category: literaturenote citekey: prechtidentifyingmainfactorscontributing2017 title: Identifying the main factors contributing to driving errors and traffic violations – Results from naturalistic driving data authors: "Precht, Lisa; Keinath, Andreas; Krems, Josef F." year: 2017 date: 2017-00-00 2017 doi: 10.1016/j.trf.2017.06.002 publication: "Transportation Research Part F: Traffic Psychology and Behaviour" url: "https://doi.org/10.1016/j.trf.2017.06.002" zotero_key: BUBGD755 zotero_storage: G47KD7AF collections: transpordi_planeerimine folder: 001_artiklid firstAuthor: "Precht, Lisa"

status: converted

Transportation Research Part F

journal homepage: www.el sevier.com/locate/trf

Identifying the main factors contributing to driving errors and traffic violations – Results from naturalistic driving data ^q

Lisa Precht a,⇑ , Andreas Keinath ^a , Josef F. Krems ^b

^a BMW Group, 80788 München, Germany
^b Technische Universität Chemnitz, 09107 Chemnitz, Germany

article info

Article history: Received 5 February 2017 Received in revised form 27 April 2017 Accepted 3 June 2017 Available online 20 June 2017

Keywords: Driver behavior Driver distraction Naturalistic driving

abstract

Researchers have identified various factors that likely affect aberrant driving behaviors and therefore crash risk. However, it remains unclear which of these factors poses the greatest risk for either errors or violations under naturalistic driving conditions. This study investigated important variables contributing to driving errors and traffic violations based on naturalistic driving data from the second Strategic Highway Research Program (SHRP 2). In addition, this study identified factors determining the drivers' willingness to perform common secondary tasks while driving, which have been associated with different degrees of crash risk. Results showed that anger, passenger presence, and persistent individual differences in driver behavior were the main factors associated with committed violations; surprise, high-risk visually distracting secondary tasks, and the driving task demand passing through an interchange were the main factors associated with errors. The willingness to engage in risky secondary tasks while driving appeared to be related to an overall tendency to engage in risky driving behaviors. However, drivers considered the driving context particularly when engaging in visually distracting secondary tasks. This study's comprehensive approach should be a step towards generating a complete model of the variables that contribute to, or mitigate dangers in traffic.

1. Introduction

According to the World Health Organization in 2015, the number of road deaths globally plateaued at 1.25 million per year, making road traffic injuries a leading cause of death (Toroyan, 2015). A central factor in crash risk is road user behavior (Rowe, Roman, McKenna, Barker, & Poulter, 2015). A survey on crash causation (Singh, 2015) indicated that driving errors and violations were the key factors in about 74% of the crashes. These ''aberrant driving behaviors" are therefore a crucial threat to road safety (Zhang, Chan, & Zhang, 2015). For any given safety-critical event, including driving errors and traffic violations, various contributing factors may play a role (Olson, Hanowski, Hickman, & Bocanegra, 2009). These factors include environmental, situational, and individual factors. The focus of the current study was to investigate how these factors contribute to aberrant driving behaviors in naturalistic driving segments. In addition, this study attempted to identify key variables affecting drivers' willingness to perform specific secondary tasks while driving. Of the possible causal factors

E-mail address: Lisa.Precht@bmw.de (L. Precht).

^q The findings and conclusions of this paper are those of the authors and do not necessarily represent the views of the VTTI, SHRP 2, the Transportation Research Board, the National Academies, or the BMW Group, who funded the current work.

^⇑ Corresponding author.

for aberrant driving behaviors, secondary tasks were of particular interest given their decisive role in motor vehicle accidents (e.g., Dingus et al., 2016).

1.1. Aberrant driving behaviors

A connection between aberrant driving behaviors and accidents has been well supported. In contrast to vehicle or environmental factors, drivers have been found to be the critical reason in as many as 94% of crashes. Aberrant driving behaviors were discovered in about 74% of these crashes (Singh et al., 2015). Nevertheless, relatively little research exists that has systematically examined the nature of and, in particular, the factors contributing to errors and violations in naturalistic driving.

Errors and violations are distinct given their different psychological origins (Reason, Manstead, Stradling, Baxter, & Campbell, 1990). Errors are defined as unwitting deviations of action from intention or the failure of planned actions to achieve their intended consequence (Reason, 1990; Reason et al., 1990). In contrast, violations are ''deliberate deviations from those practices believed necessary to maintain the safe operation of a potentially hazardous system" (Reason et al., 1990, p. 1316). Therefore it is not surprising that these driver behaviors have been associated with different traffic and driver related factors. Whereas errors have been found to coincide with driver distraction and driving task demands, violations have rather been associated with factors such as driving anger and the presence or absence of passengers. Furthermore, driver characteristics (e.g., age, driving experience, sex) appear to strongly affect both aberrant driving behaviors. The following sections present more detailed information about potential risk factors examined in the current study for aberrant driving behaviors related to driver distraction, such as secondary task demands, driving task demands, and driving anger, passenger presence, and driver characteristics.

1.2. Driver distraction

Driver distraction can be defined as ''occurring when a driver's attention is, voluntarily or involuntarily, diverted away from the driving task by an event or object to the extent that the driver is no longer able to perform the driving task adequately or safely" (Young, Regan, & Hammer, 2007, p. 380). Inattention and driver distraction due to secondary task engagement are among the leading causes of motor vehicle accidents (e.g., Klauer, Dingus, Neale, Sudweeks, & Ramsey, 2006; Klauer et al., 2014). It is assumed that a large number of these accidents are the result of driving errors caused by the driver being distracted (e.g., Staubach, 2009). Beyond that, demands of the driving task itself, such as increased traffic density or intersection influences, are also likely to either directly affect aberrant driving behaviors or moderate the effect of driver distraction on driving performance (Young et al., 2007).

1.2.1. Secondary task demands

Drivers frequently engage in potentially distracting secondary tasks. Dingus et al. (2016) reported that drivers were involved in distracting activities during more than 50% of approximately 19,700 examined naturalistic driving segments. Driving distractions can stem from competing visual, manual, or cognitive resources (Strayer, Turrill, Coleman, Ortiz, & Cooper, 2014). All of these distraction types have been associated with detrimental effects on driving performance. However, whereas there is broad agreement on the negative impact of visual and visual-manual distraction (Carsten & Merat, 2015), the effects of cognitive load from secondary task engagement on driving errors and crash risk is still debated.

Some findings suggest that cognitive distraction impairs driving performance due to a diversion of attention (e.g., Horrey & Wickens, 2006; Strayer et al., 2013). However, cognitive distraction can also reduce the risk of driving errors caused by fatigue, especially during long and monotonous drives (Jellentrup, Metz, & Rothe, 2011). Therefore, it is not surprising that cognitively distracting tasks have been associated with a decreased crash and near-crash risk in several naturalistic driving studies (Hickman, Hanowski, & Bocanegra, 2010; Klauer et al., 2006; Olson et al., 2009; Simmons, Hicks, & Caird, 2016; Victor et al., 2015).

In contrast, distracting activities requiring the driver's eyes to deviate from the roadway are known to carry the highest crash risk (e.g., Simmons et al., 2016). Congruent with this finding, numerous investigations suggested a connection between visual/visual-manual distraction and driving errors. Eye gazes away from the road have been demonstrated to result in impaired signal or event detection performance (e.g., Caird, Johnston, Willness, Asbridge, & Steel, 2014) and unintentional lane deviations (e.g., Olson et al., 2009; Young, Salmon, & Cornelissen, 2013).

Even though driver distraction and driving error causation have been thoroughly investigated, the impact of cognitive distraction compared to visual and visual-manual distraction on driving performance in real traffic remains unclear. In particular need of examination are cognitively distracting tasks also containing visually distracting subtasks such as passenger interaction.

1.2.2. Driving task demands

Evidence exists that the driving context can affect crash risk both directly or through moderating driver distraction effects. Traffic density, for example, was found to increase the risk of crashes as well as to intensify detrimental effects observed when engaging in distracting secondary tasks (e.g., Strayer, Drews, & Johnston, 2003; Tian, Li, Chen, Chen, & Witt, 2013). In addition, driving through intersections has been found to contribute to driving errors and crashes due to the increased complexity and need to make multiple decisions, often under time pressure and at considerable speed (Young, Salmon, & Lenne, 2013). Lighting condition also likely affects aberrant driving behaviors. Several studies reported an increased crash risk in darkness (e.g., Johansson, Wanvik, & Elvik, 2009). Night driving may correlate with an increase in driving errors and crash risk due to fatigue (e.g., Anund et al., 2008) or obstructed visibility (e.g., Owens & Sivak, 1996). On the other hand, especially for young drivers, accidents in darkness may be due to a tendency for more risky driving behavior during evenings and nights rather than a matter of visibility or fatigue (e.g., Clarke, Ward, Bartle, & Truman, 2006; Clarke, Ward, & Truman, 2005). Further, adverse weather conditions such as snow and rain have also been found to increase crash rates (Qiu & Nixon, 2008). In light of these results, it appears likely that demands related to the driving context affect aberrant driving behaviors.

However, as Young and Regan (2013) point out, drivers can engage in behavioral adaptation in the form of compensatory or self-regulatory behaviors, either to prepare themselves for distraction or to compensate for its effects. Tivesten and Dozza (2015) investigated the influence of the driving context on drivers' decisions to engage in particularly risky phone related tasks such as texting, dialing, and reading. They found that these tasks were more common while the vehicle was stationary as opposed to moving at high speeds. Moreover, the drivers used information about the current and upcoming driving context to decide when to engage in these phone tasks. Similar to these findings, numerous investigations have suggested that drivers are capable of adapting their behavior (e.g., increase of headway, reduction of speed) to meet the increased demands of engaging in secondary tasks while driving (e.g., Caird et al., 2014; Platten, Milicic, Schwalm, & Krems, 2013).

It remains unclear how the interrelationship of driving task demands (weather, lighting, traffic density, intersection type) and driver distraction (secondary tasks) affects aberrant driving behaviors under real driving conditions.

1.2.3. Driving anger

Several studies have indicated that anger is among the most important factors involved in aggressive and unsafe driving behavior (e.g., Dahlen, Martin, Ragan, & Kuhlman, 2005; Zhang & Chan, 2016). Furthermore, regardless of the possible triggering of aggressive behavior, driving anger results in increased cognitive load. Driving anger can be defined as a situationspecific emotional construct comprising anger-related feelings and thoughts that occur while driving (Nesbit & Conger, 2011). These feelings and thoughts could withdraw attention from the driving related information processing necessary for safe vehicle operation and might therefore cause driving errors as opposed to deliberate violations. In fact, research has demonstrated adverse effects of anger on driving performance (Jeon, Walker, & Yim, 2014; Stephens, Trawley, Madigan, & Groeger, 2013; Zhang & Chan, 2016). Conversely, other research has reported weak or non-significant relationships between driving anger and errors (Nesbit, Conger, & Conger, 2007; Zhang et al., 2015).

As driving anger is a crucial factor involved in unsafe and aggressive driving, it is necessary to investigate the emergence of errors and violations under particular consideration of this emotional impairment. In addition, other emotional states can affect driver performance. Chan and Singhal (2013), for example, reported that emotion-based distraction, both with positive and negative valence, can impact driving performance by diverting attention away from the primary driving task to the emotional content. Therefore, research on the effects of other emotions such as happiness, sadness, and surprise needs to be conducted.

1.3. Presence of one or more passengers

It is generally accepted that people change their behavior in social presence (Bond & Titus, 1983). Several investigations reported that the presence of one or more passengers influences driving performance. However, the respective findings of how it influences performance are contradictory.

On the one hand, numerous studies reported that drivers displayed safer behaviors when they were accompanied by passengers, such as a lower likelihood of performing risky phone related secondary tasks (e.g., Tivesten & Dozza, 2015), of committing traffic violations (e.g., Rosenbloom & Perlman, 2016), of driving errors (e.g., Ross, Jongen, Brijs, Brijs, & Wets, 2016) and of crash risk (e.g., Vollrath, Meilinger, & Krüger, 2002). On the other hand, research has also indicated that passenger presence correlates with an increase of driving errors and crash risk, as well as a decrease of hazard avoidance due to cognitive distraction, particularly for young drivers (for a review, see Durbin, McGehee, Fisher, & McCartt, 2014).

Consequently, more work is needed to establish how passenger presence affects aberrant driving behaviors under naturalistic driving conditions.

1.4. Driver characteristics

There is a large body of evidence that driver characteristics, such as driver sex, age, experience, and personality have a great impact on both errors and violations.

The meta-analysis by Winter and Dodou (2010), based on 174 studies using the Driver Behaviour Questionnaire (DBQ; Reason et al., 1990), found that males reported fewer errors and more violations than female drivers. Driver age is another variable consistently found to affect driving performance. The meta-analysis by Winter and Dodou (2010) reported a negative correlation between age and self-reported errors and violations. It has been well established that young drivers are more frequently involved in traffic accidents than drivers in other age groups (e.g., Preusser, 2003). This could be, for example, due to a riskier driving style or an underestimation of risks compared to older drivers (e.g., Bina, Graziano, & Bonino, 2006; Hatfield & Fernandes, 2009). However, it has also been suggested that older drivers have a decreased ability to share attention between two concurrent tasks and therefore have a greater risk of committing errors when performing secondary tasks while driving compared to younger drivers (Young et al., 2007). Gstalter and Fastenmeier (2010) examined the error and violation probabilities at intersections in a field study, finding that older drivers demonstrated significantly more aberrant driving behaviors than younger drivers.

Driving experience has also been found to affect aberrant driving behaviors, although differently than driver age. Winter and Dodou (2010) reported a positive correlation between driving experience and self-reported errors and violations. Therefore, violations decreased with age while increasing with experience, which was also observed in a longitudinal study by Wells, Tong, Sexton, Grayson, and Jones (2008).

Contrary to effects related to varying factors such as age and experience, there is evidence that there are permanent individual differences in driver behavior due to temperament and personality factors. Summala, Rajalin, and Radun (2014) conducted a 24-year follow-up study based on an on-road sample of offenders and control drivers. They reported that the offenders had more entries in driver records compared to the control group just as a quarter of century ago, indicating that individual differences in driver behavior persist for decades. An example of a personality factor possibly causing permanent individual differences is sensation seeking. It has been found to predict risky driving behavior, violations, as well as accidents in numerous investigations (e.g., Bachoo, Bhagwanjee, & Govender, 2013; Dahlen & White, 2006; Delhomme, Chaurand, & Paran, 2012). According to Zuckerman (1994), sensation seeking is ''a trait defined by the seeking of varied, novel, complex, and intense sensations and experiences, and the willingness to take physical, social, legal, and financial risks for the sake of such experience" (p. 27). Therefore, it is assumed that drivers who score highly on this trait engage in risky driving to trigger the type of sensations and experiences that they find pleasurable (Dahlen & White, 2006). Driving anger is another personality factor which has been associated with voluntary risky behaviors in numerous studies (e.g., Delhomme et al., 2012; see section 1.2.3 for more details).

Furthermore, recent research suggests that temperament and personality factors also determine whether an individual is willing to engage in risky secondary tasks while driving. According to Zhao, Reimer, Mehler, D'Ambrosio, and Coughlin (2013), individuals who frequently use cell phones while driving have a higher crash rate not only because of their cell phone use, but also due to a higher acceptance of risk taking while driving and a greater tendency to engage in risky driving behaviors independently of cell phone use. Zhao et al. (2013) found that drivers who reported frequent cell phone use while driving scored higher in self-reported driving violations compared to those who claimed not to use a cell phone regularly while driving. Moreover, Beck, Yan, and Wang (2007) demonstrated that individuals using cell phones while driving were more likely to have received traffic citations, to drive while drowsy, drive more aggressively, drive 20 mph over the speed limit, and run stop signs or red lights. These findings indicate that there are underlying factors inducing a greater willingness of drivers to engage in both risky driving behaviors and hazardous secondary tasks (Zhao et al., 2013).

Driver characteristics clearly have a great impact on the way in which drivers behave. However, little evidence exists on how these different variables (e.g., sex, age, experience, and personality) affect aberrant driving behaviors under real driving conditions and how they interrelate with driving task demands and driver distractions.

1.5. Investigative approach

It is important to note that a major part of the previous findings on the cause of aberrant driving behaviors stem from questionnaire studies, relying on self-reported error and violation frequencies. Given that self-reported errors and violations cannot be verified, it is essential to investigate the complex interrelationship between potential risk factors for aberrant driving behaviors and their combined effect on errors and violations under real driving conditions. In comparison to driving simulation and field experiments, naturalistic driving studies (NDSs) offer researchers a unique view into the vehicle and an invaluable opportunity to study driver performance and behavior in the real world with actual consequences and risks (Carney, McGehee, Harland, Weiss, & Raby, 2015). NDSs allow the evaluation of driver behavior by objectively collecting in situ driving data continuously for an extended period of time. This enables the analysis of environmental, situational, and individual factors recorded in the seconds prior to real live errors or violations. The largest and most comprehensive naturalistic driving study to date is the second Strategic Highway Research Program (SHRP 2; Campbell, 2012), therefore representing the current gold standard for driver distraction data. The analysis of this data is needed to improve understanding of potential risk factors for aberrant driving behaviors and their actual effects on driver behavior in real traffic.

1.6. The current study – aims and hypotheses

The objective of the present investigation was to examine the variables contributing to driving errors and traffic violations based on naturalistic driving data. Research has identified various factors likely to affect aberrant driving behaviors and therefore crash risk. However, it remains unclear which of these factors poses the greatest risk under naturalistic driving conditions and whether observed risks change when considering several contributing variables. For this reason, 10,800 sixsecond trip segments of SHRP 2 video footage were analyzed in the current study with regard to the effects of secondary and driving task demands, emotional impairment, passenger presence, and driver characteristics on aberrant driving behaviors. In addition, this study attempted to identify key factors affecting drivers' willingness to perform common secondary tasks while driving, which possess differing degrees of visual, manual, or cognitive demands as well as different degrees of crash risk. All the following hypotheses have been previously examined in simulation, field, or questionnaire studies. However, to the best of the authors' knowledge, this study is the first to investigate these interdependencies based on naturalistic driving data and therefore provides new insights into the variables that contribute to or mitigate dangers in traffic.

(1) Consistent with previous research, we hypothesized that high driving task demands, such as rain, high-density traffic, or intersections would affect aberrant driving behaviors by increasing the number of errors.
(2) We also assumed that drivers would adapt their behaviors to the driving task demands due to
- (a) fewer committed violations and risky secondary tasks performed during high driving task demands and
- (b) more frequently performed risky secondary tasks in driving contexts such as low-traffic periods, at red lights or in parking lots.
(3) We hypothesized that drivers would commit more violations during the late evening and nighttime compared to daytime.
(4) Regarding the secondary task demands, we predicted that visually and visual-manually distracting secondary tasks, which have been found to considerably increase crash risk, would result in more errors.
(5) We did not expect a connection between cognitively distracting secondary tasks and error occurrences.
(6) In line with this, we assumed that the cognitive demands connected with emotional impairments would not increase the number of errors.
(7) We predicted that driving anger would be associated with an increase in traffic violation frequency.
(8) We hypothesized that drivers would commit fewer violations in the presence of one or more passengers.
(9) Regarding driver characteristics, we assumed that while sex would not affect aberrant driving behaviors,
(10) age/driving experience would be negatively related to errors and positively related to violations.
(11) We expected that drivers' past behavior would predict their current behavior in terms of the frequency of committed violations.
(12) We predicted that drivers' past violations and crash involvements, sensation seeking score, as well as committed violations coded in this study would predict the performance of risky visually or visual-manually distracting secondary tasks.
(13) Lastly, we hypothesized a positive relationship between drivers' degree of sensation seeking and the number of aberrant driving behaviors.

