Olkin (KMO) measure of sampling adequacy was 0.909 and Bartlett’s test of sphericity was significant (x2 (171) = 1975.92, p < 0.001), which indicates that the data were suitable for factor analysis. No rotation was used because only one component was extracted. Factor analysis indicated that a single factor exceeded the parallel analysis criterion and accounted for 35.28% of variance. All the 19 items had positive loadings on this factor, ranging from 0.52 to 0.68 (Table 2). 3.3. ARDES validity

3. Results 3.1. Descriptive statistics for all variables Table 1 presents the descriptive statistics for all of the measured variables. All of the scales have acceptable reliability except for DD, which only had two items. The descriptive statistics for selfreported accidents, points and fines are included. Few drivers reported that they had been involved in accidents (n = 62), received penalty points (n = 66) or been fined (n = 78) in the prior year. 3.2. ARDES reliability Descriptive statistics for each of the 19 items of the ARDESChina are presented in Table 2, which also provides mean values, corrected item-total correlation values and factor loadings. The mean values range from 1.61 to 2.04. Each of the items was averaged into a single score. The higher the score is, the greater the attentional error propensity will be. The ARDES has a mean score of 1.76. The corrected item-total correlation values range from 0.46 to 0.61, i.e., from moderate to high levels, indicating that the items feature good discrimination power. The lowest corrected itemtotal correlation values were for item 6 (“On approaching a corner, I do not realize that a pedestrian is crossing the street.”), while item 13 has the highest value (“I drive through a traffic light that has just turned red as I was following the car right in front of me.”). In

To obtain further evidence of the validity of the predictive capacity of the ARDES-China, the association between ARDES and DDDI scores was analyzed. The results of a correlation analysis showed that ARDES scores were positively related with ARCES, DDDI and its subscales, which suggests that the numbers of driving attentional errors may increase with increasingly dangerous driving behavior, as measured using the DDDI. The correlations between ARDES scores and self-reported traffic accidents and violations were also analyzed using Spearman’s correlation. ARDES positively correlated with the total number of traffic accidents that they were involved during the previous year. However, no significant correlation was found between ARDES and the total penalty points or fines for traffic citations during the previous year. The correlation index is also shown in Table 3. To assess the validity of the ARDES, Pearson’s correlation analysis was used to examine the associations between ARDES, DDDI and reported daily attention (see the correlation matrix in Table 3). The relationships between ARDES-China scores and sociodemographic variables were analyzed. ARDES scores exhibited a small negative correlation with age (r = 0.15, p < 0.01) and driving years (r = 0.18, p < 0.01). Age and ARDES score were not significantly correlated in a partial correlation that controlled for driving years. However, the correlation between driving years and ARDES scores remained significant (r = 0.13, p < 0.05) when controlling for age.

Table 2 Descriptive statistics for the 19-item Attention-Related Driving Errors Scale (n = 317). Item

Mean Std. dev.

Range Factor loading

Corrected item-total correlation

1. When I head toward a known place, I drive past it for being inattentive. 2. I signal a move, and unintentionally make another (e.g., I turn on the right-turn blinker but turn left instead). 3. On approaching an intersection, I miss a car coming down the road for being inattentive. 4. Suddenly I notice that I have lost or mistaken my way to a known place. 5. On approaching an intersection, instead of looking at the traffic coming in, I look at the opposite direction. 6. On approaching a corner, I do not realize that a pedestrian is crossing the street. 7. I do not realize that there is an object or a car behind and unintentionally hit into it. 8. I do not realize that the vehicle right in front of me has slowed down and I have to brake abruptly to avoid a crash. 9. Another driver honks at me making me realize that the traffic light has turned green. 10. I forget that my lights are on full beam until flashed by another motorist. 11. For a brief moment, I forget where I am heading to. 12. I have to take more turns than necessary to arrive at a place. 13. I drive through a traffic light that has just turned red as I was following the car right in front of me. 14. I try to drive the car forward and do not realize that I have not put it into first gear. 15. I try to use a car device but use another one instead (e.g., I turn on the lights instead of the windshield wipers). 16. I intend to go to a certain place and suddenly realize that I am heading somewhere else. 17. I realize that I had been inattentive and had not noticed the traffic light. 18. I unintentionally make a wrong turn or drive toward coming traffic. 19. I unintentionally make a mistake in shifting the gear or shift to the wrong gear.

2.04 1.66

0.90 0.77

1–4 1–4

0.55 0.60

0.49 0.54

1.71 1.97 1.78

0.75 0.87 0.82

1–4 1–4 1–5

0.66 0.52 0.65

0.60 0.46 0.59

1.62 1.76 1.79

0.76 0.82 0.84

1–4 1–4 1–4

0.54 0.61 0.62

0.47 0.55 0.55

1.82 1.77 1.64 2.00 1.77 1.66 1.68

0.83 0.85 0.82 0.98 0.80 0.79 0.76

1–5 1–4 1–4 1–5 1–4 1–4 1–5

0.61 0.60 0.64 0.52 0.68 0.56 0.61

0.56 0.54 0.58 0.47 0.61 0.50 0.54

1.61 1.75 1.74 1.73

0.75 0.80 0.82 0.82

1–4 1–5 1–4 1–4

0.57 0.58 0.55 0.57

0.50 0.52 0.48 0.51

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Table 3 Correlation matrix of variables in ARDES validation study.