2. Method and materials

2.1. Data

Naturalistic driving data from the second Strategic Highway Research Program (SHRP 2) was analyzed in this study. The SHRP 2 is the largest study of its kind, including data from 35 million vehicle miles and 5.4 million trips. Data from instrumented vehicles was collected from more than 3500 U.S. participants during a 3-year period (Dingus et al., 2016; Victor et al., 2015). Recorded data consisted of driving parameters such as speed, acceleration, and braking, all vehicle controls, forward radar, and lane position. In addition, video views forward, to the rear, on the driver's face, and on the dashboard were captured (Blatt et al., 2015). A more detailed description of the data in SHRP 2 can be found at the study's data access website ''InSight" (see Fig. 1 for a summary of the data and the sampling procedure; Precht, Keinath, & Krems, 2017).

The subset of SHRP 2 data used in this study consisted of 108 different ten-minute trip segments. It also provided the basis for an analysis of the effects of driving anger, anger triggers, and anger intensity on driver behavior, which is described in more detail in Precht et al. (2017). ''Driving anger" is supposedly one of the key factors contributing to unsafe driving. Therefore, half of the trip segments analyzed were selected based on the following data specifications (defined in the SHRP 2 event data dictionary, available at https://insight.SHRP2nds.us/) to allow for the examination of the interrelations between all crucial factors for unsafe driving, including driving anger:

(1) The trip duration is greater than or equal to 10 min.
(2) The locality is either ''Moderate Residential", ''Business/Industrial" or ''Interstate/Bypass/Divided Highway with no traffic signals".
(3) The traffic density is either ''Level-of-service A1: Free flow, no lead traffic", ''Level-of-service A2: Free flow, leading traffic present", ''Level-of-service B: Flow with some restrictions", or ''Level-of-service C: Stable flow, maneuverability and speed are more restricted".
(4) The driver impairment ''Angry" is present.

The data specifications were based on six-second trip segments assessed and provided by VTTI. The locality and traffic density specifications were intended to ensure a certain level of data homogeneity. The requested data specifications resulted in 54 trip segments in which the drivers exhibited anger.

As most studies working with naturalistic driving data analyzed trip segments lasting less than a minute (e.g., Dingus et al., 2016; Hickman et al., 2010; Klauer et al., 2006; Olson et al., 2009; Young, 2015) there is little information on naturalistic driving during longer periods of time. On this account, in the present study, trip segments lasting for ten minutes were

Fig. 1. Data and sampling procedure summary.

coded, making it possible to examine the effects of driver characteristics, driving task demands, passengers, and driver behaviors over a longer period of time; thus, providing the basis for a deeper understanding of the interrelations that influence driving performance and road safety.

Furthermore, in order to be able to also analyze the impacts of driver characteristics independently of a coincidental emotional impairment, the second half of the data subset used in this study consisted of matched trip segments for the ''anger group" segments. These segments were intended to be as similar as possible to the associated anger group segments, the only difference being that emotional impairment was not present. Thus, VTTI matched the baselines individually and manually for each event (see Precht et al., 2017 for more details).

2.1.1. Final data sample

The final data set comprised (1) 54 ten-minute segments in which the drivers exhibited anger and (2) 54 ten-minute segments of the same drivers not exhibiting anger. The 108 trip segments stem from 38 different drivers. The video reduction data provided by VTTI is categorized according to its ''event severity", such as ''crash", ''near-crash", ''crash-relevant", and baseline. Both the trip segments including driving anger and the matched trip segments were selected from the same population, which included all events and all event severities. As a result of the respective data specifications, the final data set included 24 near-crash, 3 crash, and 81 baseline events (two of the three crash events involved hitting the curb; see Fig. 1 for a summary of the sampling procedure). It is important to note, that the vast majority of both crashes and near crashes in the current study were caused or provoked by driving anger and deliberate infringements, which were mostly aggressive and targeted at other road users. A substantial proportion of the near-crash events in the coded data sample were deliberate ''brake checks" (see Precht et al., 2017 for more details).

2.2. Variables and data coding

Fig. 2 depicts a summary of the variables investigated in this study and the study approach.

2.2.1. Dependent variables

Appendix A shows a list of the coding scheme used including all variables, definitions, and examples. Driver behavior was the main dependent variable and was operationalized through the variables: (1) driving error, (2) traffic violation, (3) unclassified unsafe driving behavior, (4) other driver behavior and (5) aggressive expression. Most of the driver behaviors were coded based on the original ''SHRP 2 Researcher Dictionary for Video Reduction Data" (Version 3.4, pp. 46–51; available at https://insight.SHRP2nds.us/). The aggressive expressions were coded via 15 variables based on clearly observable items of the 49-item Driving Anger Expression Inventory (DAX; Deffenbacher, Lynch, Oetting, & Swaim, 2002). However, in the current study, only the aggressive expression variables, which were categorized as a violation (see Section 2.2.3), were included in the analyses.

Fig. 2. Summary of variables and study approach. Aberrant driving behaviors were analyzed as a function of secondary and driving task demands, driver characteristics, passenger presence, and emotional impairments; common secondary tasks were analyzed as a function of driving task demands, driver characteristics, passenger presence, and traffic violations.

In addition, specific secondary tasks associated with either visual-manual, cognitive-manual, mainly cognitive, or mainly visual demands of various degrees of crash risk were dependent variables in this examination, including: (1) ''Cell phone, texting/browsing/dialing, hand-held", (2) ''Adjusting/monitoring climate control/radio", (3) ''External distraction", (4) ''Cell phone, talking/listening, hand-held", (5) ''Passenger in adjacent seat, talking without looking at passenger", and (6) ''Passenger in adjacent seat, talking while looking at passenger". Three of these secondary tasks were selected as representatives of visual/visual-manual distractions with either high (texting/browsing/dialing; external distraction) or moderate crash risk (climate control/radio). The other tasks were selected as representatives of cognitive distractions with either moderate (cell phone, talking/listening) or low crash risk (talking with passenger). However, talking while looking at the passenger constitutes a mainly visually distracting subtask of the cognitively distracting task talking with passenger. These tasks were selected given their high prevalence and their respective crash risks reported by Dingus et al. (2016), who performed the first largescale, crash-only analysis of risk factors based on naturalistic driving data (SHRP 2 NDS).

2.2.2. Independent variables

The independent variables used in this study were: (1) secondary task demands, (2) driving task demands, (3) emotional impairment, (4) presence of passenger(s), and (5) driver characteristics (see Appendix A for all variable definitions). The secondary task demands were operationalized through the secondary tasks performed by the drivers. The secondary tasks were coded based on the original ''SHRP 2 Researcher Dictionary for Video Reduction Data" (Version 3.4, pp. 53–60; available at https://insight.SHRP2nds.us/). To determine the proportion of visual distraction in the passenger related secondary tasks ''Passenger in adjacent seat - interaction" and ''Passenger in rear seat – interaction", these original SHRP 2 categories were replaced by the following categories: ''Passenger in adjacent/rear seat – talking without looking at passenger", ''Passenger in adjacent/rear seat – talking while looking at passenger", and ''Passenger in adjacent/rear seat – any interaction but talking". Furthermore, working with VTTI data coding experts, some SHRP 2 coding categories were combined into a higher-level secondary task category due to their strong relation with each other and shared similar task demands.

The driving task demands analyzed included weather (''no adverse conditions", ''raining"), surface condition (''dry", ''wet", ''snowy"), lighting (''dawn", ''daylight", ''dusk", ''darkness"), traffic density (''Level-of-service A1 to B"/''smooth traffic", ''Level-of-service C to D"/''moderate traffic", ''Level-of-service E to F"/''heavy traffic"), and intersection influence (''traffic signal/stop sign", ''uncontrolled", ''interchange", ''parking lot", ''no intersection influence"). All driving task demand variables were coded based on the original ''SHRP 2 Researcher Dictionary for Video Reduction Data" (with the exception of the variables interchange and parking lot, which were slightly modified). Moreover, consulting with VTTI data coding experts, some of the ''traffic density" and ''intersection influence" SHRP 2 coding categories sharing similar driving demands were combined to higher-level categories (see Appendix A for all variable definitions).

Four categories of driver emotions (''happy", ''angry-frustrated", ''sad", ''surprised") were coded based on a coding scheme developed by Fitch, Hanowski, Burnett, and Crundall (2013; see Appendix A, 1.1 - 1.7).

Regarding passenger presence, the two categories ''no passenger present" and ''one or more passengers present" were investigated. Driver characteristics analyzed included age, sex, sensation seeking, driving experience (''annual mileage last 5 years", ''years driving"), and the drivers' past behavior (''number of moving/traffic violations in past three years", ''number of crashes in past three years"), which were collected via a ''Sensation Seeking Scale Survey", a ''Driver Demographic Questionnaire", and a ''Driving History Questionnaire" (see https://insight.SHRP2nds.us/ for more information).

2.2.3. Data coding procedure

The following four video views were coded for each ten-minute trip segment: (1) forward, (2) rear, (3) on the driver's face, and (4) a view of the driver's interactions with the vehicle's steering wheel and center console. The video coding occurred at VTTI's secure data enclave. The data reduction software tool used was developed by VTTI, and the data was coded in collaboration with a professional VTTI data reductionist. The start and end time of each dependent and independent variable were coded. Each rater coded different variables, and therefore both raters coded all segments. The highly trained and experienced VTTI data reductionist coded the original SHRP 2 driver behavior and secondary task categories. (See Precht et al., 2017 for more detailed information regarding the coding of the emotion variables.)

Following completion of the data reduction procedure, coded driver behaviors were categorized according to Reason et al.'s (1990) differentiation between a violation and an error. The classification of aberrant driving behaviors as either deliberate or a misjudgment was mostly straightforward with the aid of video recordings and continuous data. Several indications, such as the driver's direction of gaze, relation to other vehicles, adherence to traffic laws, and driving data (e.g., speed, acceleration, deceleration) were evaluated. Some driver behaviors such as ''failed to signal" and ''exceeded speed limit" were categorized as a violation unless there was a reason to think it was an error, as these driver behaviors are representing traffic laws. In addition, the classification of driver behaviors as either deliberate or a misjudgment is already inherent to a large proportion of the SHRP 2 driver behavior categories (e.g., ''signal violation, apparently did not see signal", ''signal violation, intentionally disregarded signal", ''signal violation, tried to beat signal change"). When a driver behavior considered unsafe could not be assigned to either violation or error according to Reason et al.'s (1990) definitions, it was categorized as unclassified unsafe driving behavior (behavior was neither error nor violation but still unsafe, or it was ambiguous whether behavior was deliberate or a misjudgment). Driver behaviors not identified as a violation, error, or unsafe were categorized as other driver behavior. Other driver behaviors and unclassified unsafe driving behaviors were excluded from the analyses.

The ten-minute trip segments were recoded to allow for an appropriate treatment of the variables and their interrelations in the analyses. Given the wide variety of the occurrences of the variables during the trips, each six-second segment of the 108 ten-minute trip segments was coded with regard to the frequency of occurrence of the variables analyzed in the current study. Therefore, 10,800 six-second trip segments were included in the analyses.

2.3. Data analysis

2.3.1. Analysis of aberrant driving behaviors

To evaluate the factors contributing to traffic violations and driving errors, the frequency of these driver behaviors was analyzed as a function of secondary and driving task demands, driver characteristics, passenger presence, and emotional impairments. As a result, the dependent variables violation and error consisted of discrete counts, thus the assumption of a normal distribution was inappropriate (e.g., Coxe, West, & Aiken, 2009; Gardner, Mulvey, & Shaw, 1995). More appropriate distributions for this type of data include the Poisson and negative binomial distribution. Additionally, in the current study, at least two trip segments were sampled from the same driver. Driver behaviors exhibited by the same driver may thus be correlated and violate the independence assumption of traditional regression models (Hubbard et al., 2010). Generalized Linear Mixed Models (GLMM) with a log link function were applied to take account of the Poisson/negative binomial distributed data with repeated measures. The application of GLMM techniques allows addressing elements such as nested count data (Zuur, Ieno, Walker, Saveliev, & Smith, 2009). They combine the properties of both linear mixed models (which incorporate random effects and therefore the autocorrelation of driver behavior within each driver in the current study) and generalized linear models (which analyze non-normal data by using link functions, such as Poisson or binomial distributed data; Bolker et al., 2009).

The glmmADMB package (Fournier et al., 2012; Skaug, Fournier, Bolker, Magnusson, & Nielsen, 2016) in R 3.3.1 (R Core Team, 2016) was used to investigate how environmental, situational, and driver factors predicted aberrant driving behaviors. The response variables consisted of the coded error and violation frequencies in each six-second segment and followed a Poisson distribution. Eight separate GLMMs were fit. The driver was treated as a random effect to consider the autocorrelation of driver behavior within driver. Fixed effects included driving task demands (traffic density; intersection influence; weather; lighting), driver characteristics (years driving; sex; sensation seeking score; annual mileage last five years; number of moving/traffic violations in past three years; number of crashes in past three years), secondary task demands (''talking/ singing, audience unknown"; ''dancing"; ''passenger in adjacent seat, talking without looking at passenger"; ''passenger in adjacent seat, talking while looking at passenger"; ''object dropped/reaching for object, other"; ''cell phone, talking/listening, hand-held"; ''cell phone, texting/browsing/dialing, hand held"; ''adjusting/monitoring climate control/radio"; ''external distraction"; ''reaching for food-related or drink-related item/eating/drinking"; ''reaching for personal body-related item/personal hygiene/removing/adjusting clothing/jewelry/contact lenses or glasses"; ''reaching for/lighting/smoking/ extinguishing cigar/cigarette"), passenger presence, and driver emotions (happy; angry/frustrated; surprised; sad).

Age-group and years driving were highly correlated variables. Years driving was included into the models given the enhanced fit over age-group. Moreover, weather and surface condition were highly correlated and the analyses only included weather. Finally, the lighting category dawn was combined with the category daylight as dawn occurred too infrequently to be analyzed separately. Furthermore, only the secondary tasks occurring at least once simultaneously with a driving error were included in the analyses as it was impossible to fit models with all secondary tasks observed in the current study (see Table B1). Moreover, fewer fixed effects could be included in the models predicting errors due to the fewer number of errors than violations in this study. For this reason, number of moving/traffic violations in past three years and sensation seeking were factors excluded from the respective models as these variables did not have significant effects on error and were less likely than other factors to predict driving errors. Apart from these modifications, the fixed effects of the error and violation predicting GLMMs were kept consistent to allow comparability.

To observe the unique contribution of each set of variables in explaining the aberrant driving behaviors, four separate models predicting errors and four separate models predicting violations were applied. In order to control for the driving task demands, these variables were entered at first. The second model included driving task demands and driver characteristics. The third model additionally included secondary task demands and passenger presence. Finally, the fourth model included all four sets of predictor variables.

As the different sets included related variables such as the passenger related factors, all variable sets were estimated in various model versions. The significance level of the remaining variables and the respective coefficient magnitudes were stable when one or more of the related variables were excluded from the analyses. Therefore, these variables were included in the analyses.

2.3.2. Analysis of secondary tasks

Moreover, the key as well as accompanying factors contributing to the drivers' willingness to perform mainly visually or cognitively distracting secondary tasks while driving were evaluated. Therefore, the presence or absence of six secondary tasks was analyzed as a function of driving task demands, driver characteristics, passenger presence, and the four most frequently committed violations within the current study (illegal/improper passing on right; signal/stop sign violation; failed to signal/improper signal; exceeded speed limit). GLMMs with a logit link function were applied to take account of the binomial distributed data with repeated measures.

The glmmADMB package (Fournier et al., 2012; Skaug et al., 2016) in R 3.3.1 (R Core Team, 2016) was used to investigate how environmental, situational, and driver factors predicted current secondary tasks, which have been associated with different degrees of crash risk. The response variables were (1) cell phone, texting/browsing/dialing, hand-held, (2) adjusting/monitoring climate control/radio, (3) external distraction, (4) cell phone, talking/listening, hand-held, (5) passenger in adjacent seat, talking without looking at passenger, and (6) passenger in adjacent seat, talking while looking at passenger. Twelve separate GLMMs were fit. The dependent variables consisted of the presence or absence of the coded secondary tasks in each sixsecond segment and followed a binomial distribution. Driver was treated as a random effect to take into account the autocorrelation of driver behavior within driver. Fixed effects were driving task demands (traffic density; intersection influence; weather; lighting), driver characteristics (years driving; sex; annual mileage last five years; number of moving/traffic violations in past three years; number of crashes in past three years), passenger presence, and the simultaneously committed violations: illegal/improper passing (illegal passing on right/illegal passing (but on right)/other improper or unsafe passing), signal/stop sign violation (signal violation, tried to beat signal change/stop sign violation, ''rolling stop"/stop sign violation, intentionally ran stop sign at speed), failed to signal/improper signal, and exceeded speed limit.

As passenger related secondary tasks only occurred during smooth traffic flow (''Level-of-service A1 to B") and passengers were never present during heavy traffic (''Level-of-service E to F") in the current study, the variable traffic density was excluded from the respective models. Furthermore, the secondary task cell phone, talking/listening, hand-held was never performed in this study under rainy conditions, thus weather was not included in the respective models. Traffic density and weather were excluded from the respective models given external distractions only occurred during smooth traffic and nice weather. Apart from these modifications, the fixed effects of the secondary tasks predicting GLMMs were kept consistent to allow comparability.

The goal was to analyze how both the self-reported number of violations in past three years and the most frequently committed violations collected in the current study predicted secondary task performance. These variables were highly correlated and affected each other when included in the same model, necessitating the application of two separate models for each secondary task. The first model contained all fixed factors apart from the violations committed within this study, whereas the second model included all fixed factors.

3. Results

3.1. Driver characteristics

The 38 drivers (18 female) were between 16 and 79 years of age (N = 13 were between 16 and 24 years; N = 10 were between 25 and 34 years; N = 9 were between 35 and 59 years; N = 6 were between 60 and 79 years). Participants' vehicles included 20 ''Cars", 3 ''Pickup Trucks", 13 ''Crossover SUVs", and 2 ''Vans/Minivans". The participants' self-reported average annual mileage over the past five years was between less than 5000 miles and more than 30,000 miles (N = 11 were between less than 5000 and 10,000 miles; N = 22 were between 10,000 and 25,000 miles; N = 5 were between 25,000 and more than 30,000 miles). Moreover, they stated that they had been driving for 2 to 60 years (M = 20.41, SD = 17.19). The participants' sensation seeking scores ranged from 4 to 28 (M = 15.13, SD = 5.94; possible value range of sensation seeking score: 0– 40). Furthermore, 20 participants stated that they never received a moving or traffic violation in the past three years, 14 received one moving or traffic violation in the past three years, and 4 received two or more. Twenty-five participants stated that they had not experienced a crash that was, or could have been, police-reported in the past three years and 13 participants claimed that they had experienced one or more police-reported crashes.

3.2. Driving task demands

Table 1 depicts a summary of the descriptive statistics for the situational and environmental variables coded in the current study (see Appendix A for all variable definitions).

3.3. Secondary tasks and passenger presence

A summary of the descriptive statistics for the secondary task variables effectively coded in the current study is given in Table B1 (see Appendix A for all variable definitions). It must be noted that the secondary tasks were coded following the SHRP 2 reduction standard. The tasks were therefore coded as interrupted, when an interruption lasted for 5 s or longer. For this reason, the respective reported task durations can include interruptions of a maximum of 5 s. Fig. 3 gives an overview of the ten most commonly performed secondary tasks in this study. One or more passengers were present in 34 of the 108 coded trips.

3.4. Emotional impairment

Fig. 4 shows the total number of the six-second segments exhibiting the emotion variables angry/frustrated, happy, sad, or surprised. Most of the observed ''surprises" in the current study were caused by abrupt braking maneuvers of preceding vehicles (''brake checks").

3.5. Aberrant driving behaviors

A summary of the descriptive statistics for the aberrant driving behavior variables coded in the current study is given in Table B2 (see Appendix A for all variable definitions). Of all coded aberrant driving behaviors, 317 were categorized as vio-

Table 1 Total number and duration of coded driving task demands (related to ten-minute segments).

Situational/environmental variables		Total number (M, SD)	Total duration in sec (M, SD)
Traffic density	Level-of-service A1 to B (smooth traffic)	123 (1.14, 0.44)	61565.97 (570.06, 85.08)
	Level-of-serv. C to D (moderate traffic)	35 (0.32, 0.88)	2172.04 (20.11, 57.19)
	Level-of-service E to F (heavy traffic)	18 (0.17, 0.48)	1062.00 (9.83, 34.03)
Intersection influence	Traffic signal/stop sign	273 (2.53, 2.08)	8417.08 (77.94, 77.30)
	Uncontrolled	21 (0.19, 0.54)	258.46 (2.39, 9.60)
	Interchange	62 (0.57, 0.79)	2241.17 (20.75, 34.84)
	Parking lot	37 (0.34, 0.61)	3408.14 (31.56, 82.56)
	No intersection influence	–a	11400.00 (105.56, 229.52)
Lighting	Dawn	1 (0.01, 0.10)	600.00 (5.56, 57.74)
	Daylight	88 (0.81, 0.39)	52638.20 (487.39, 233.96)
	Dusk	7 (0.06, 0.25)	3464.97 (32.08, 129.40)
	Darkness (lighted)	15 (0.14, 0.35)	8096.83 (74.97, 196.84)
Weather	No adverse conditions	103 (0.95, 0.21)	61467.61 (569.14, 128.45)
	Raining	7 (0.06, 0.25)	3332.39 (30.86, 128.45)
Surface condition	Dry	103 (0.95, 0.21)	61482.18 (569.28, 128.40)
	Wet	6 (0.06, 0.23)	3247.11 (30.07, 128.38)
	Snowy	1 (0.01, 0.10)	70.71 (0.65, 6.80)

^a The total number of ''no intersection influences" was not coded in relation to ten-minute segments.

Fig. 3. Total number for each of the ten most common secondary tasks effectively coded in the current study, related to ten-minute segments.

Fig. 4. Total number of the six-second segments exhibiting the emotion variables angry/frustrated, happy, sad, or surprised.

lation and 54 were categorized as error. Figs. 5 and 6 show the ten most commonly committed violations and the errors coded in this study.

3.6. Predictors of aberrant driving behaviors

Fig. 7 depicts a summary of the aberrant driving behaviors analysis approach.

3.6.1. Predictors of violations

Table 2 presents the findings from the four conducted GLMMs predicting violations. The complete model (Model 4) shows that violations significantly decreased on average by a factor of 0.25 in moderate traffic compared to driving in smooth traffic when analyzing driving task demands. Moreover, violations significantly decreased on average by a factor of 0.47 while passing through an interchange and by a factor of 0.36 while waiting at/passing a traffic light or stop sign compared to not driving through an intersection. Furthermore, violations significantly decreased by a factor of 0.32 when participants drove in nice weather (no adverse condition) compared to rainy weather. Finally, violations significantly decreased on average by a factor of 0.13 when participants drove during nighttime (darkness, lighted) compared to daytime (dawn-daylight).

Most of the driver characteristic variables analyzed had no significant effect on violations. However, when participants stated having had two or more moving or traffic violations in the past three years, the actually committed violations significantly increased by a factor of 12.42 compared to participants who had stated that they have had no moving or traffic vio-

Fig. 5. Total number for each of the ten most common violations effectively coded in the current study, related to ten-minute segments.

Fig. 6. Total number for each of the errors effectively coded in the current study, related to ten-minute segments.

Fig. 7. Aberrant driving behaviors were analyzed as a function of secondary and driving task demands, emotional impairments, driver characteristics, and passenger presence.

lations in the past three years. Therefore, the number of previous driver violations committed best predicted the number of violations committed in the current study.

Violations significantly decreased by a factor of 0.25 when drivers were accompanied by one or more passengers compared to when drivers drove alone.

Finally, the emotion variables angry/frustrated, surprised, and sad had significant effects. Specifically, violations significantly increased by a factor of 2.02 whenever drivers exhibited anger/frustration, by a factor of 2.66 whenever drivers exhibited surprise, and by a factor of 2.07 whenever drivers exhibited sadness.

3.6.2. Predictors of errors

The findings from the four GLMMs predicting errors are presented in Table 3. Regarding the analyzed driving task demands, the complete model (Model 4) shows that driving errors significantly increased on average by a factor of 4.79 when drivers were passing through an interchange compared to driving uninfluenced by intersections. The driving task demands traffic density, weather, and lighting did not significantly affect errors.

Although there were not any significant effects on driving errors regarding the analyzed driver characteristics, there were several significant effects on driving errors for the secondary task demands. Whenever drivers talked or sang without passenger presence (talking/singing, audience unknown), errors significantly decreased on average by a factor of 0.33. Moreover, errors significantly increased on average by a factor of 3.29 whenever drivers dropped and then immediately picked back up or reached for an object not described in any other SHRP 2 coding scheme category (object dropped/reaching for object). Whenever drivers pressed buttons or activated a touch screen on a cell phone to create/send a text message, to browse, or to dial a number (cell phone, texting/browsing/dialing, hand held), errors significantly increased on average by a factor of 6.74. Furthermore, errors significantly increased on average by a factor of 2.93 whenever drivers tracked an external distraction. Neither the other secondary tasks nor the presence of one or more passengers significantly affected errors.

3.7. Predictors of secondary tasks

Fig. 8 depicts a summary of the secondary tasks analysis approach.

3.7.1. Passenger in adjacent seat – talking without looking/while looking at passenger (low crash risk)

Table 4 demonstrates the findings from the four conducted GLMMs predicting the secondary tasks passenger in adjacent seat – talking without looking at passenger and passenger in adjacent seat – talking while looking at passenger. The frequency of talking to the adjacent passenger without looking at him/her significantly decreased on average by a factor of 0.30 while driving or parking in a parking lot, by a factor of 0.65 while waiting at/passing a traffic light or stop sign, and by a factor of 0.25 while passing an uncontrolled intersection compared to not driving through intersections. In contrast, the frequency of talking to the adjacent passenger while looking at him/her significantly increased on average by a factor of 3.88 while driving or parking in a parking lot and by a factor of 6.19 while waiting at/passing a traffic light or stop sign compared to not driving through intersections.

Moreover, frequencies of talking to the adjacent passenger without looking at him/her significantly decreased on average by a factor of 0.07 during nice weather (no adverse condition) compared to rainy weather. On the other hand, the amount

Table 2 GLMMs predicting violations.

		Model 1 (AIC = 6758.9)		Model 2 (AIC = 6765.7)		Model 3 (AIC = 6669.3)		Model 4 (AIC = 6549.6)
Fixed Effect		Exp(b)	z-value	Exp(b)	z-value	Exp(b)	z-value	Exp(b)	z-value
Driving task demands
Traffic density	Moderate Traffica	0.26***	6.50	0.26***	6.50	0.28***	6.25	0.25***	6.81
	Heavy Traffica	0.06***	4.88	0.06***	4.88	0.05***	5.05	0.05***	5.26
IntersectionInflb	Interchangec	0.43***	5.38	0.43***	5.39	0.42***	5.50	0.47***	4.77
	Parking Lotc	0.11***	6.36	0.11***	6.36	0.12***	6.27	0.12***	6.21
	TraffSignal/Stopc/d	0.35***	8.87	0.35***	8.86	0.36***	8.59	0.36***	8.39
	Uncontrolledc	0.67	0.68	0.67	0.69	0.68	0.65	0.71	0.58
Weather	No adverse conde	0.28***	5.06	0.27***	5.11	0.31***	4.70	0.32***	4.49
Lighting	Duskf	1.79***	4.92	1.79***	4.91	1.55***	3.63	1.17	1.31
	Darkness, lightedf	0.12***	7.84	0.12***	7.76	0.13***	7.53	0.13***	7.76
Driver characteristics
Years Driving (Age)		–	–	1.00	0.08	1.01	0.32	1.01	0.31
Sex	Maleg	–	–	2.94	1.74	3.22	1.88	3.21	1.89
Annual Miles	10,000mih	–	–	1.17	0.14	0.89	0.11	0.86	0.14
	10,000–25,000mih	–	–	1.45	0.36	1.21	0.18	1.19	0.16
Sensation seeking		–	–	1.06	0.99	1.03	0.47	1.03	0.54
Num. Violationsi	1j	–	–	1.21	0.29	1.01	0.01	0.97	0.04
	2 or morej	–	–	9.13*	2.14	13.87*	2.57	12.42*	2.48
Num. Crashk	1 or morel	–	–	1.88	1.00	1.41	0.54	1.35	0.48
	Secondary task demands & passenger presence
Talking/Singingm		–	–	–	–	1.93***	7.26	1.57***	4.90
Dancing		–	–	–	–	0.48	0.52	0.48	0.53
PassAdjn		–	–	–	–	1.25	1.44	1.19	1.14
PassAdjLooko		–	–	–	–	1.16	0.49	1.08	0.26
ObjDropp/reachp		–	–	–	–	0.60	1.51	0.54	1.78
CellTalkq		–	–	–	–	0.85	0.84	0.74	1.51
CellTextr		–	–	–	–	1.04	0.33	1.01	0.07
Radio/Climates		–	–	–	–	0.96	0.21	0.96	0.23
Externalt		–	–	–	–	0.46*	2.01	0.49	1.84
Foodu		–	–	–	–	0.98	0.13	0.97	0.24
Hygienev		–	–	–	–	0.61	1.66	0.68	1.31
Smokingw		–	–	–	–	0.00	0.00	0.00	0.00
Passenger Pres.x	1 Pass Presenty	–	–	–	–	0.24***	6.01	0.25***	5.76
Driver emotions
Happy		–	–	–	–	–	–	1.35	1.23
Angry/frustrated		–	–	–	–	–	–	2.02***	10.54
Surprised		–	–	–	–	–	–	2.66**	2.84
Sad		–	–	–	–	–	–	2.07***	4.11

^a The reference group for this categorical variable is ''smooth traffic".
^b Intersection influence.
^c The reference group for this categorical variable is ''no intersection influence".
^d Traffic signal/stop sign.
^e The reference group for the categorical variable ''no adverse condition" is ''raining".
^f The reference group for this categorical variable is ''dawn-daylight".
^g The reference group for the categorical variable ''male" is ''female".
^h The reference group for this categorical variable is ''25,000 miles". ⁱ Number of moving/traffic violations in past three years.
^j The reference group for this categorical variable is ''zero violations".
^k Number of crashes in past three years.
^l The reference group for this categorical variable is ''zero crashes".
^m Talking/singing, audience unknown.
ⁿ Passenger in adjacent seat talking without looking at passenger.
^o Passenger in adjacent seat talking while looking at passenger.
^p Object dropped/reaching for object, other.
^q Cell phone, talking/listening, hand-held.
^r Cell phone, texting/browsing/dialing, hand held.
^s Adjusting/monitoring climate control/radio.
^t External distraction.
^u Reaching for food-related or drink-related item/eating/drinking.
^v Reaching for personal body-related item/personal hygiene/removing/adjusting clothing/jewelry/contact lenses or glasses.
^w Reaching for/lighting/smoking/extinguishing cigar/cigarette.
^x Presence of passengers.
^y The reference group for the categorical variable ''one or more passengers present" is ''no passenger present".
*** p < 0.001.
** p < 0.01.
* p < 0.05.

Table 3 GLMMs predicting errors.

		Model 1 (AIC = 1070.7)		Model 2 (AIC = 1080)		Model 3 (AIC = 1063.2)		Model 4 (AIC = 1067.3)
Fixed effect		Exp(b)	z-value	Exp(b)	z-value	Exp(b)	z-value	Exp(b)	z-value
Driving task demands
Traffic density	Moderate Traffica	1.40	0.76	1.39	0.75	1.69	1.14	1.67	1.11
	Heavy Traffica	0.00	0.08	0.00	0.15	0.00	0.00	0.00	0.02
IntersectionInflb	Interchangec	4.07***	4.80	4.05***	4.79	4.61***	5.17	4.79***	5.27
	Parking Lotc	0.00	0.06	0.00	0.12	0.00	0.03	0.00	0.09
	TraffSignal/Stopc/d	0.06**	2.75	0.06**	2.75	0.05**	2.96	0.05**	2.96
	Uncontrolledc	2.54	1.26	2.54	1.26	3.78	1.77	3.87	1.80
Weather	No adverse conde	15,517	0.09	1,296.1	0.22	2.7e + 09	0.00	7.7e + 05	0.02
Lighting	Duskf	0.87	0.23	0.85	0.27	0.86	0.25	0.84	0.28
	Darkness, lightedf	1.01	0.02	1.02	0.02	1.21	0.27	1.18	0.23
Driver characteristics
Years driving (Age)		–	–	1.01	0.29	1.01	0.47	1.01	0.52
Sex	Maleg	–	–	1.27	0.32	1.10	0.13	1.12	0.15
Annual miles	10,000mih	–	–	1.23	0.16	1.65	0.39	1.68	0.40
	10,000–25,000mih	–	–	1.76	0.48	2.48	0.76	2.43	0.74
Num. Crashi	1 or morej	–	–	0.79	0.30	0.69	0.48	0.66	0.54
Secondary task demands & passenger presence
Talking/Singingk		–	–	–	–	0.36	1.92	0.33*	2.04
Dancing		–	–	–	–	3.02	1.77	2.88	1.66
PassAdjl		–	–	–	–	1.67	1.16	1.62	1.08
PassAdjLookm		–	–	–	–	1.76	1.13	1.65	0.99
ObjDropp/reachn		–	–	–	–	3.51*	2.21	3.29*	2.07
CellTalko		–	–	–	–	2.33	1.70	2.29	1.64
CellTextp		–	–	–	–	6.62***	4.90	6.74***	4.91
Radio/Climateq		–	–	–	–	2.13	1.70	2.21	1.79
Externalr		–	–	–	–	2.85*	2.03	2.93*	2.07
Foods		–	–	–	–	2.53	1.78	2.68	1.89
Hygienet		–	–	–	–	1.84	0.99	1.83	0.98
Smokingu		–	–	–	–	2.19	0.79	2.42	0.88
Passenger Pres.v	1 Pass Presentw	–	–	–	–	0.34	1.53	0.32	1.62
Driver emotions
Happy		–	–	–	–	–	–	1.66	0.91
Angry/frustrated		–	–	–	–	–	–	1.12	0.40
Surprised		–	–	–	–	–	–	10.96*	2.28
Sad		–	–	–	–	–	–	1.23	0.20

^a The reference group for this categorical variable is ''smooth traffic".
^b Intersection influence.
^c The reference group for this categorical variable is ''no intersection influence".
^d Traffic signal/stop sign.
^e The reference group for the categorical variable ''no adverse condition" is ''raining".
^f The reference group for this categorical variable is ''dawn-daylight".
^g The reference group for the categorical variable ''male" is ''female".
^h The reference group for this categorical variable is ''25,000 miles". ⁱ Number of crashes in past three years.
^j The reference group for this categorical variable is ''zero crashes".
^k Talking/singing, audience unknown.
^l Passenger in adjacent seat talking without looking at passenger.
^m Passenger in adjacent seat talking while looking at passenger.
ⁿ Object dropped/reaching for object, other.
^o Cell phone, talking/listening, hand-held.
^p Cell phone, texting/browsing/dialing, hand held. ^q Adjusting/monitoring climate control/radio.
^r External distraction.
^s Reaching for food-related or drink-related item/eating/drinking.
^t Reaching for personal body-related item/personal hygiene/removing/adjusting clothing/jewelry/contact lenses or glasses.
^u Reaching for/lighting/smoking/extinguishing cigar/cigarette.
^v Presence of passengers.
^w The reference group for the categorical variable ''one or more passengers present" is ''no passenger present".
*** p < 0.001.
** p < 0.01.
* p < 0.05.

drivers conversed with the adjacent passenger while looking at him/her significantly increased on average by a factor of 4.60 during nice weather compared to rainy weather. Furthermore, drivers talked to the adjacent passenger without looking at

Fig. 8. Common secondary tasks while driving, which possess differing visual, manual, or cognitive demands and differing degrees of crash risk, were analyzed as a function of driving task demands, driver characteristics, passenger presence, and traffic violations.

him/her 12.3 times more often during dusk than during daytime (dawn-daylight) and addressed the adjacent passenger while looking at him/her 10.1 times more often during nighttime (darkness, lighted) than daytime conditions.

Finally, drivers conversed with the adjacent passenger without looking at him/her 4.46 times more often whenever they failed to signal. The other analyzed violations did not yield significant effects.

3.7.2. Cell phone, talking/listening, hand-held (moderate crash risk)

Table 5 depicts the findings from the two conducted GLMMs predicting talking/listening on the cell phone, hand-held. In terms of the analyzed driving task demands, the complete model (Model 2) shows that drivers talked and listened on the hand-held cell phone on average 2.87 times as often during dusk as during daytime (dawn-daylight). Traffic density, intersection influence, and weather did not significantly affect cell phone conversations.

Regarding driver characteristics, those who self-reported being involved in one or more crashes that were, or could have been, police-reported in the past three years, were talking and listening on the hand-held cell phone 75.05 times as often as drivers who were not in crashes that were, or could have been, police-reported in the past three years.

Finally, whenever drivers passed another vehicle illegally or improperly, occurrences of talking and listening on the handheld cell phone significantly decreased on average by a factor of 0.13. None of the other committed violations analyzed had an effect on cell phone conversations.