Accidents ARCES MAAS DDDI NCED AD RD DD

ARDES

ARCES

0.21** 0.60** 0.24** 0.63** 0.53** 0.62** 0.48** 0.53**

0.25** 0.56** 0.51** 0.49** 0.45** 0.39**

MAAS

0.16** 0.14** 0.18** 0.11** 0.08**

DDDI

NCED

AD

RD

0.90** 0.80** 0.90** 0.56**

0.57** 0.76** 0.39**

0.55** 0.53**

0.36**

Notes: ARDES: Attention-Related Driving Errors Scale; ARCES: Attention-Related Cognitive Errors Scale; MAAS: Mindful Attention Awareness Scale; DDDI: Dula Dangerous Driving Index (total score); NCED: negative cognitive/emotional driving in DDDI; AD: aggressive driving in DDDI; RD: risky driving in DDDI; DD: drunk driving in DDDI. * p < 0.05. ** p < 0.01.

To measure the effect of ARCES and ARDES on dangerous driving behavior, hierarchical multiple regression analyses were conducted while controlling the age and the number of driving years in the first step. The ARCES score was entered at the second step, and the ARDES score was entered at the third step. Total DDDI score was selected as the dependent variable. The results of the regression are presented in Table 4. The final regression equation is DDDI = 0.61 + 0.01 (driving years) + 0.09 (annual miles) + 0.25 (ARCES) + 0.42 (ARDES). The results indicate that the ARDES was a significant predictor of dangerous driving behavior and accidents. The effects were significant even when the demographic variables and general attention-related errors were controlled for. 4. Discussion 4.1. Summary of the findings The primary aim of this paper was to develop an adapted ARDES for Chinese people based on the culture, language, traffic environment and regulations in China. In our study, the ARDES was translated into Chinese, and its reliability and validity were confirmed. Desirable psychometric qualities were found in the Chinese version of the ARDES. The predictive capacity of ARDESChina scores was also confirmed in relation to self-reported traffic violations. First, psychometric results confirmed that the ARDES-China has adequate psychometric properties to be a useful tool for evaluating proneness to attentional errors in the Chinese driving population. The Chinese ARDES was found to be highly reliable and to have a stable structure. The internal consistency of the ARDES was relatively high and was comparable with those of other versions of the ARDES (Roca et al., 2013a,b). The ARDES-China has a slightly

Table 4 Effect of demographic variables, ARCES and ARDES on DDDI.

b

Variables Step 1 Age Driving years Annual mileage

0.13* 0.01 0.25**

ARCES

0.55**

ARDES

0.45**

Step 2

Step 3

* **

p < 0.05. p < 0.01.

R2

DR2

0.07

0.07**

0.36

0.29**

0.47

0.12**

higher Cronbach’s alpha coefficient value than does the ARDESArgentina (0.88 and 0.86, respectively). All the 19 items yielded high loadings on the first factor, good discrimination indexes, and high internal consistency. The ARDES-China items measure a common factor related to individual differences in attentionrelated errors while driving. This finding is in accordance with the results of previous research conducted using similar instruments; these prior studies discovered an “inattention factor” that could be differentiated from other dimensions of driver behavior (Reason et al., 1990; Taubman-Ben-Ari et al., 2004). Second, ARDES-China scores were positively correlated with dangerous driving behavior as measured by not only DDDI but also the four DDDI subscales of RD, AD, DD and NCED. Regression analysis revealed that ARDES was a significant predictor of dangerous driving behavior when controlling for social demographic variables and ARCES. In addition, the numbers of accidents reported by study participants were used as a criterion to support the empirical validity of the Chinese ARDES. In particular, ARDESChina score was found to be positively related to the number of accidents in the prior year. These findings suggest that inattention during driving is highly correlated with driver behavior and driver safety. Consistent with the findings of Ledesma et al. (2010) and Roca et al. (2013a,b), our results show that drivers who reported being in traffic collisions were more prone to attentional errors while driving than the drivers who did not report being in accidents. According to another report that conducted in-depth analyses of driver inattention using the driving data collected in the 100-Car Naturalistic Driving Study, 78% of accidents and 65% of near-accidents involved one or more inattention factors (Dingus et al., 2006). Our results further support that driver inattention could be a major cause of dangerous driving behavior. Third, we also explored the relationships between ARDES scores and a variety of cognitive and psychological variables. We found that ARDES-China scores were strongly correlated with a measure of the frequency of cognitive errors in everyday life. This finding indicated that the drivers with attention-related driving errors are more likely to experience attentional failures in everyday life, in agreement with previous research (Ledesma et al., 2010; Roca et al., 2013a,b). Furthermore, ARDES-China scores were found to be strongly correlated with MAAS scores, which strengthens the hypothesis that this type of driving error is closely linked to inattention and a lack of awareness in everyday life. This result further supports previous findings that mindfulness is negatively correlated with the commission of errors (Cheyne et al., 2006; Herndon, 2008). Finally, comparisons of scores across different demographic groups and dangerous driving behaviors revealed significant differences and showed that the numbers of attention-related driving errors varied with age and the years of driving experience. Our study found that age negatively correlated with the number of attentional driving errors. However, the effect disappeared when controlling for driving experience. Moreover, the years of driving experience were also found to negatively correlate with the number of attentional driving errors when age was controlled for. This result is in contrast with the results of a study by Klauer et al. (2006), which showed that experienced drivers are involved in accidents more frequently than are novice drivers. However, some studies have also found that ARDES scores did not vary significantly with the number of years of driving experience (Ledesma et al., 2010). It should be noted that age is a variable that is strongly associated with driving experience. Roca et al. (2013a,b) also found significant correlation between ARDES-Spain total scores and the age of the drivers. However, the result was not significant after controlling for the driving years, which suggests that the negative correlation between ARDES and age might be explained by differences in driver experience.