3.7.3. Adjusting/monitoring climate control/radio (moderate crash risk)

Table 5 depicts the findings from the two conducted GLMMs predicting adjusting/monitoring the climate control/radio. The complete model (Model 2) shows that drivers adjusted or monitored the climate control or the radio 3.28 times more often during dusk and 3.94 times more often during nighttime (darkness, lighted) than during daytime (dawn-daylight). Thus the time of day best predicted the occurrences of adjusting or monitoring the climate control or radio.

3.7.4. Cell phone, texting/browsing/dialing, hand held (high crash risk)

Table 6 presents the findings from the two conducted GLMMs predicting texting, browsing, and dialing on the cell phone. Model 1 and 2 show similar effects regarding the analyzed driving task demands. Drivers texted, browsed or dialed 2.34 (3.07 in Model 2) times as often when driving in heavy traffic as opposed to smooth traffic on average. In contrast, occurrences of texting, browsing, or dialing significantly decreased on average by a factor of 0.40 (0.50 in Model 2) in moderate traffic compared to smooth traffic. Moreover, occurrences of texting, browsing, and dialing significantly decreased on average by a factor of 0.18 (0.19 in Model 2) when drivers passed through an interchange compared to not driving through an intersection. On the contrary, when drivers waited at/passed a traffic light or stop sign and when they drove or parked in a parking lot, occurrences of texting, browsing, or dialing significantly increased on average by a factor of 2.84 (3.44 in Model 2) and by a factor of 3.07 (3.58 in Model 2) compared to driving uninfluenced by intersections. Furthermore, drivers texted, browsed or dialed 16.2 (21.5 in Model 2) times as often during nice weather (no adverse condition) as during rainy weather.

In terms of the analyzed driver characteristics, the findings depended on whether or not the model included the violation variables illegal/improper passing, signal/stop sign violation, failed to/improper signal, and exceeded speed limit. In Model 1, drivers who stated having had two or more moving or traffic violations in the past three years texted, browsed or dialed 10.03 times as often as those stating they did not have any moving or traffic violations in the past three years. This effect became non-significant once the model included the actually committed violations coded in this study. In both models, a significant effect existed for the driving experience variable years driving, implying that each year of driving experience decreased

Table 4 GLMMs predicting the cognitively and cognitive-visually distracting secondary tasks passenger in adjacent seat – talking without/while looking at passenger.

			Pass. adj. talking without looking			Pass. adj. talking with looking
		Model 1 (AIC = 3461.2)		Model 2 (AIC = 3459.2)		Model 1 (AIC = 1586.9)		Model 2 (AIC = 1593.6)
Fixed effect		Exp(b)	z-value	Exp(b)	z-value	Exp(b)	z-value	Exp(b)	z-value
Driving task demands
Traffic density	Moderate Traffica	–	–	–	–	–	–	–	–
IntersectionInflb	Heavy Traffica Interchangec Parking Lotc	– 0.49 0.30***	– 1.81 7.55	– 0.49 0.30***	– 1.80 7.53	– 0.00 3.86***	– 0.00 5.40	– 0.00 3.88***	– 0.00 5.39
	TraffSignal/Stopc/d Uncontrolledc	0.65* 0.29	3.49 2.89	0.65* 0.25	3.48 3.11	6.14*** 0.00	10.07 0.01	6.19*** 0.00	10.08 0.01
Weather	No adverse conde	0.07***	4.06	0.07***	4.06	4.60***	3.30	4.60***	3.30
Lighting	Duskf Darkness, lightedf	12.3*** 0.97	3.57 0.12	12.3*** 0.97	3.56 0.11	1.76 10.1***	0.81 4.46	1.76 10.1***	0.80 4.46
Driver characteristics
Years driving (Age)		1.03	0.26	1.03	0.28	0.98	0.25	0.98	0.26
Sex	Maleg	0.51	0.23	0.51	0.23	0.13	0.92	0.12	0.96
Annual miles	10,000mih 10,000–25,000mih	4.44 11.31	0.30 0.53	3.77 10.21	0.26 0.51	0.24 2.88	0.42 0.32	0.24 2.93	0.42 0.32
Sensation seeking		0.75	0.89	0.76	0.87	0.68	1.43	0.68	1.45
Num. Violationsi	1j	0.12	0.58	0.13	0.55	0.14	0.86	0.14	0.86
Num. Crashk	2 or morej 1 or morel	0.90 0.15	0.02 0.60	0.85 0.17	0.04 0.57	4.84 0.02	0.43 1.27	4.40 0.02	0.40 1.28
Passenger presence Passenger Presm	1 Pass Presentn	–	–	–	–	–	–	–	–
Traffic violations Illeg/Impr Passingo		–	–	1.33	0.29	–	–	0.00	0.00
Signal/StopS. Violat.p		–	–	3.59	1.60	–	–	1.22	0.24
Failed to Signalq		–	–	4.46*	2.28	–	–	0.77	0.37
Exceed.Speed Limitr		–	–	0.90	0.45	–	–	1.75	0.88

^a The reference group for this categorical variable is ''smooth traffic".

occurrences of texting, browsing, or dialing by a factor of 0.84 (0.83) on average. Neither the other driver characteristic variables nor the presence of one or more passengers had a significant effect.

Finally, there were significant effects regarding driving violations. Occurrences of texting, browsing, or dialing significantly decreased on average by a factor of 0.28 whenever drivers passed another vehicle illegally or improperly. However, whenever drivers exceeded the speed limit, occurrences of texting, browsing, or dialing significantly increased on average by a factor of 3.07. The aberrant driving behaviors signal/stop sign violation and failed to/improper signal did not have significant effects on texting, browsing, or dialing on the cell phone.

3.7.5. External distraction (high crash risk)

Table 6 shows the findings from the two conducted GLMMs predicting the secondary task external distraction. Regarding the analyzed driving task demands, the complete model (Model 2) shows that the occurrences of drivers engaged in an external distraction significantly increased on average by a factor of 2.84 and by a factor of 2.45 when drivers were waiting at/-

^b Intersection influence.

^c The reference group for this categorical variable is ''no intersection influence".

^d Traffic signal/stop sign.

^e The reference group for the categorical variable ''no adverse condition" is ''raining".

^f The reference group for this categorical variable is ''dawn-daylight".

^g The reference group for the categorical variable ''male" is ''female".

^h The reference group for this categorical variable is ''25,000 miles". ⁱ Number of moving/traffic violations in past three years.

^j The reference group for this categorical variable is ''zero violations".

^k Number of crashes in past three years.

^l The reference group for this categorical variable is ''zero crashes".

^m Presence of passengers.

ⁿ The reference group for the categorical variable ''one or more passengers present" is ''no passenger present".

^o Illegal passing on right/Illegal passing (but on right)/Other improper or unsafe passing.

^p Signal violation, tried to beat signal change/Stop sign violation, ''rolling stop"/Stop sign violation, intentionally ran stop sign at speed.

^q Failed to signal/Improper signal.

^r Exceeded speed limit.

^*** p < 0.001.

^** p < 0.01.

^* p < 0.05.

Table 5 GLMMs predicting the cognitive-manually distracting secondary task cell phone, talking/listening, hand held and the visual-manually distracting secondary task adjusting/monitoring climate control/radio.

		Cell P., talking/listening, hand-held				Adj./monit. climate contr./radio
		Model 1 (AIC = 2102.7)		Model 2 (AIC = 2102.9)		Model 1 (AIC = 2662.1)		Model 2 (AIC = 2667.7)
Fixed effect		Exp(b)	z-value	Exp(b)	z-value	Exp(b)	z-value	Exp(b)	z-value
Driving task demands
Traffic density	Moderate Traffica	0.00	0.23	0.00	0.26	1.34	0.36	1.15	0.37
	Heavy Traffica	0.00	0.13	0.00	0.21	0.48	1.00	0.48	1.00
IntersectionInflb	Interchangec	1.79	1.50	1.72	1.38	0.70	0.92	0.71	0.90
	Parking Lotc	0.00	0.18	0.00	0.25	0.16**	3.10	0.16**	3.08
	TraffSignal/Stopc/d	1.27	1.39	1.27	1.34	1.02	0.12	1.03	0.17
	Uncontrolledc	0.00	0.01	0.00	0.00	0.00	0.24	0.00	0.21
Weather	No adverse conde	–	–	–	–	1.06	0.15	1.07	0.17
Lighting	Duskf	2.88*	2.23	2.87*	2.21	3.27*	2.49	3.28*	2.50
	Darkness, lightedf	0.18***	5.34	0.18***	5.42	3.98***	5.49	3.94***	5.43
Driver characteristics
Years driving (Age)		0.86	1.92	0.86	1.93	0.98	1.36	0.98	1.38
Sex	Maleg	0.11	0.98	0.11	0.99	0.63	0.90	0.62	0.94
Annual miles	10,000mih	17.80	0.80	18.19	0.81	0.73	0.37	0.74	0.36
	10,000–25,000mih	17.90	0.82	18.15	0.82	0.62	0.57	0.62	0.57
Sensation seeking		1.16	0.83	1.16	0.83	0.98	0.40	0.98	0.41
Num. Violationsi	1j	1.35	0.14	1.34	0.14	1.73	1.03	1.73	1.03
	2 or morej	0.46	0.24	0.47	0.23	0.39	1.03	0.38	1.06
Num. Crashk	1 or morel	75.28*	2.04	75.05*	2.04	1.94	1.30	1.94	1.30
Passenger presence
Passenger Presm	1 Pass Presentn	5.87*	2.50	5.82*	2.49	1.03	0.06	1.03	0.06
Traffic Violations					–	–
Illeg/Impr Passingo		–	–	0.13*	1.99	–	–	0.48	1.00
Signal/StopS. Violat.p		–	–	0.88	0.25	–	–	0.82	0.19
Failed to Signalq		–	–	1.28	0.46	–	–	1.89	1.14
Exceed.Speed Limitr		–	–	1.03	0.10	–	–	1.09	0.32

^a The reference group for this categorical variable is ''smooth traffic".
^b Intersection influence.
^c The reference group for this categorical variable is ''no intersection influence".
^d Traffic signal/stop sign.
^e The reference group for the categorical variable ''no adverse condition" is ''raining".
^f The reference group for this categorical variable is ''dawn-daylight".
^g The reference group for the categorical variable ''male" is ''female".
^h The reference group for this categorical variable is '' 25,000 miles". ⁱ Number of moving/traffic violations in past three years.
^j The reference group for this categorical variable is ''zero violations".
^k Number of crashes in past three years. ^l The reference group for this categorical variable is ''zero crashes".
^m Presence of passengers.
ⁿ The reference group for the categorical variable ''one or more passengers present" is ''no passenger present".
^o Illegal passing on right/Illegal passing (but on right)/Other improper or unsafe passing.
^p Signal violation, tried to beat signal change/Stop sign violation, ''rolling stop"/Stop sign violation, intentionally ran stop sign at speed.
^q Failed to signal/Improper signal.
^r Exceeded speed limit.
*** p < 0.001.
** p < 0.01. * p < 0.05.

passing a traffic light or stop sign and when they drove or parked in a parking lot compared to not driving through an intersection.

In regard to the analyzed driver characteristics, drivers who stated having had one moving or traffic violation in the past three years engaged in external distractions 3.40 times as often as those who self-reported no moving or traffic violations in the past three years. However, committed traffic violations did not significantly affect the frequency of engagement in external distractions.

4. Discussion

The current study investigated the variables affecting driving errors and traffic violations. In addition, this study identified essential factors influencing drivers' willingness to engage in common secondary tasks while driving which possess differing visual, manual, or cognitive demands and differing degrees of crash risk. Therefore, 10,800 six-second trip segments of SHRP

Table 6 GLMMs predicting the visual-manually and visually distracting secondary tasks cell phone, texting/browsing/dialing and external distraction.

		CellPhone, texting/browsing/dialing			External distraction
		Model 1 (AIC = 2804)		Model 2 (AIC = 2763)		Model 1 (AIC = 2056.7)		Model 2 (AIC = 2049.2)
Fixed Effect		Exp(b)	z-value	Exp(b)	z-value	Exp(b)	z-value	Exp(b)	z-value
Driving task demands
Traffic density	Moderate Traffica	0.40**	3.03	0.50*	2.26	–	–	–	–
	Heavy Traffica	2.34***	3.37	3.07***	4.29	–	–	–	–
IntersectionInflb	Interchangec	0.18***	3.76	0.19***	3.51	0.59	0.88	0.56	0.97
	Parking Lotc	3.07***	4.29	3.58***	4.80	2.50**	3.24	2.45**	3.17
	TraffSignal/Stopc/d	2.84***	7.68	3.44***	8.69	2.79***	6.74	2.84***	6.82
	Uncontrolledc	0.00	0.01	0.00	0.04	0.00	0.00	0.00	0.02
Weather	No adverse conde	16.2***	5.64	21.5***	6.11	–	–	–	–
Lighting	Duskf	1.01	0.02	0.78	0.78	1.00	0.00	1.02	0.03
	Darkness, lightedf	0.76	1.43	0.88	0.64	0.64	1.14	0.62	1.23
Driver characteristics
Years driving (Age)		0.84**	3.28	0.83***	3.31	1.01	0.89	1.01	0.91
Sex	Maleg	2.30	1.10	2.14	0.97	4.14***	3.51	4.25***	3.58
Annual miles	10,000mih	0.11	1.69	0.11	1.62	1.00	0.01	1.01	0.02
	10,000–25,000mih	0.11*	1.98	0.11	1.90	0.71	0.51	0.71	0.49
Sensation seeking		1.11	1.51	1.10	1.42	1.01	0.19	1.01	0.17
Num. violationsi	1j	0.92	0.11	0.87	0.17	3.45**	2.94	3.40**	2.92
	2 or morej	10.03*	2.04	7.31	1.72	1.13	0.17	1.32	0.40
Num. crashk	1 or morel	3.20	1.40	3.01	1.29	0.64	1.04	0.63	1.06
Passenger presence
Passenger Presm	1 Pass Presentn	1.55	0.92	1.80	1.17	2.34*	2.14	2.27*	2.08
Traffic violations
Illeg/Impr Passingo		–	–	0.28*	2.08	–	–	0.00	0.12
Signal/StopS. Violat.p		–	–	0.40	0.87	–	–	0.00	0.00
Failed to Signalq		–	–	0.46	1.42	–	–	0.00	0.02
Exceed.Speed Limitr		–	–	3.07***	6.40	–	–	0.65	0.93

^a The reference group for this categorical variable is ''smooth traffic".

2 video footage were analyzed with regard to the environmental, situational, and driver factor effects on aberrant driving behaviors and on secondary tasks. To the best of the authors' knowledge, this comprehensive analysis based on naturalistic driving data is the first of its kind.

In summary, violations committed in the current study increased particularly with emotional impairment due to anger/ frustration, surprise, or sadness and while driving unaccompanied. However, the number of violations drivers previously committed best predicted the coded violations, indicating that individual differences in driver behavior indeed persist for years. Driving errors frequently occurred in particular when drivers passed through an interchange or were surprised while driving. Moreover, drivers made significantly more errors whenever they dropped something and picked it back up/reached for an object, pressed buttons/a touch screen on a cell phone, or engaged in an external distraction. All of these tasks are visual or visual-manual distractions and are the secondary tasks with the highest crash risks of the driver distractions analyzed in this study (Dingus et al., 2016). In contrast, cognitive distractions did not have a detrimental effect on driving performance.

^b Intersection influence.

^c The reference group for this categorical variable is ''no intersection influence".

^d Traffic signal/stop sign.

^e The reference group for the categorical variable ''no adverse condition" is ''raining".

^f The reference group for this categorical variable is ''dawn-daylight".

^g The reference group for the categorical variable ''male" is ''female".

^h The reference group for this categorical variable is '' 25,000 miles". ⁱ Number of moving/traffic violations in past three years.

^j The reference group for this categorical variable is ''zero violations".

^k Number of crashes in past three years.

^l The reference group for this categorical variable is ''zero crashes".

^m Presence of passengers.

ⁿ The reference group for the categorical variable ''one or more passengers present" is ''no passenger present".

^o Illegal passing on right/Illegal passing (but on right)/Other improper or unsafe passing.

^p Signal violation, tried to beat signal change/Stop sign violation, ''rolling stop"/Stop sign violation, intentionally ran stop sign at speed.

^q Failed to signal/Improper signal.

^r Exceeded speed limit.

^*** p < 0.001.

^** p < 0.01.

^* p < 0.05.

Both drivers' past violations, as well as committed violations coded in this study predicted engagement in the high-risk secondary tasks texting, browsing, dialing and external distraction. These findings support the hypothesis that individuals who frequently engage in risky secondary tasks while driving have a higher crash rate not only because of, for example, their cell phone use, but also due to a greater tendency to engage in risky driving behaviors independently of risky secondary tasks. However, drivers did consider the driving context in a number of ways, in particular in connection with the performance of visually distracting secondary tasks.

4.1. Driving task demands

In contrast to earlier findings, the driving task demands investigated in the current study failed to majorly impact driving error frequency. A significant increase in driving errors existed only when the vehicles traveled through an interchange. Driving in high-density traffic, in the dark, or during rain did not yield effects on the number of errors. The hypothesis that high driving task demands would increase the number of errors was therefore only partially supported (Hypothesis 1).

However, the driving context significantly influenced drivers' willingness to engage in risky behaviors while driving, supporting the hypothesis that drivers would adapt their behaviors to the driving task demands (Hypothesis 2). The secondary tasks including texting, browsing, and dialing on the phone were consistently found to be a decisive factor in crash risk (e.g., Simmons et al., 2016). Whether drivers performed these tasks was strongly dependent on the driving context. They were more likely initiated during good weather conditions and while not moving (at traffic lights, in parking lots, and during congestions), but less likely while driving in high-density traffic (but not yet congestion) or while passing through an interchange. These findings are in good agreement with the observations of Tivesten and Dozza (2015). Furthermore, similar effects were found for external distractions, which also have been associated with a high increase in crash risk (Dingus et al., 2016). Drivers only engaged in this risky behavior during good weather conditions and more often at traffic signals and in parking lots. Congruent with these findings, drivers committed fewer violations during demanding traffic conditions and when passing through interchanges. Interestingly, drivers particularly considered the driving context when interacting with the adjacent passenger. The visually distracting subtask talking while looking at the passenger occurred much more often in parking lots, at traffic lights, during nice weather and during nighttime (considered a low-traffic period). Altogether, these findings provide strong evidence that drivers factor in the driving context when deciding to engage in risky behaviors and therefore use behavioral adaptation (Young & Regan, 2013).

In contrast to previous studies suggesting that traffic violations are committed more often during evenings and nights (e.g., Clarke et al., 2006), there was a significant decrease of violations during nighttime compared to daytime. Therefore, the respective hypothesis was not supported (Hypothesis 3). However, what may partly explain this contradictory result is that Clarke et al. (2006) analyzed driving behaviors of young drivers, whereas the current study included only 13 drivers aged between 16 and 24.

4.2. Secondary task demands

Based on an analysis of 6- and 20-s naturalistic driving segments, Dingus et al. (2016) reported that drivers engaged in distracting activities more than 50% of the time while driving. The findings of the current study were almost identical even though they were based on 108 ten-minute trip segments. Specifically, drivers performed potentially distracting secondary tasks in 49% of the time investigated.

Not surprisingly, distracting activities requiring the driver to gaze away from the forward roadway and those associated with a particularly high crash risk (Dingus et al., 2016) had the highest chances of causing driving errors, supporting the respective hypothesis (Hypothesis 4). These results are consistent with earlier studies that observed glance periods away from the road scene resulting in impaired signal or event detection performance (e.g., Caird et al., 2014) as well as unintentional lane deviations (e.g., Olson et al., 2009; Young et al., 2013).

In contrast, cognitive distractions did not have a detrimental effect on driving performance, as was hypothesized (Hypothesis 5). Even speaking with a passenger did not impact driving errors despite factoring in its visually distracting subtask looking at the passenger. This finding is in good agreement with recent observations that cognitively distracting secondary tasks are connected with decreased crash and near-crash risk (e.g., Hickman et al., 2010; Klauer et al., 2006; Olson et al., 2009). Indeed, error occurrences were reduced on average by a third in this study whenever drivers talked or sang while driving alone. Moreover, consistent with the hypothesis that cognitively demanding tasks can reduce the risk of driving errors caused by fatigue especially during long and monotonous drives (Jellentrup et al., 2011), drivers engaged with the radio and passengers much more often during dusk and nighttime compared to daytime. This behavior could suggest that drivers employed cognitive distractions to stay awake.

Regarding traffic violations, most of the secondary tasks failed to have an impact. However, violations significantly increased on average by a factor of 1.57 whenever drivers talked or sang without passengers. Most likely, the explanation for this effect lies within the connection between this secondary task and emotional impairments. Soliloquies as well as singing alone while driving were more likely whenever drivers exhibited happiness, anger/frustration, surprise, or sadness. The emotional expressions of anger/frustration, surprise, and sadness in turn were the main factors associated with committed violations.

4.3. Emotional impairments

Corroborating the findings of previous studies (e.g., Zhang & Chan, 2016), traffic violations increased approximately two times on average whenever the participants drove while exhibiting anger or frustration, supporting the respective hypothesis (Hypothesis 7). For this reason, consistent with previous findings (e.g., Dahlen et al., 2005), anger was among the most important factors involved in unsafe driving behavior. Moreover, sadness had a very similar effect on traffic violations. However, it is possible that the observations of sadness in the current study were partly linked to perceived anger. A part of the coded anger occurrences were caused by conflicts with the passenger or with someone on the phone (see Precht et al., 2017 for more detailed information). Therefore, it is possible that the same conflicts triggered at least some observations of sadness and were confounded with anger. Thus the effects of this emotional impairment must be interpreted against this background. The emotional impairment surprise strongly impacted both violations and driving errors. However, it is important to note that abrupt braking maneuvers of preceding vehicles caused most of the observed ''surprises" in the current study. As hypothesized, the cognitive demands connected with emotional impairments failed to impact driving errors (Hypothesis 6).

4.4. Presence of one or more passengers

Consistent with earlier observations that drivers display safer behaviors when accompanied by passengers (Rosenbloom & Perlman, 2016), violations declined on average by a quarter when one or more passengers accompanied drivers compared to those driving alone, supporting the respective hypothesis (Hypothesis 8). However, in contrast to recent findings that passenger presence decreases driving errors (e.g., Ross et al., 2016) or increases errors due to cognitive distraction (e.g., Durbin et al., 2014), this factor did not affect the number of driving errors in the current investigation.

Furthermore, unlike the finding of Tivesten and Dozza (2015) that passenger presence lowers the likelihood of performing risky phone related secondary tasks, in the current study, accompanied drivers were talking and listening on the hand-held cell phone approximately 6 times more often than unaccompanied drivers. It is possible that the presence of passengers increased the general need to socialize with mutual acquaintances or that necessary calls were remembered or only became necessary due to the passengers. However, it remains unclear why the driver and not the passenger would make these phone calls. Additionally, in the presence of passengers, drivers engaged in external distractions about twice as often.

4.5. Driver characteristics

Differing from previous findings based on questionnaire data (e.g., Winter & Dodou, 2010), sex did not affect aberrant driving behaviors in the current study, supporting the respective hypothesis (Hypothesis 9). Men only performed the secondary task tracking external distractions more often than women. Furthermore, contrary to earlier study results (e.g., Gstalter & Fastenmeier, 2010; Winter & Dodou, 2010), age and driving experience did not impact the aberrant driving behaviors errors and violations. Therefore, the hypothesis that these driver characteristics would be negatively related to driving errors and positively related to violations was not supported (Hypothesis 10). However, there was a negative relationship between years of driving and the performance of the risky secondary tasks texting, browsing, or dialing. This implies that with each additional year of driving experience (and age in the case of this study), drivers engaged in texting, browsing, or dialing less often by a factor of 0.83 on average. This observation corresponds to findings that younger drivers have a riskier driving style and/or underestimate risks compared to older drivers (e.g., Bina et al., 2006; Hatfield & Fernandes, 2009). However, this effect could also be the result of a lower level of technology use in older age groups in general.

Analogous to Summala et al.'s (2014) findings based on a 24-year follow-up study, the results of the current investigation indicate that individual differences in driver behavior persist over time. Indeed, as hypothesized, drivers' past behavior in terms of committed violations predicted drivers' current violation frequency (Hypothesis 11). Moreover, drivers' past behavior in terms of committed violations was also predictive of risky secondary task performance, including such tasks as texting, browsing, dialing and engaging in external distractions. Noteworthy was that the secondary task talking and listening on the cell phone was clearly best predicted by drivers' past behavior in terms of crash involvement. However, given that this secondary task is not known to have a very high crash risk (crash risk odds ratio of 2.2; Dingus et al., 2016) and that talking and listening on the phone was not related to committed violations, drivers' past violations, or driving errors in the current study, it has to be considered that this effect, at least to that extent, might be spurious (see Section 4.6 for more details).

Beyond drivers' past behaviors, there were also positive relationships between violations coded in this study and the performance of secondary tasks. Unlike Tivesten and Dozza (2015) finding that drivers engaged in texting, dialing, and reading on the phone less often while driving at high speeds, drivers in the current study who exceeded the speed limit simultaneously texted, browsed, or dialed more often. This result corresponds to findings that individuals who frequently use cell phones while driving were more likely to have received traffic citations and to drive 20 mph over the speed limit (Beck et al., 2007; Zhao et al., 2013). However, this observation does not necessarily imply that drivers did not reduce their speed to meet the increased demands of engaging in this risky secondary task. It is possible that the mean speed was reduced while, however, still traveling at a speed greater than 10 mph above the posted speed limit. Moreover, drivers conversed with the adjacent passenger without looking at him/her 4.46 times more often whenever they failed to signal.

Altogether, these results support the hypothesis that drivers' past violations, as well as committed violations coded in this study would predict the performance of risky visually distracting secondary tasks. However, past crash involvements and sensation seeking were not related to these tasks. Therefore, the respective hypothesis was only partially supported (Hypothesis 12).

Furthermore, there were also negative relationships found between committed violations and the number of performed secondary tasks. All investigated phone related tasks were performed much less often when drivers illegally or improperly passed another road user, being a driving behavior with a crash risk odds ratio of approximately 14 (Dingus et al., 2016). This finding suggests that drivers differentiate between the apparent risks of varying driving contexts. However, the violation exceeding the speed limit has been found to have a crash risk odds ratio of approximately 13, almost identical to the observed risk of illegally or improperly passing (Dingus et al., 2016). These results could therefore imply that drivers poorly judge the risks associated with their aberrant driving behaviors.

Interestingly, sensation seeking did not affect engagement in aberrant driving behavior, which does not support the respective hypothesis (Hypothesis 13). This result contradicts findings that this variable predicts, for example, citations, risky driving behavior, and violations (e.g., Bachoo et al., 2013; Dahlen & White, 2006; Delhomme et al., 2012). What could partly explain this finding is that the participants' sensation seeking scores ranged between 4 and 28 with a mean of approximately 15, even though the possible range is between 0 and 40. Therefore, this study possibly did not include enough participants scoring high on sensation seeking to obtain significant effects on aberrant driving behaviors.

4.6. Limitations

This study contained some limitations. Even though a large number of naturalistic driving segments was analyzed, in terms of participants the sample size was relatively small. Taking into consideration that various factors were looked at in the models, there is a chance that some significant effects were spurious and that some effects were not significant because of the small sample size. Furthermore, the categories of the investigated variables were not necessarily distributed representatively among the participants and the investigated trip segments. Therefore, especially the reported effects regarding driver characteristics have to be interpreted with caution. Moreover, important driver impairments highly related to aberrant driving behaviors, such as driver fatigue and intoxicated driving, could not be considered in the current analyses. Having said this, the fact that the current study did consider the driver impairment anger might have affected some results as well. It is possible that people who visibly exhibit this emotion while driving and other drivers differ in some aspects, such as personality traits.

It is therefore clear that this investigation is only a primary step towards a complete model of the various variables that contribute to, or mitigate aberrant driving behaviors and therefore crash risk. Overall, further work is needed to validate the findings presented in this study. It is necessary to investigate potential risk factors for aberrant driving behaviors in larger samples of drivers in naturalistic driving data. In order to develop measures for reducing aberrant driving behaviors and therefore crash risk on the roads, wide-ranging studies are needed to reveal the mechanisms behind the emergence of driving errors and traffic violations. Therefore, risk factors should also be analyzed in samples of drivers not exhibiting driver impairments, in order to replicate effects of the current study. Furthermore, the variables that contribute to aberrant driving behaviors should be explored in samples of drivers exhibiting different types of risky driver impairment, such as drowsiness. In addition, future research should systematically analyze the causation of errors and violations in driving segments preceding crashes and near-crashes. It could be very promising to compare the respective effects to very similar segments, preferably by the same drivers, not preceding safety critical incidents. Beyond that, it would be worthwhile to specifically select driving segments in which drivers engaged in secondary tasks. This data selection procedure would increase the sample size of both the risk factors and aberrant driving behaviors and would therefore allow for extensive modeling, including less frequent secondary tasks. These different approaches should be the next promising steps towards a complete model of the causation of aberrant driving behaviors.

5. Conclusion

The current analysis has provided insight into the emergence of aberrant driving behaviors under real-world driving conditions. Anger, passenger presence, and persistent individual differences in driver behavior were the main factors associated with committed violations. The primary factors associated with driving errors under naturalistic driving conditions were surprise, high-risk visually distracting secondary tasks, and the driving task demand passing through an interchange.

In fact, willingness to engage in risky secondary tasks while driving appeared to be related to an overall tendency to engage in risky driving behaviors. However, drivers considered the driving context particularly when engaging in visually distracting secondary tasks. Interestingly, several effects noted in previous studies on aberrant driving behaviors were not observed in this investigation based on naturalistic driving data. For example, driving task demands such as high-density traffic, driving in the dark, or during rain did not affect the number of driving errors. In addition, with the exception of drivers' past behavior, most of the examined driver characteristics, had little or no impact on the number of aberrant driving behaviors coded in the current study.

Funding

The BMW Group supported this work.

Acknowledgements

The authors would like to thank the Virginia Tech Transportation Institute employees for their very helpful cooperation and especially Sarah Robinson for the data reduction.Moreover we thank Tim Kugler for technical assistance with the data recoding.

Appendix A. Used coding scheme

Variable #	Variable name	Variable definition	Examples and hints
1	Driver Emotion	Driver Emotion Reaction Definitions
1.1	Unable to Determine	Cannot tell what emotion the driver is showing
1.2	Neutral/No Emotion Shown	The driver has a straight face, does not smile or laugh, does not gesture
1.3	Happy	The driver smiles or laughs The driver gestures in excitement
1.4	Angry/Frustrated	The driver lowers/squeezes eyebrows, wrinkling forehead The driver clenches his/her teeth The driver yells (opens mouth wide with eyebrows lowered) The driver gestures in anger/ frustration The driver raises his/her upper lip or tightens lips
1.5	Sad	The driver has droopy eyebrows (raises inner eyebrows, lowers outer eyebrows) The driver frowns by lowering the outer corners of his/her lips
1.6	Surprised	The driver's eyebrows raise The driver's mouth opens The driver gestures
1.7	Other	Emotional reaction that does not fit into any other category Please define in the Notes section on the Excel Log
2	Aggressive Driving	Driver is driving in an aggressive manner not described in the ''Traffic Violation" categories, Expressions of Aggression	Verbal aggressive expression, Using the vehicle for aggressive expression, Physically aggressive expression
2.1	Head-shaking	Driver shakes the head at another driver
2.2	''Evil Eye"	Driver glares at another driver Driver gives another driver dirty looks
2.3	Yelling	Driver yells (apparently) at another driver
2.4	Hostile Gesture/Hostile Gesture, other	Driver gives the finger/ Driver makes other hostile gesture than giving the finger

Appendix A (continued)

Variable #	Variable name	Variable definition	Examples and hints
2.5	''Flip out"/More than 3 aggressive expressions combined	The driver goes crazy behind the wheel/ Driver shows 4 or more aggressive expressions at the same time	Wild and vehement gesticulation, can be combined with apparent yelling; does not fit single aggressive driving category, can be a combination of several aggressive driving categories
2.6	Fist shaking	The driver shakes his/her fist
2.7	Tongue	The driver sticks his/her tongue out
2.8	Window down	The driver rolls down the window to communicate his/her anger
2.9	Forcing to side	The driver tries to force another driver to the side of the road
2.10	Out of car/Physical Fight	The driver tries to get out of the car and tell the other driver off/ The driver tries to get out and have a physical fight
2.11	Bump	The driver bumps another driver's bumper with his/her own
2.12	Horn/Flash Lights	The driver beeps his/her horn or flashes lights at another driver
2.13	Blocking	Driver blocks another driver from doing what he/she wants to do
2.14	Inappropriate braking	E.g. driver prevents other drivers from passing The driver brakes hard to disturb another driver
3	Driver Behavior	Driver behaviors that include what the driver did
3.1	Drowsy, sleepy, asleep, fatigued	Subject vehicle driver exhibits obvious signs of being asleep or tired, or is actually asleep while driving, degrading performance of the driving task. This should also be coded as Drowsy, sleepy, asleep, fatigued under Driver Impairment
3.2	Exceeded speed limit	Subject vehicle traveling at a speed greater than the posted speed limit. In Variable Speed Zones, this is relative to the speed limit in effect at the time of the event	Over 10 mph above posted speed limit
3.3	Exceeded safe speed but not speed limit	Subject vehicle traveling at a speed close to or under the posted speed limit, but still too fast to maintain a safe driving environment given current environmental conditions (e.g., weather, traffic, lighting)	Ex. during conditions that may require slower speeds such as weather, and traffic situation
3.4	Driving slowly: below speed limit	Subject vehicle traveling at a speed much lower than the posted speed limit when higher speeds are appropriate	More than under 10 mph under posted speed limit

Appendix A (continued)

Variable #	Variable name	Variable definition	Examples and hints
3.5	Driving slowly in relation to other traffic: not below speed limit	Subject vehicle traveling much slower than other vehicles in traffic stream (but not substantially below the posted speed limit)
3.6	Illegal passing on right	Subject vehicle deliberately and illegally passes another vehicle in the lane immediately to the right of the other vehicle	This variable is not applicable in heavy traffic
3.7	Illegal passing (but on right)	Subject vehicle passes another vehicle in an unsafe or illegal manner (other than on the right)	Ex. passing across double line, going straight through turn lane, using shoulder. Passing in a High Occupancy Vehicle (HOV) lane, would NOT be considered illegal passing, even if the driver is the only passenger in the vehicle
3.8	Other improper or unsafe passing	Subject vehicle passes another vehicle in an improper manner not included in previous categories	Ex. passing on two-lane road with limited sight distance or other vehicle present
3.9	Cutting in, too close in front of/ behind other vehicle	Subject vehicle enters lane of another vehicle too closely to the front of that vehicle Subject vehicle enters lane of another vehicle too closely to the back of that vehicle
3.10	Making turn from wrong lane	Subject vehicle turns left or right from a lane not intended for making that turn	Ex. making turn across lanes
3.11	Did not see other vehicle during lane change or merge	Subject vehicle enters a lane or merges into a lane without being aware of another vehicle close by that is already traveling in that lane
3.12	Driving in other vehicle's blind zone	Subject vehicle is traveling close to another vehicle in such a way that the driver of the other vehicle is not expected to be able to see it. Subject vehicle must maintain this relative position for at least 5 s
3.13	Wrong side of road, not overtaking	Subject vehicle is traveling on the wrong side of the road with no intent of passing or overtaking another vehicle
3.14	Following too closely	Subject vehicle is traveling at an unsafe distance (too close) behind the lead vehicle
3.15	Failed to signal/Improper signal	Subject vehicle failed to properly signal its intent by not signaling at all. Applies to planned maneuvers, not sudden evasive maneuvers Subject vehicle failed to properly signal its intent by signaling incorrectly. Use with planned maneuvers, not sudden evasive maneuvers	Ex. changed lanes or made a turn without signaling Ex. used right turn signal when making a left turn

Appendix A (continued)

Variable #	Variable name	Variable definition	Examples and hints
3.16	Improper turn, wide right/left turn	Subject vehicle turned right from the initial travel path, unnecessarily encroaching into the left adjacent lane or median Subject vehicle turned left from the initial travel path, unnecessarily encroaching into the right adjacent lane or shoulder/curb	Ex. turning into oncoming traffic, turning into wrong lane on left Ex. turning into wrong lane on right or going over curb
3.17	Improper turn, cut corner on right/ left turn	Subject vehicle turned right from the initial travel path, unnecessarily encroaching into the right adjacent lane or shoulder/curb Subject vehicle turned left from the initial travel path, unnecessarily encroaching into	Ex. turning into wrong lane on right, or going over curb Ex. cuts into adjacent lane or oncoming traffic, turning into wrong lane on left or going over median
3.18	Improper turn, other	the left adjacent lane or median Subject vehicle turned left or right from the initial travel path in an unsafe manner not described in previous categories
3.19	Improper backing, did not see	Subject vehicle traveled in reverse without obtaining a proper view of the surroundings behind the vehicle	Ex. did not check mirrors when backing
3.20	Improper backing, other	Subject vehicle traveled in reverse in an unsafe manner not described in previous categories	Ex. backing into traffic, backing up on interstate
3.21	Improper start from parked position	Subject vehicle moved from a parked position in an unsafe manner	Ex. did not check mirrors
3.22	Disregarded officer or watchman	Subject vehicle driver did not notice or obey an officer of the law or traffic guard serving to provide guidance in traffic flow and the driving task	Ex. unaware or late to react
3.23	Signal violation, apparently did not see signal	Subject vehicle driver did not notice and thus disobeyed (or nearly disobeyed) a traffic signal	Ex. unaware or late to react
3.24	Signal violation, intentionally disregarded signal	Subject vehicle driver saw a traffic signal but purposefully disregarded its instruction. (If driver was trying to beat a yellow light before it phased into red, code ''Signal violation, tried to beat signal change".)	Ex. driver saw a red traffic light but proceeded through the intersection, driver failed to stop before making a right on red
3.25	Signal violation, tried to beat signal change	Subject vehicle driver accelerated or continued at a speed intended to pass through an intersection before the traffic signal turned red	Ex. went through (or attempted to go through) intersection while light was yellow
3.26	Stop sign violation, apparently did not see stop sign	Subject vehicle driver did not notice and thus disobeyed or nearly disobeyed a stop sign	Ex. unaware or late to react
			(continued on next page)