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4.2. Limitations This study has several limitations. One important limitation of this study is that the data depend on drivers’ self-reports to measure traffic accidents and illegal behaviors; these reports may be affected by social pressures. If we were able to obtain the actual traffic records of traffic offenders (i.e., those convinced of traffic accidents, ignoring traffic signs or markings), the analysis of the scale scores for such individuals would be more reliable. Future studies would be enhanced by integrating self-reported measures with other methods (such as field observation and simulated driving). Another concern is whether the sequence in which the questionnaires are completed is counter-balanced. Finally, the sample recruitment method yielded a sample that is not representative of all drivers. 5. Implications The current study is the first to translate the ARDES into Chinese. The Chinese version of the ARDES demonstrates a relatively high internal consistency and good validity for surveying different types of attentional driving errors. For example, the results of this studycan be used to design training classes that provide feedback to drivers about their behaviors that lead to attentional driving errors. Such programs would improve the drivers’ awareness of attentional driving errors and help them to modify their driving behavior. Furthermore, ARDES is useful for evaluating driver attention and could be used in practical applications after additional testing in onroad studies. Additionally, driver attention might change according to the traffic situation and familiarity with the road. Further studies should investigate these effects on inattention driving errors. Relevant results could then be used to develop specific training classes to help drivers respond appropriately when encountering novel driving situations. Acknowledgments This study was partially supported by grants from the National Natural Science Foundation of China (Grant nos. 31100750,31400886 and 91124003) and the Basic Project of National Science and Technology of China (No. 2009FY110100). References Brown, K.W., Ryan, R.M., 2003. The benefits of being present: mindfulness and its role in psychological well-being. J. Pers. Soc. Psychol. 84 (4), 822–848. doi:http://dx.doi.org/10.1037/0022-3514.84.4.822. Carciofo, R., Du, F., Song, N., Zhang, K., 2014. Chronotype and time-of-day correlates of mind wandering and related phenomena. Biol. Rhythm Res. 45, 37–49. doi:http://dx.doi.org/10.1080/09291016.2013.790651. Carriere, J.S., Cheyne, J.A., Smilek, D., 2008. Everyday attention lapses and memory failures: the affective consequences of mindlessness. Conscious. Cogn. 17 (3), 835–847. doi:http://dx.doi.org/10.1016/j.concog.2007.04.008. Cheyne, J.A., Carriere, J.S., Smilek, D., 2006. Absent-mindedness: lapses of conscious awareness and everyday cognitive failures. Conscious. Cogn. 15 (3), 578–592. doi:http://dx.doi.org/10.1016/j.concog.2005.11.009. Deng, Y.Q., Li, S., Tang, Y.Y., Zhu, L.H., Ryan, R., Brown, K., 2011. Psychometric properties of the Chinese translation of the mindful attention awareness scale (MAAS). Mindfulness 3 (1), 10–14. Dingus, T.A., Klauer, S.G., Neale, V.L., Petersen, A., Lee, S.E., Sudweeks, J.D., Knipling, RR, 2006. The 100-Car Naturalistic Driving Study. Virginia Tech Transportation Institute. Dula, C.S., Balard, M.E., 2003. Development and evaluation of a measure of dangerous, aggressive, negative emotional, and risky driving. J. Appl. Soc. Psychol. 2, 263–282 33. Einstein, G.O., McDaniel, M.A., 1997. Aging and mind wandering: reduced inhibition in older adults? Exp. Aging Res. 23 (4), 343–354. doi:http://dx.doi.org/10.1080/ 03610739708254035.

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