Appendix A (continued)

Variable #	Variable name	Variable definition	Examples and hints
3.27	Stop sign violation, intentionally ran stop sign at speed	Subject vehicle driver saw a stop sign but purposefully drove through the intersection at a	Ex. purposefully ran stop sign without decelerating below a speed of 15 mph
3.28	Stop sign violation, ''rolling stop"	speed greater than 15 mph Subject vehicle driver did not come to a complete stop at a stop sign (minimum speed was below 15 mph, but above 0 mph)
3.29	Other sign (e.g., Yield) violation, apparently did not see sign	Subject vehicle driver did not notice and thus disobeyed a traffic sign (other than a stop sign)	e.g., Yield, Do Not Enter, One Way
3.30	Other sign (e.g., Yield) violation, intentionally disregarded	Subject vehicle Driver saw a traffic sign (other than a stop sign) but purposefully disobeyed that sign
3.31	Other sign violation	Subject vehicle driver disobeyed a traffic sign in a manner not described in previous categories
3.32	Non-signed crossing violation	Subject vehicle driver proceeded through a non-signed intersection in an unsafe manner	Ex. did not check traffic when entering roadway from driveway or parking lot
3.33	Right-of-way error in relation to other vehicle or person, apparent recognition failure	Subject vehicle driver made the incorrect decision regarding who had the right-of-way (his/her own vehicle or another vehicle or pedestrian) due to a misunderstanding of the situation	Ex. did not see other vehicle
3.34	Right-of-way error in relation to other vehicle or person, apparent decision failure	Driver made the incorrect decision regarding who had the right-of way (his/her own vehicle or another vehicle or pedestrian) due to improper analysis of the situation	Ex. did see other vehicle prior to action but misjudged gap
3.35	Right-of-way error in relation to other vehicle or person, other or unknown cause	Subject vehicle driver made incorrect decision regarding who had the right-of-way (his/her own vehicle or another vehicle or pedestrian) for an unknown reason or for reasons not described in previous categories	Right-of-way error in relation to other vehicle or person, other or unknown cause
3.36	Sudden or improper braking	Subject vehicle braked suddenly or in an unsafe manner in the roadway, but did not come to a complete stop (i.e., speed indicator did not drop to zero)	If the sudden braking leads directly to stopping (speed indicator goes to zero), code as ''sudden or improper stopping on roadway"
3.37	Sudden or improper stopping on roadway	Subject vehicle stopped (speed indicator dropped to zero) without ample warning or in an unsafe manner in the roadway	Ex. hard or late braking/code only when driver speed indicator goes to zerocode ''sudden or improper braking" otherwise. The only time to code braking and stopping for one event would be when the braking doesn't lead directly to the stopping (subject brakes, then a bit later has to suddenly stop)

Appendix A (continued)

Parking in improper or dangerous location Failure to dim headlights Driving without lights or with insufficient lights Avoiding pedestrian Avoiding other vehicle Avoiding animal Apparent unfamiliarity with roadway	Subject vehicle parked (stopped with the intent of remaining stopped) in a location not intended for parking Subject vehicle traveling with high beams activated on headlights, without dimming the lights when appropriate Subject vehicle traveling with no headlights on (or insufficient headlights) when the situation requires such lighting for safety Subject vehicle driver behaved in a manner intended to avoid conflict with a pedestrian Subject vehicle driver behaved in a manner intended to avoid conflict with another vehicle Subject vehicle driver behaved in a manner intended to avoid conflict with an animal Subject vehicle driver behaved in an unsafe manner, apparently due	Ex. shoulder of Interstate Ex. repeated U-turns, reading






	to an unfamiliarity with the surrounding traffic situation or locality	maps, papers, etc
Apparent unfamiliarity with vehicle	Subject vehicle driver behaved in an unsafe manner, apparently due to an unfamiliarity with the vehicle	Ex. unfamiliarity with displays and controls
Apparent general inexperience driving	Subject vehicle driver behaved in an unsafe manner, apparently due to lack of experience with the driving task	Ex. hyper-focused driving, overly cautious maneuvers, etc
Unintentional Lane Deviation	Subject vehicle driver fails to maintain appropriate and safe lane position and unintentionally drifts towards and/or over one or
Other	Subject vehicle driver engages in other behavior not described in
Unknown	Cannot determine the behavior(s) engaged in by the subject vehicle driver due to limitations in video views, lighting, visual obstructions, or limited perspective	Ex. Part of the video is missing or there is insufficient information in the video to make a determination
Secondary Task	Observable driver engagement in any of the listed secondary tasks, beginning at any point during the Event time. Distractions include non-driving related glances away	Code the task as interrupted if the interruption lasts for 5 s or longer (SHRP2 reduction standard)
		more lane lines previous categories from the direction of vehicle

Appendix A (continued)

Variable #	Variable name	Variable definition	Examples and hints
		movement. Does not include tasks that are critical to the driving task, such as speedometer checks, mirror/blind spot checks, activating wipers/headlights, or shifting gears. Other non-critical tasks are included, including radio adjustments, seatbelt adjustments, window adjustments, and visor and mirror adjustments. Note that there is no
4.1	Talking/singing, audience unknown/Talking to pet	lower limit for task duration Subject vehicle driver is moving lips as if talking or singing, the interaction is not believed to be with a passenger. This category includes whistling. This category does not include the driver talking to a pedestrian or other known party outside the vehicle, which should be coded as the appropriate external distraction	Driver may or may not also be interacting with a passenger, but this Secondary Task involves singing with radio, talking to self, etc.
4.2	Dancing	Subject vehicle driver is moving his/her arms, head, or other body part seemingly in time with the beat of music	e.g., tapping hands/fingers on steering wheel, bobbing head, ''air drums" or ''air guitar"
4.3	Reading/Writing	Subject vehicle driver is reading material that is in the vehicle, but not a part of the vehicle (i.e., not reading external signs, or center stack display)./Subject vehicle driver is writing with a pen/pencil or using a stylus on a tablet	This could be reading directions, paper material, packaging. If reading a phone number, record as dialing cell phone./Driver could be writing on a piece of paper, making notes on a tablet, etc.
4.4.1	Passenger in adjacent seat – talking without looking to passenger	A front seat passenger is visible or not visible, but the subject vehicle driver is clearly talking with a passenger in the adjacent/front seat. While talking or listening, the driver does not look towards the passenger	Use this distraction if the driver is talking to the front seat passenger but is not visually distracted; includes apparent listening to passenger. Entire trip file or segment may be used to look for evidence of passenger. Consider this distraction as long as the driver and passenger both remain in the vehicle (even if the car stops or is idling)
4.4.2	Passenger in adjacent seat – talking with looking to passenger	A front seat passenger is visible or not visible, but the subject vehicle driver is clearly talking with a passenger in the adjacent/front seat. While talking or listening, the driver looks towards the passenger	Use this distraction if the driver is talking to the front seat passenger and is visually distracted; includes apparent listening to passenger. Entire trip file or segment may be used to look for evidence of passenger. Consider this distraction as long as the driver and passenger both remain in the vehicle (even if the car stops or is idling)

Appendix A (continued)

Variable #	Variable name	Variable definition	Examples and hints
4.4.3	Passenger in adjacent seat – any interaction but talking	A front seat passenger is visible or not visible, but the subject vehicle driver is clearly interacting with a passenger in the adjacent/front seat. This could be gesturing towards, moving toward or away from the passenger, or reaching to take something from or give something to the passenger	Only consider ''looking" if the driver maintains a glance for 1 s or more and disregard short glances unless they are repeated If the driver is talking while interacting, code as the appropriate ''talking" category. Entire trip file or segment may be used to look for evidence of passenger. Consider this distraction as long as the driver and passenger both remain in the vehicle (even if the car stops or is
4.5.1	Passenger in rear seat - talking without looking to passenger	A rear seat passenger is visible or not visible, but the subject vehicle driver is clearly talking with a passenger in the rear seat. While talking or listening, the driver does not look towards the passenger	idling) Use this distraction if the driver is talking to the rear seat passenger but is not visually distracted; includes apparent listening to passenger. Entire trip file or segment may be used to look for evidence of passenger. Consider this distraction as long as the driver and passenger both remain in the vehicle (even if the car stops
4.5.2	Passenger in rear seat - talking with looking to passenger	A rear seat passenger is visible or not visible, but the subject vehicle driver is clearly talking with a passenger in the rear seat. While talking or listening, the driver looks towards the passenger	or is idling) Use this distraction if the driver is talking to the rear seat passenger and is visually distracted; includes apparent listening to passenger. Entire trip file or segment may be used to look for evidence of passenger. Consider this distraction as long as the driver and passenger both remain in the vehicle (even if the car stops or is idling) Only consider ''looking" if the driver maintains a glance for 1 s or more and disregard short glances
4.5.3	Passenger in rear seat - any interaction but talking	A rear seat passenger is visible or not visible, but the driver is clearly interacting with a passenger in the rear seat. This could be moving toward or away from the passenger, or reaching for the rear seat passenger/reaching to take something from or give something to the passenger	unless they are repeated If the driver is talking while interacting, code as the appropriate ''talking" category. Entire trip file or segment may be used to look for evidence of passenger. Consider this distraction as long as the driver and passenger remain in the vehicle (even if the car stops or is
4.6	Moving object in vehicle	An object inside the vehicle, which is not being held by the driver or passenger(s) (if present) is in motion, either due to the motion of the vehicle or due to another passenger throwing the object	idling) Ex. object fell off seat when driver stopped hard at a traffic light

Appendix A (continued)

Variable #	Variable name	Variable definition	Examples and hints
4.7	Insect in vehicle	Interaction with any insect in the vehicle (e.g., swatting at insect, moving body to avoid insect, looking around trying to locate insect)
4.8	Pet in vehicle	Any interaction with a pet in the vehicle but talking, including holding, petting, or moving pet	Only code if animal/pet is visible at some point in the trip file or if there is history/context with the driver and the driver is exhibiting behaviors that are appropriate to having a pet in the vehicle
4.9	Object dropped by driver/ Reaching for object, other/Object in vehicle, other	Subject vehicle driver is initially holding something and drops it and the driver then immediately picks it back up, taking the driver's attention away from the driving task./Subject vehicle driver reaches for an object not described in any other category. Includes objects in storage compartments./ Subject vehicle driver clearly is looking at, handling, holding, or manipulating an object (visible or not) or thing located in the vehicle, other than those listed in other categories	This category supersedes other ''reaching" categories in the situation of an object being dropped and immediately retrieved./Once the driver has finished reaching for the object and has it in hand (if not being moved for intended usage), then it becomes ''object in vehicle, other," as long as it doesn't fit into any of the other categories (e.g., eating, drinking, etc.)
4.10	Cell phone, Holding	Subject is holding a cell phone but not manipulating it. Could be holding it in hand, lap, or some other way
4.11	Cell phone, Talking/listening, hand-held	Subject vehicle driver is talking on a handheld phone or has phone up to ear as if listening to a phone conversation or waiting for person they are calling to pick up the phone. If driver has an earpiece or headset, the driver must be
4.12	Cell phone, Talking/listening, hands-free	observed talking repeatedly Subject vehicle driver is talking or listening on a phone using a hands-free device such as a headset, in-vehicle integrated system, or hands-free speaker phone. If driver has an earpiece or headset, the driver must be observed talking repeatedly	If this category cannot be reliably determined code as ''Talking/ singing, audience unknown"
4.13	Cell Phone, Texting/Browsing/Cell Phone, Dialing hand-held/dialing hand-held using quick keys	Subject vehicle driver is pressing buttons or a touch screen on the cell phone to create and/or send a text message./Subject vehicle driver is pressing buttons or a touch screen on the cell phone to browse the internet or phone applications. May also include voice commands (e.g., Siri)./

Appendix A (continued)

Variable #	Variable name	Variable definition	Examples and hints
		Subject vehicle driver is pushing number buttons on a cell phone or touch screen to dial a number or browse/check something else on their cell phone (this would also include reading the phone number from a sheet of paper)./Subject vehicle driver is pushing quick key
4.14	Cell Phone, Dialing hands-free using voice-activated software	buttons (e.g., speed dial) Driver speaks into open or activated cell phone, headset, or in-vehicle integrated device for the purpose of dialing with long, prior delay of no speaking into device (i.e., most likely not in prior conversation) and no more than one or two button presses (e.g., push to begin) on phone, earpiece, headset, or in-vehicle integrated
4.15	Cell Phone, Locating/reaching/an swering/Cell phone, other	system are made first Subject vehicle driver is glancing to find cell phone, reaching towards his/her cell phone, and/or flipping phone open or pressing a button to answer a call./Subject vehicle driver is interacting with a cell phone in some manner (e.g., looking at a cell phone, but not necessarily manipulating the cell phone in any way), or action does not fit in any other category	If more than one distraction happens (e.g., driver looks for phone, reaches for it and then answers it), the last frame number would be the last step in this sequence (e.g., answering cell phone). Once phone is at driver's ear or conversation has clearly begun, code as the appropriate ''talking" category/ Includes plugging phone into charger, cleaning screen, putting
4.16	Tablet device, Locating/reaching/ Operating/Viewing/Other	Subject vehicle driver is doing anything with a tablet device (reaches or starts to glance around for an electronic tablet device (e.g., iPad)./is pressing buttons on or using the touch screen on the electronic tablet device./is holding and looking at an electronic tablet device, but not pressing any buttons.)	on headset, etc. Includes plugging tablet into charger, cleaning screen, headset, holding without manipulating, etc.
4.17	Adjusting/monitoring climate control/radio	Subject vehicle driver interacts with in-vehicle climate control system either by touching the climate control buttons, glancing at the climate control on dashboard, or adjusting climate control vents./Subject vehicle driver interacts with in-vehicle radio/audio system either by touching the radio buttons on dashboard or steering wheel, or glancing at the radio on dashboard

Appendix A (continued)

Variable #	Variable name	Variable definition	Examples and hints
4.18	Inserting/retrieving CD (or similar)	Subject vehicle driver picks up CD, cassette, or other music storage device (other than MP3 player) in vehicle and/or inserts it into radio, presses any subsequent buttons to get device to play/rewind/fast forward and then play, or driver presses button to eject device and then places it somewhere in
4.19	Adjusting/monitoring other devices integral to vehicle	vehicle Subject vehicle driver interacts with a manufacturer-installed device other than those listed in other categories, either by touching or glancing at the device. Does not include driving-critical tasks, such as turn signal, wipers, headlights, gear shift, speedometer	Includes interaction with seat belt, door locks, window controls, Navigation system, sun visors, rear view mirror, etc. Does not include retrieving objects inside storage compartments
4.20	Looking at previous crash or inci dent/pedestrian/animal/object external to the vehicle/Distracted by construction/Other external distraction	Subject vehicle driver is looking outside of the vehicle in the direction of what is obviously an accident or similar incident/pedestrian either on the side of the road or in front of them (i.e., using a cross walk or riding a bike at a red light)/animal either on the side of the road (this would not be used for an animal crossing the road)/object on the side of the road (e.g., a box)./Subject vehicle driver is looking outside of the vehicle in the direction of a construction zone. A construction zone would be defined as the presence of a barrel, person in a hard hat, construction equipment or vehicles./Subject vehicle driver is looking outside of the vehicle for purposes not described in previous categories, or for an unknown reason when glance is not considered to be part of the driving task	Only mark if it is clear that the driver is tracking a specific external distraction as they drive by. Quick glances are not categorized in this category. Only consider ''looking" if the driver maintains a glance for 1 s or more and disregard short glances unless they are repeated
4.21	Reaching for food-related or drink-related item/Eating/ Drinking	Subject vehicle driver is looking for or reaching for any item related to eating or drinking./ Driver eats/drinks
4.22	Reaching for /Lighting/Smoking/E xtinguishing cigar/cigarette	Subject vehicle driver reaches or starts to glance around for cigar/cigarette or related items/ process of lighting cigar/cigarette/ has a lit cigar/cigarette either in their mouth or hand/puts out his/ her cigar/cigarette, hands it to someone else, or tosses it out the window

Appendix A (continued)

Variable #	Variable name	Variable definition	Examples and hints
4.23	Reaching for personal body related item/personal hygiene/ Removing/adjusting clothing/jewelry/contact lenses or glasses	Subject vehicle driver is reaching for any item related to personal hygiene, health, or adornment./ Subject vehicle driver is engaged in some personal hygiene activity (ies)	Ex. reaching for comb, brush, makeup, razor, dental floss, contact lenses, glasses (not currently being worn), hat (not currently being worn). Item is in hand and being moved with the intent to use
4.24	Other non-specific internal eye glance	Subject vehicle driver glances away from the direction of travel at something inside the subject vehicle, but cannot determine a specific glance location	Only consider ''looking" if the driver maintains a glance for 1 s or more and disregard short glances unless they are repeated
4.25	Other known secondary task	Subject vehicle driver is engaged in a recognizable secondary task that is not listed in other categories Please define in the Notes section on the Excel Log	Includes cases where the vehicle is traveling in reverse and the driver is looking out the forward or side windows (other than side mirrors), rather than the roadway behind the car, which is now the direction of travel
4.26	Unknown type (secondary task present)	Subject vehicle driver is clearly distracted from the driving task, but the specific distraction is unknown
4.27	Unknown	Cannot determine whether the subject vehicle driver is engaged in a secondary task due to limitations in video views, lighting, visual obstructions, or limited perspective	Ex. Part of the video is missing or there is insufficient information in the video to make a determination
5	Traffic Density	The level of traffic density. Based entirely on number of vehicles present in the subject's travel lane and other lanes in the subject's direction of travel, and the ability of the subject vehicle driver to maneuver between lanes and select the driving speed. In Variable Speed zones, consider a reduced speed limit to be an indicator of traffic density (e.g., a variable speed limit of 30mph on an Interstate should be interpreted as a 50% reduction in
5.1	Level-of-service A1: Free flow, no lead traffic &Level-of-service A2: Free flow, leading traffic present &Level-of-service B: Flow with some restrictions	travel speeds) LOS A1 represents a free flow traffic situation when the subject vehicle has no leading traffic in any lane (following traffic may or may not be present). Individual users are unaffected by the traffic stream. Freedom to select desired speeds and to maneuver within the traffic stream is at the highest level possible LOS A2 represents a free flow	Driving-related decisions are made with virtually no need to consider the presence of other vehicles (due to the lowest traffic density) Ex. 1: If more than 1 lane is present in the direction of travel, then LOS A2 may apply if there is a lead vehicle in the subject's lane but no vehicles in the adjacent lane preventing the driver from

Appendix A (continued) Variable # Variable name Variable definition Examples and hints traffic with a leading vehicle present in at least one lane. However, individual drivers are still virtually unaffected by the presence of others in the traffic stream. Freedom to select desired speeds and to maneuver within the traffic stream is extremely high. The general level of comfort and convenience provided to the motorist, passenger, or pedestrian is excellent LOS B is still in the range of stable flow, but the presence of other users in the traffic stream begins to be noticeable. Freedom to select desired speeds is relatively unaffected, but there is a slight decline in the freedom to maneuver within the traffic stream from LOS A. The level of comfort and convenience provided is somewhat less than at LOS A, because the presence of others in the traffic stream begins to affect individual behavior passing the lead vehicle Driving-related decisions are made with a small need to consider the presence of other vehicles (due to a fairly low traffic density). Ex. 2: If greater than1 through lane is present, then LOS B may apply if there is a lead vehicle as well as an adjacent vehicle preventing the driver from easily passing OR if there are adjacent vehicles on both sides. However, this situation should be transient. The subject driver should not be ''boxed" in for a more than a few seconds. LOS B would also apply if several vehicles are present in the mid-range vicinity, even if they are not directly in front of or adjacent to the subject. Driving speeds are still at or near the speed limit and are not persistently affected by surrounding traffic 5.2 Level-of-service C: Stable flow,

maneuverability and speed are more restricted & Level-of-service D: Unstable flow - temporary restrictions substantially slow driver

LOS C is still in the range of stable flow, but marks the beginning of the range of flow in which the operation of individual users becomes significantly affected by interactions with others in the traffic stream. The selection of speed is now affected by the presence of others, and maneuvering within the traffic stream requires substantial vigilance on the part of the driver. The general level of comfort and convenience declines noticeably at this level LOS D represents a high-density, but stable flow. Speed and freedom to maneuver are severely restricted, and the driver or pedestrian experiences a generally

poor level of comfort and convenience. Small increases in traffic flow will generally cause operational problems at this level Driving-related decisions are made with a definite need to consider the presence of other vehicles, with a good chance of mishap if such considerations are not made (due to a medium traffic density). Ex. 1: If only 1 through lane is present, LOS C may apply if subject has a lead vehicle AND another car is following the subject. OR, if subject is following multiple vehicles. In either case, the speed is significantly controlled by leading traffic, but the prevailing speed is not more than 10 mph below the speed limit. Ex. 2: If greater than1 through lane is present, LOS C may apply if the subject is ''boxed in" by lead and adjacent vehicles and this condition is not transient (e.g., it persists as the vehicles travel for some time). LOS C would also apply if multiple vehicles are present in the near-range vicinity, and travel speeds are moderately affected (but are not more than 10 mph below the posted speed limit).Ex. 3: If the subject is preparing to exit, merge, change

Variable # Variable name Variable definition Examples and hints

lanes, etc. in an LOS C environment, there will be multiple vehicles posing potential hazards and requiring careful monitoring by the subject. The maneuver will be more difficult, but will generally be completed without incident Driving-related decisions are made with urgent need to consider the presence of other vehicles, with a great likelihood of mishap if such considerations are not made (due to a fairly high traffic density) Ex. 1: If only 1 through lane is

present, LOS D may apply if subject is following another car AND another car is following the subject. OR, if subject is following multiple vehicles. In either case, the speed is significantly controlled by leading traffic, and prevailing speed is more than 10 mph below the speed limit Ex. 2: If greater than1 through lane is present, LOS D may apply if the subject is persistently ''boxed in" by lead vehicles and adjacent vehicles, AND the prevailing travel speed is determined by surrounding traffic and is more than 10 mph below the posted speed limit

Ex. 3: If the subject is preparing to exit, merge, change lanes, etc. in an LOS D environment, there will be multiple vehicles posing potential hazards and requiring careful monitoring. The maneuver will not be easy and will likely involve braking, accelerating, or excessive steering on the part of the subject or other vehicles Driving-related decisions are made with an urgent need to consider the presence of other vehicles, with a great likelihood of mishap if such considerations are not made/freedom to execute maneuvers is severely restricted such that drivers must be aggressive in maneuvering (due to a very high traffic density)

Ex. 1: If only 1 through lane is

5.3 Level-of-service E: Flow is unstable, vehicles are unable to pass, temporary stoppages, etc. & Level-of-service F: Forced traffic flow condition with low speeds and traffic volumes that are below capacity

LOS E represents operating conditions at or near the capacity level. All speeds are reduced to a low, but relatively uniform value. Freedom to maneuver within the traffic stream is extremely difficult, and it is generally accomplished by forcing a vehicle or pedestrian to ''give way" to accommodate such maneuvers. Comfort and convenience levels

Appendix A (continued) Variable # Variable name Variable definition Examples and hints are extremely poor, and driver or pedestrian frustration is generally high. Operations at this level are usually unstable, because small increases in flow or minor perturbations within the traffic stream will cause breakdowns LOS F represents forced or breakdown flow. This condition exists wherever the amount of traffic approaching a point exceeds the amount which can traverse the point. Queues form behind such locations. Operations within the queue are characterized by stop-and-go waves, and they are extremely unstable. Vehicles may progress at reasonable speeds for several hundred feet or more, then be required to stop in a cyclic fashion. LOS F is used to describe the operating conditions within the queue, as well as the point of the breakdown. It should be noted, however, that in many cases operating conditions of vehicles or pedestrians discharged from the queue may be quite good. Nevertheless, it is the point at which arrival flow exceeds discharge flow, which causes the queue to form, and level-ofservice F is an appropriate designation for such points present, LOS E may apply if subject is following multiple cars AND multiple cars are following the subject. The speed is significantly controlled by leading traffic, and the prevailing speed is reduced to less than half the posted speed limit Ex. 2: If greater than1 through lane is present, then LOS E may apply if the subject is persistently ''boxed in" by lead vehicles and adjacent vehicles, AND the prevailing travel speed is determined by surrounding traffic and is less than half the posted speed limit. Ex. 3: If the subject is preparing to exit, merge, change lanes, etc. in an LOS E environment, there will be multiple vehicles posing potential hazards and requiring careful monitoring by the subject. The maneuver will be ''forced" and will likely involve braking, accelerating, or excessive steering on the part of both the subject and other vehicles Traffic flow and related driving decisions are based entirely on the presence and actions of other vehicles (due to the highest traffic density) Ex. 1: Regardless of the number of travel lanes, LOS F represents ''traffic jam" or ''stop and go" conditions. Ex. 2: If the subject is preparing to exit, merge, change lanes, etc. queues will be forming or present either in the subject's desired lane and/or in the subject's destination lane. The maneuver will be ''forced" and will involve braking, accelerating, or excessive steering on the part of both the subject and other vehicles 5.4 Unknown Cannot determine the traffic density due to limitations in video views, lighting, visual obstructions, or limited perspective Ex. Part of the video is missing or there is insufficient information in the video to make a determination 6 Lighting Lighting condition. If inside a tunnel or parking facility, code the conditions inside the facility,

regardless of the lighting conditions outside

Appendix A (continued)

Variable #	Variable name	Variable definition	Examples and hints
6.1	Dawn	The time of day during the Event is sunrise	It is just starting to get light, but is still mostly dark. Most vehicles will drive with headlights
6.2	Daylight	The Event occurs in daylight, such as occurs in after dawn but before dusk
6.3	Dusk	The time of day during the Event is sunset	It is just before full darkness. Most vehicles will drive with headlights
6.4	Darkness, lighted	It is dark during the Event, but the roadway is sufficiently lighted. (Vehicle may be outside or inside a parking structure or tunnel.)	Lighted roadway includes street lamps as well as lighting coming from businesses provided that they illuminate the roadway also
6.5	Darkness, not lighted	It is dark during the Event, and the roadway is not lighted. (Vehicle may be outside or inside a parking structure or tunnel.)
6.6	Unknown	Cannot determine the lighting conditions due to limitations in video views, lighting, visual obstructions, or limited perspective	Ex. Part of the video is missing or there is insufficient information in the video to make a determination
7	Weather	Weather condition of the Event. If inside a tunnel or parking facility, code the conditions inside the facility, regardless of the weather conditions outside
7.1	No Adverse Conditions	There are no adverse atmospheric conditions at the time of the Event (no conditions described in other categories)
7.2	Fog	There is fog visible at the time of the Event
7.3	Mist/Light Rain	There is mist in the air or light rain at the time of the Event
7.4	Raining	It is raining steadily at the time of the Event. (Code wet road in Surface Condition.)	Check for wiper use
7.5	Snowing	It is snowing at the time of the Event. (Code snow or slush on
7.6	Sleeting	road in Surface Condition.) It is sleeting at the time of the Event. (Code ice on road in Surface Condition.)
7.7	Rain & Fog	It is both raining and foggy at the time of the Event. (Code wet road in Surface Condition.)
7.8	Snow/Sleet & Fog	It is both snowing and foggy at the time of the Event. (Code snow on road in Surface Condition.)
7.9	Other	There is some type of adverse atmospheric condition present, not described in other categories, at the time of the Event	Ex. smog, blowing sand, blowing snow (not falling from sky), crosswind, hail, sand/dust, smoke

Appendix A (continued)

Variable #	Variable name	Variable definition	Examples and hints
7.10	Unknown	Cannot determine the weather at the time of the Event due to limitations in video views, lighting, visual obstructions, or limited perspective	Ex. Part of the video is missing or there is insufficient information in the video to make a determination
8	Surface Condition	The type of roadway surface condition that would affect the vehicle's coefficient of friction at the Event. Includes weather related surface conditions as well as non-paved surface descriptions. If inside a tunnel or parking facility, code the conditions inside the facility, regardless of the surface conditions outside
8.1	Dry	There is no foreign material (rain, snow, oil, etc.) on the roadway in the area of the event (nothing on the road to affect the driving task)
8.2	Wet	Roadway is completely or partially wet in the area of the event (not snowy, icy, muddy, or oily)
8.3	Snowy	There is some amount of unmelted snow or slush on the roadway in the area of the event (no ice on the road in the area of interest)	If other conditions are also present in the area affecting the event, choose the first category from this list that is applicable: icy, snowy, oily, or muddy, and add other conditions to narrative
8.4	Icy	There is some amount of ice on the roadway in the area of the event	If there is ice on the surface that affects the event, code as icy, regardless of any other coexisting conditions. Add other conditions to narrative
8.5	Gravel/Dirt Road	The road surface consists of gravel or dirt. Not paved. Use this option only if weather-related options above do not also apply
8.6	Gravel over Asphalt	The road surface is paved, but there is gravel over the asphalt that may create traction issues. Use this option only if weather related options above do not also apply
8.7	Muddy	There is some amount of mud on the roadway in the area of the event, enough to affect the driving task	If other conditions (other than simply a wet road) are also present in the area affecting the event, choose the first category from this list that is applicable: icy, snowy, or oily. Add other conditions to narrative
8.8	Oily	There is some amount of oil, grease, or other slippery fluid on the roadway in the area of the event, enough to affect the driving task	If the road is also icy (or icy and snowy) in the area affecting the event, categorize as icy. If the road is also snowy, categorize as snowy. Add other conditions to narrative

Appendix A (continued)

Variable #	Variable name	Variable definition	Examples and hints
8.9	Other	There is some type of foreign substance on the road, not listed in previous categories, enough to affect the driving task	If the substance on the road can be driven over, but would affect the vehicle's coefficient of friction, code as ''other" road condition. Material large or harmful enough to necessitate maneuvering around it would be categorized as an object or obstacle in the road and is not considered a Surface Condition
8.10	Unknown	Cannot determine whether any Surface Conditions affected the event due to limitations in video views, lighting, visual obstructions, or limited perspective	Ex. Part of the video is missing or there is insufficient information in the video to make a determination
9	Intersection Influence	A judgment call as to whether the subject vehicle's safe movement, travel path, and travel speed, are under the influence of an intersection at the time of the event (at any time between Event Start through Event End). This can include the subject or other involved vehicle(s) accelerating or decelerating in relation to an intersection or intersecting trafficway, accelerating or decelerating prior to a turn onto a new roadway or into a parking lot or driveway, waiting in a queue of traffic, moving between through lanes and turn lanes or through lanes and acceleration/ deceleration lanes, yielding to oncoming or cross traffic, etc. Note that a ''Yes" option can be coded here even if Relation to Junction is Non-junction if the vehicle(s) are too far from the intersection to code Relation to Junction categories but are still being influenced in a manner described here by an intersection (e.g., a longer queue of traffic at a signal,
9.1	Yes, Traffic Signal & Yes, Stop Sign	or a long process of deceleration prior to a turn) Subject vehicle's safe movement along the roadway (e.g., speed, travel path, lane changes) is influenced by a traffic signal at the time of the event. This can include accelerating or decelerating in a queue of traffic or in response to signal color changes or in

Appendix A (continued)

Variable # Variable name Variable definition Examples and hints

preparation for a turn at such an

intersection, moving between through lanes and turn lanes, yielding to oncoming or cross traffic, etc. Does not include green lights that have no impact on the subject's travel during the event (to be coded as ''No"). Includes parking lot and driveway entrance/exits that are control by a traffic signal. Subject vehicle's safe movement along the roadway (e.g., speed, travel path, lane changes) is influenced by a stop sign at the time of the event. This can include accelerating or decelerating in a queue of traffic or in preparation for a turn, moving between through lanes and turn lanes, yielding to oncoming or cross traffic, etc. Includes parking lot and driveway exits when the subject is the exiting vehicle and is subject to a stop sign 9.2 Yes, Uncontrolled & Yes, Other Subject vehicle's safe movement along the roadway (e.g., speed, travel path, lane changes) is influenced by an intersection or intersecting traffic-way, other than a driveway or parking lot entrance/exit, that is not controlled (in the subject's direction of travel) by any type of traffic control device. This can include accelerating or decelerating in a queue of traffic or in preparation for a turn, moving between through lanes and turn lanes, yielding to oncoming or cross traffic, etc. Generally will only be coded if cross traffic or oncoming traffic is moving across the subject's intended travel path and requiring some speed or steering response from the subject Subject vehicle's safe movement along the roadway (e.g., speed, travel path, lane changes) is influenced by some other type of intersection or intersecting trafficway not listed in other categories at the time of the event

This can include influences related to crosswalks, railroad crossings, traffic circles/round-abouts, etc.

Appendix A (continued)

Variable #	Variable name	Variable definition	Examples and hints
9.3	Yes, Interchange	Subject vehicle's safe movement along the roadway (e.g., speed, travel path, lane changes) is influenced by an interchange area (including entrance and exit ramps) at the time of the event. This can include accelerating or
		decelerating in a queue of traffic or prior to merging, moving between through lanes and acceleration/deceleration lanes, yielding to traffic in adjacent lanes related to merging by the subject or another vehicle, etc.
9.4	Yes, Parking lot, Driveway Entrance/Exit	Subject vehicle's safe movement along the roadway (e.g., speed, travel path, lane changes) is influenced by a parking lot or driveway entrance/exit that is not controlled (in the subject's direction of travel) by any type of traffic control device (although other involved vehicles may or may not have a stop sign). This can include the subject or other involved vehicle(s) decelerating in traffic in preparation to turn into a
9.5	No	parking lot or driveway, turning into or out of a parking lot or driveway, yielding to oncoming or cross traffic prior to turning in or exiting a parking lot or driveway, etc. This category includes driving or parking in parking lot Subject vehicle's safe movement along the roadway (e.g., speed, travel path, lane changes) is not influenced by any type of intersection or intersecting trafficway at the time of the event. This may be coded even if the
9.6	Unknown	subject is in or near an intersection or intersecting trafficway provided that the subject's speed, travel path, etc. are not being actively affected during the event Cannot determine whether the subject is being influenced by an intersection due to limitations in video views, lighting, visual obstructions, or limited perspective	Ex. Part of the video is missing or there is insufficient information in the video to make a determination

Appendix B

See Tables B1 and B2.

Table B1 Total number and duration of effectively coded secondary tasks (related to ten-minute segments).

Secondary task	Total number (M, SD)	Total duration in sec (M, SD)
Talking/singing, audience unknown	190 (1.76, 2.47)	4194.31 (38.84, 95.74)
Dancing	17 (0.16, 0.73)	1346.11 (12.46, 78.74)
Reading/writing	5 (0.05, 0.32)	175.59 (1.63, 10.42)
Passenger in adjacent seat – talking without looking at passenger	150 (1.39, 2.75)	11238.15 (104.06, 187.94)
Passenger in adjacent seat – talking while looking at passenger	66 (0.61, 1.58)	1435.96 (13.30, 39.09)
Passenger in rear seat – talking without looking at passenger	17 (0.16, 0.67)	1045.67 (9.68, 52.30)
Passenger in rear seat – talking while looking at passenger	9 (0.08, 0.44)	190.90 (1.77, 11.55)
Moving object in vehicle	1 (0.01, 0.10)	26.55 (0.25, 2.56)
Object dropped/reaching for object, other	31 (0.29, 0.83)	862.54 (7.99, 32.93)
Cell phone, holding	23 (0.21, 0.71)	1644.04 (15.22, 69.89)
Cell phone, talking/listening, hand-held	28 (0.26, 0.69)	2010.72 (18.62, 59.10)
Cell Phone, texting/browsing/dialing, hand held	82 (0.76, 1.35)	2243.48 (20.77, 49.82)
Cell Phone, locating/reaching/answering/cell phone, other	11 (0.10, 0.33)	61.29 (0.57, 2.23)
Adjusting/monitoring climate control/radio	167 (1.55, 1.94)	1003.44 (9.29, 29.27)
External distraction	103 (0.95, 1.81)	976.45 (9.04, 22.00)
Reaching for food-related or drink-related item/eating/drinking	47 (0.44, 1.08)	1271.32 (11.77, 43.68)
Reaching for/lighting/smoking/extinguishing cigar/cigarette	4 (0.04, 0.19)	654.34 (6.06, 37.14)
Reaching for personal body-related item/personal hygiene/removing/adjusting clothing/jewelry/contact lenses or glasses	53 (0.49, 1.11)	572.66 (5.30, 15.90)
Other non-specific internal eye glance	4 (0.04, 0.19)	9.48 (0.09, 0.47)
Other known secondary task	21 (0.19, 0.78)	448.60 (4.15, 18.07)

Table B2 Total number of effectively coded driver behaviors and their corresponding driver behavior category assignments (total numbers, mean and standard deviation of each total number, are related to ten-minute segments).

Driver behaviors categorized as violation	Total number	M	SD
Exceeded speed limit	51	0.47	0.86
Illegal passing on right	86	0.80	1.61
Illegal passing (but on right)	9	0.08	0.34
Other improper or unsafe passing	4	0.04	0.23
Cutting in, too close in front/behind other vehicle	6	0.06	0.23
Following too closely	18	0.17	0.54
Failed to signal/Improper signal (applies to planned maneuvers, not sudden evasive maneuvers)	61	0.56	1.76
Signal violation, tried to beat signal change	1	0.01	0.10
Stop sign violation, ''rolling stop"	23	0.21	0.66
Stop sign violation, intentionally ran stop sign at speed	1	0.01	0.10
Sudden or improper braking (when clearly deliberate)	18	0.17	0.54
Sudden or improper stopping on roadway (when clearly deliberate)	5	0.05	0.40
Driver engaged in behavior not described in ''SHRP 2 categories"	6	0.06	0.23
Aggressive expressions categorized as violation	Total number	M	SD
Forcing to side	1	0.01	0.10
Bump	1	0.01	0.10
Blocking	3	0.03	0.17
Horn/Flash lights (when clearly not used as a warning signal)	23	0.21	0.60
Driver behaviors categorized as error	Total number	M	SD
Driving in other vehicle's blind zone	1	0.01	0.10
Signal violation, apparently did not see signal	2	0.02	0.14
Unintentional lane deviation	51	0.47	0.86
Driver behaviors categorized as unclassified unsafe driving behavior	Total number	M	SD
Drowsy, sleepy, asleep, fatigued	4	0.04	0.23
Exceeded safe speed but not speed limit	2	0.02	0.14
Improper turn, wide right/left turn	20	0.19	0.46
Improper turn, cut corner on right/left turn	21	0.19	0.50
Driver engaged in behavior not described in ''SHRP 2 categories"	5	0.05	0.40
Driver behaviors categorized as other driver behavior	Total number	M	SD
Avoiding pedestrian	1	0.02	0.14
Avoiding other vehicle	14	0.13	0.43
Apparent unfamiliarity with roadway	1	0.01	0.10

References

Anund, A., Kecklund, G., Peters, B., Forsman, Å., Lowden, A., & Åkerstedt, T. (2008). Driver impairment at night and its relation to physiological sleepiness. Scandinavian Journal of Work, Environment & Health, 142–150.
Bachoo, S., Bhagwanjee, A., & Govender, K. (2013). The influence of anger, impulsivity, sensation seeking and driver attitudes on risky driving behaviour among post-graduate university students in Durban, South Africa. Accident Analysis & Prevention, 55, 67–76.
Beck, K. H., Yan, F., & Wang, M. Q. (2007). Cell phone users, reported crash risk, unsafe driving behaviors and dispositions: A survey of motorists in Maryland. Journal of Safety Research, 38(6), 683–688.
Bina, M., Graziano, F., & Bonino, S. (2006). Risky driving and lifestyles in adolescence. Accident Analysis & Prevention, 38(3), 472–481.
Blatt, A., Pierowicz, J., Flanigan, M., Lin, P.-S., Kourtellis, A., Jovanis, P.,... Hoover, M. (2015). Naturalistic driving study: Field data collection. SHRP 2 Report S2- S07-RW-1.
Bolker, B. M., Brooks, M. E., Clark, C. J., Geange, S. W., Poulsen, J. R., Stevens, M. H. H., & White, J.-S. S. (2009). Generalized linear mixed models: A practical guide for ecology and evolution. Trends in Ecology & Evolution, 24(3), 127–135.
Bond, C. F., & Titus, L. J. (1983). Social facilitation: A meta-analysis of 241 studies. Psychological Bulletin, 94(2), 265.
Caird, J. K., Johnston, K. A., Willness, C. R., Asbridge, M., & Steel, P. (2014). A meta-analysis of the effects of texting on driving. Accident Analysis & Prevention, 71, 311–318.
Campbell, K. (2012). The SHRP 2 naturalistic driving study: Addressing driver performance and behavior in traffic safety. Wahsington, DC: Transportation Research Board of the National Academies.
Carney, C., McGehee, D., Harland, K., Weiss, M., & Raby, M. (2015). Using naturalistic driving data to assess the prevalence of environmental factors and driver behaviors in teen driver crashes. Washington, DC: AAA Foundation for Traffic Safety.
Carsten, O., & Merat, N. (2015). Protective or Not? In 4th International Conference on Driver Distraction & Inattention.
Chan, M., & Singhal, A. (2013). The emotional side of cognitive distraction: Implications for road safety. Accident Analysis & Prevention, 50, 147–154.
Clarke, D. D., Ward, P., Bartle, C., & Truman, W. (2006). Young driver accidents in the UK: The influence of age, experience, and time of day. Accident Analysis & Prevention, 38(5), 871–878.
Clarke, D. D., Ward, P., & Truman, W. (2005). Voluntary risk taking and skill deficits in young driver accidents in the UK. Accident Analysis & Prevention, 37(3),
Coxe, S., West, S. G., & Aiken, L. S. (2009). The analysis of count data: A gentle introduction to poisson regression and its alternatives. Journal of Personality Assessment, 91(2), 121–136.
Dahlen, E. R., Martin, R. C., Ragan, K., & Kuhlman, M. M. (2005). Driving anger, sensation seeking, impulsiveness, and boredom proneness in the prediction of unsafe driving. Accident Analysis & Prevention, 37(2), 341–348.
Dahlen, E. R., & White, R. P. (2006). The Big Five factors, sensation seeking, and driving anger in the prediction of unsafe driving. Personality and Individual Differences, 41(5), 903–915.
Deffenbacher, J. L., Lynch, R. S., Oetting, E. R., & Swaim, R. C. (2002). The driving anger expression inventory: A measure of how people express their anger on the road. Behaviour Research and Therapy, 40(6), 717–737.
Delhomme, P., Chaurand, N., & Paran, F. (2012). Personality predictors of speeding in young drivers: Anger vs. sensation seeking. Transportation Research Part F: Traffic Psychology and Behaviour, 15(6), 654–666.
Dingus, T. A., Guo, F., Lee, S., Antin, J. F., Perez, M., Buchanan-King, M., & Hankey, J. (2016). Driver crash risk factors and prevalence evaluation using naturalistic driving data. Proceedings of the National Academy of Sciences, 113(10), 2636–2641.
Durbin, D. R., McGehee, D. V., Fisher, D., & McCartt, A. (2014). Special considerations in distracted driving with teens. Annals of Advances in Automotive Medicine, 58, 69.
Fitch, G. M., Hanowski, R., Burnett, G., & Crundall, D., 2013. Development of a protocol to classify drivers' emotional conversation. In 3rd international conference on driver distraction and inattention 2013.
Fournier, D. A., Skaug, H. J., Ancheta, J., Ianelli, J., Magnusson, A., Maunder, M. N., ... Sibert, J. (2012). AD Model Builder: Using automatic differentiation for statistical inference of highly parameterized complex nonlinear models. Optimization Methods and Software, 27(2), 233–249.
Gardner, W., Mulvey, E. P., & Shaw, E. C. (1995). Regression analyses of counts and rates: Poisson, overdispersed Poisson, and negative binomial models. Psychological Bulletin, 118(3), 392–404.
Gstalter, H., & Fastenmeier, W. (2010). Reliability of drivers in urban intersections. Accident; Analysis and Prevention, 42(1), 225–234.
Hatfield, J., & Fernandes, R. (2009). The role of risk-propensity in the risky driving of younger drivers. Accident Analysis & Prevention, 41(1), 25–35.
Hickman, J. S, Hanowski, R. J, & Bocanegra, J. (2010). Distraction in commercial trucks and buses: Assessing prevalence and risk in conjunction with crashes and near-crashes (reprt no. FMCSA-RRR-10-049). Washington, DC: Federal Motor Carrier Safety Administration.
Horrey, W. J., & Wickens, C. D. (2006). Examining the impact of cell phone conversations on driving using meta-analytic techniques. Human Factors: The Journal of the Human Factors and Ergonomics Society, 48(1), 196–205.
Hubbard, A. E., Ahern, J., Fleischer, N. L., van der Laan, M., Lippman, S. A., Jewell, N., ... Satariano, W. A. (2010). To GEE or Not to GEE. Epidemiology, 21(4), 467–474.
Jellentrup, N., Metz, B., & Rothe, S. (2011). Can talking on the phone keep the driver awake? Results of a field-study using telephoning as a countermeasure against fatigue while driving. In Proceedings of the 2nd international conference on driver distraction and inattention.
Jeon, M., Walker, B. N., & Yim, J.-B. (2014). Effects of specific emotions on subjective judgment, driving performance, and perceived workload. Transportation Research Part F: Traffic Psychology and Behaviour, 24, 197–209.
Johansson, Ö., Wanvik, P. O., & Elvik, R. (2009). A new method for assessing the risk of accident associated with darkness. Accident Analysis & Prevention, 41 (4), 809–815.
Klauer, S. G, Dingus, T. A, Neale, V. L, Sudweeks, J. D, & Ramsey, D. J (2006). The impact of driver inattention on near-crash/crash risk: An analysis using the 100 car naturalistic driving study data (report no. DOT HS 810 594). Washington, DC: National Highway Traffic Safety Administration.
Klauer, S. G., Guo, F., Simons-Morton, B. G., Ouimet, M. C., Lee, S. E., & Dingus, T. A. (2014). Distracted driving and risk of road crashes among novice and experienced drivers. New England Journal of Medicine, 370(1), 54–59.
Nesbit, S. M., & Conger, J. C. (2011). Evaluation of cognitive responses to anger-provoking driving situations using the Articulated Thoughts during Simulated Situations procedure. Transportation Research Part F: Traffic Psychology and Behaviour, 14(1), 54–65.
Nesbit, S. M., Conger, J. C., & Conger, A. J. (2007). A quantitative review of the relationship between anger and aggressive driving. Aggression and Violent Behavior, 12(2), 156–176.
Olson, R. L, Hanowski, R. J, Hickman, J. S, & Bocanegra, J. L. (2009). Driver distraction in commercial vehicle operations (report no. FMCSA-RRR-09-042). Washington, DC: Federal Motor Carrier Safety Administration.
Owens, D. A., & Sivak, M. (1996). Differentiation of visibility and alcohol as contributors to twilight road fatalities. Human Factors: The Journal of the Human Factors and Ergonomics Society, 38(4), 680–689.
Platten, F., Milicic, N., Schwalm, M., & Krems, J. (2013). Using an infotainment system while driving A continuous analysis of behavior adaptations. Transportation Research Part F: Traffic Psychology and Behaviour, 21, 103–112.
Precht, L., Keinath, A., & Krems, J. F. (2017). Effects of driving anger on driver behavior Results from naturalistic driving data. Transportation Research Part F: Traffic Psychology and Behaviour, 45, 75–92.
Preusser, D. F. (2003). Young driver crash risk. In Annual proceedings/association for the advancement of automotive medicine (Vol. 47, p. 527).
Qiu, L., & Nixon, W. (2008). Effects of adverse weather on traffic crashes: Systematic review and meta-analysis. Transportation Research Record: Journal of the Transportation Research Board, 2055, 139–146.

R Core Team (2016). R: A Language and Environment for Statistical Computing. Vienna, Austria. Retrieved from <https://www.R-project.org/>.

Reason, J. (1990). Human error. Cambridge: Cambridge University Press.

Reason, J., Manstead, A., Stradling, S., Baxter, J., & Campbell, K. (1990). Errors and violations on the roads: A real distinction? Ergonomics, 33(10–11),

Rosenbloom, T., & Perlman, A. (2016). Tendency to commit traffic violations and presence of passengers in the car. Transportation Research Part F: Traffic Psychology and Behaviour, 39, 10–18.

Ross, V., Jongen, E. M. M., Brijs, K., Brijs, T., & Wets, G. (2016). Investigating risky, distracting, and protective peer passenger effects in a dual process

framework. Accident; Analysis and Prevention, 93, 217–225. Rowe, R., Roman, G. D., McKenna, F. P., Barker, E., & Poulter, D. (2015). Measuring errors and violations on the road: A bifactor modeling approach to the

Driver Behavior Questionnaire. Accident; Analysis and Prevention, 74, 118–125. Simmons, S. M., Hicks, A., & Caird, J. K. (2016). Safety-critical event risk associated with cell phone tasks as measured in naturalistic driving studies: A

systematic review and meta-analysis. Accident Analysis & Prevention, 87, 161–169. Singh, S. (2015). Critical reasons for crashes investigated in the national motor vehicle crash causation survey (traffic safety facts crashdstats. Report no. DOT HS

812 115). Washington, DC: National Highway Traffic Safety Administration. Skaug, H., Fournier, D., Bolker, B., Magnusson, A., & Nielsen, A. (2016). Generalized linear mixed models using AD model builder. R package version 0.8.3.3. Staubach, M. (2009). Factors correlated with traffic accidents as a basis for evaluating Advanced Driver Assistance Systems. Accident Analysis & Prevention, 41

(5), 1025–1033. Stephens, A. N., Trawley, S. L., Madigan, R., & Groeger, J. A. (2013). Drivers display anger-congruent attention to potential traffic hazards. Applied Cognitive Psychology, 27(2), 178–189.

Strayer, D. L., Drews, F. A., & Johnston, W. A. (2003). Cell phone-induced failures of visual attention during simulated driving. Journal of Experimental Psychology: Applied, 9(1), 23.

Strayer, D. L., Cooper, J. M., Turrill, J., Coleman, J., Medeiros-Ward, N., & Biondi, F. (2013). Measuring cognitive distraction in the automobile.

Strayer, D. L., Turrill, J., Coleman, J. R., Ortiz, E. V., & Cooper, J. M. (2014). Measuring cognitive distraction in the automobile II: Assessing in-vehicle voice-based interactive technologies. Washington, DC: AAA Foundation for Traffic Safety.

Summala, H., Rajalin, S., & Radun, I. (2014). Risky driving and recorded driving offences: A 24-year follow-up study. Accident Analysis & Prevention, 73, 27–33. Tian, R., Li, L., Chen, M., Chen, Y., & Witt, G. J. (2013). Studying the effects of driver distraction and traffic density on the probability of crash and near-crash events in naturalistic driving environment. IEEE Transactions on Intelligent Transportation Systems, 14(3), 1547–1555.

Tivesten, E., & Dozza, M. (2015). Driving context influences drivers' decision to engage in visual–manual phone tasks: Evidence from a naturalistic driving study. Journal of Safety Research, 53, 87–96.

Toroyan, T. (2015). Global status report on road safety 2015. Geneva, Switzerland: World Health Organization.

Transportation Research Board of the National Academies of Science. (2013). The 2nd Strategic Highway Research Program Naturalistic Driving Study Dataset. Available from the SHRP2 NDS InSight Data Dissemination web site: https://insight.shrp2nds.us.

Victor, T., Dozza, M., Bärgman, J., Boda, C.-N., Engström, J., Flannagan, C., ... Markkula, G. (2015). Analysis of Naturalistic Driving Study Data: Safer Glances, Driver Inattention, and Crash Risk. (No. SHRP 2 Report S2–S08A-RW-1). Washington, DC.

Vollrath, M., Meilinger, T., & Krüger, H.-P. (2002). How the presence of passengers influences the risk of a collision with another vehicle. Accident Analysis & Prevention, 34(5), 649–654.

Wells, P., Tong, S., Sexton, B., Grayson, G., & Jones, E. (2008). Cohort II: A Study of Learner and New Drivers: Volume 1 - Main Report. Road Safety Research Report No. 81. London: Department for Transport.

Winter, J. de, & Dodou, D. (2010). The driver behaviour questionnaire as a predictor of accidents: A meta-analysis. Journal of Safety Research, 41(6), 463–470. Young, R. (2015). Revised odds ratio estimates of secondary tasks: A re-analysis of the 100-Car naturalistic driving study data. In Proceedings of Society of Automotive Engineers, Detroit, MI.

Young, K. L, & Regan, M. A (2013). Defining the relationship between behavioural adaptation and driver distraction. In Behavioural adaptation and road safety. Theory, evidence and action (pp. 227–243). CRC Press, Taylor and Francis Group Boca Raton.

Young, K., Regan, M., & Hammer, M. (2007). Driver distraction: A review of the literature. Distracted Driving, 379–405.

Young, K. L., Salmon, P. M., & Cornelissen, M. (2013). Distraction-induced driving error: An on-road examination of the errors made by distracted and undistracted drivers. Accident Analysis & Prevention, 58, 218–225.

Young, K. L., Salmon, P. M., & Lenne, M. G. (2013). At the cross-roads: An on-road examination of driving errors at intersections. Accident; Analysis and Prevention, 58, 226–234.

Zhang, T., & Chan, A. H. S. (2016). The association between driving anger and driving outcomes: A meta-analysis of evidence from the past twenty years. Accident Analysis & Prevention, 90, 50–62.

Zhang, T., Chan, A. H. S., & Zhang, W. (2015). Dimensions of driving anger and their relationships with aberrant driving. Accident; Analysis and prevEntion, 81, 124–133.

Zhao, N., Reimer, B., Mehler, B., D'Ambrosio, L. A., & Coughlin, J. F. (2013). Self-reported and observed risky driving behaviors among frequent and infrequent cell phone users. Accident Analysis & Prevention, 61, 71–77.

Zuckerman, M. (1994). Behavioral expressions and biosocial bases of sensation seeking. Cambridge University Press.

Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A., & Smith, G. M. (2009). Mixed effects models and extensions in ecology with R. New York: Springer.

Glossary

DBQ: Driver Behaviour Questionnaire GLMM: Generalized Linear Mixed Model NDS: Naturalistic driving study SHRP 2: Second Strategic Highway Research Program VTTI: Virginia Tech Transportation Institute

prechtidentifyingmainfactorscontributing2017.md 306 KB 履歴 Raw

status: converted

Transportation Research Part F

Identifying the main factors contributing to driving errors and traffic violations – Results from naturalistic driving data q

article info

Article history: Received 5 February 2017 Received in revised form 27 April 2017 Accepted 3 June 2017 Available online 20 June 2017

abstract

1. Introduction

1.1. Aberrant driving behaviors

1.2. Driver distraction

1.2.1. Secondary task demands

1.2.2. Driving task demands

1.2.3. Driving anger

1.3. Presence of one or more passengers

1.4. Driver characteristics

1.5. Investigative approach

1.6. The current study – aims and hypotheses

2. Method and materials

2.1. Data

2.1.1. Final data sample

2.2. Variables and data coding

2.2.1. Dependent variables

2.2.2. Independent variables

2.2.3. Data coding procedure

2.3. Data analysis

2.3.1. Analysis of aberrant driving behaviors

2.3.2. Analysis of secondary tasks

3. Results

3.1. Driver characteristics

3.2. Driving task demands

3.3. Secondary tasks and passenger presence

3.4. Emotional impairment

3.5. Aberrant driving behaviors

3.6. Predictors of aberrant driving behaviors

3.6.1. Predictors of violations

3.6.2. Predictors of errors

3.7. Predictors of secondary tasks

3.7.1. Passenger in adjacent seat – talking without looking/while looking at passenger (low crash risk)

3.7.2. Cell phone, talking/listening, hand-held (moderate crash risk)

3.7.3. Adjusting/monitoring climate control/radio (moderate crash risk)

3.7.4. Cell phone, texting/browsing/dialing, hand held (high crash risk)

3.7.5. External distraction (high crash risk)

4. Discussion

4.1. Driving task demands

4.2. Secondary task demands

4.3. Emotional impairments

4.4. Presence of one or more passengers

4.5. Driver characteristics

4.6. Limitations

5. Conclusion

Funding

Acknowledgements

Appendix A. Used coding scheme

Appendix A (continued)

Appendix A (continued)

Appendix A (continued)

Appendix B

References

Glossary

prechtidentifyingmainfactorscontributing2017.md 306 KB

履歴 Raw

Identifying the main factors contributing to driving errors and traffic violations – Results from naturalistic driving data ^q