Driver profiling – Data-based identification of driver behavior dimensions and affecting driver characteristics for multi-agent traffic simulation

Transportation Research Part F 64 (2019) 361–376

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

Driver profiling – Data-based identification of driver behavior dimensions and affecting driver characteristics for multi-agent traffic simulation Manuela Witt a, Klaus Kompaß b, Lei Wang b, Ronald Kates c, Marcus Mai d, Günther Prokop d a

Department for Vehicle Safety, BMW Group, Knorrstraße 147, 80788 Munich, Germany Department for Vehicle Safety, BMW Group, Munich, Germany c REK Consulting, Munich, Germany d Chair of Automobile Engineering, Dresden University of Technology, Dresden, Germany b

a r t i c l e

i n f o

Article history: Received 8 November 2018 Received in revised form 13 May 2019 Accepted 13 May 2019

Keywords: Driver behavior Multi-agent traffic simulation Prospective impact assessment Cognitive driver behavior modeling Driver characteristics Driver personality Automated driving

a b s t r a c t This paper focusses on the role of driver individuality in the field of cognitive driver behavior modeling for the prospective safety impact assessment of advanced driver assistance systems (ADAS) and automated driving functions. Virtual traffic simulation requires valid models for the environment, the vehicle and the driver. Especially modeling human driver behavior is a major challenge, which in recent years has already led to the development of various driver models for the purpose of virtual simulation. Modeling human behavior in traffic with a precise representation of human cognition, capability and individuality, are crucial demands, which require thorough investigation and understanding of the human driver. Current driver behavior models often leave aside the aspect of driver individuality and lack the consideration of differences in driving behavior between different drivers. To take into account all the aspects from complex human cognitive processes to individual differences in action implementation, the Stochastic Cognitive Model (SCM) was developed. The SCM is based on five subcomponents: gaze control, information acquisition, mental model, action manager and situation manager (=decision making process) and action implementation. The aim of the present study is to provide a basis for establishing a solid logic for the integration of driver individuality into the current structure of the SCM by creating a new submodule that takes into account several behavior affecting driver characteristics. This subcomponent controls the stochastic variance in several driver behavior parameters, such as velocity or comfort longitudinal acceleration. In a representative driving simulator study with 43 participants, driver behavior on the highway was investigated and thoroughly analyzed. Information about several relevant driver characteristics and personality traits of the participants was collected and a logical hierarchical model was set up to cluster several dependent and independent variables into four layers: independent manifest driver variables, such as age or gender (Level 1), latent driver personality factors, such as thrill seeking or anxiety (Level 2), driver behavior dimensions, such as dynamics and law conformity (Level 3), and various dependent driver behavior parameters, such as velocity, acceleration or speed limit violation (Level 4). Multiple linear regression analyses were run to find the individual driver characteristics and personality traits, by which most of the stochastic variance in the measured driver behavior parameters can be explained. Subsequently, a principal component analysis (PCA) was run to test, if the previously clustered driver behavior parameters were loading on the presumed behavioral dimensions on the third level of the model to identify significant components

E-mail addresses: [email protected] (M. Witt), [email protected] (K. Kompaß), [email protected] (L. Wang), Ronald.Kates@ t-online.de (R. Kates), [email protected] (M. Mai), [email protected] (G. Prokop) https://doi.org/10.1016/j.trf.2019.05.007 1369-8478/Ó 2019 Elsevier Ltd. All rights reserved.

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of driver behavior, such as dynamics or law conformity. Results of the present study show significant correlations between driver characteristics and driver behavior parameters. According to the results of the PCA, variability in driver behavior can be explained to a great extent by three largely independent components: (1) Speed and cruise control, (2) Dynamics and (3) Driver performance. With the consideration of driver individuality in driver behavior models for the agent-based traffic simulation, validity of the results from prospective safety impact assessment analyses of automated driving functions can be enhanced. Beyond that, the findings of the current study can be used as a solid basis for the development of adaptive functions in the field of vehicle automation, considering the different driving skills and preferences of drivers with different individual profiles. Ó 2019 Elsevier Ltd. All rights reserved.

1. Introduction Enhancing safety in road traffic is a basic claim in continuous vehicle development and is at the same time one of the main needs of customers and drivers in the broad population (see Kompaß, Helmer, Blaschke, & Kates, 2014; Stylidis, Hoffenson, Wickman, Söderman, & Söderberg, 2014). Integral vehicle safety means connecting technologies for passive and active safety to prevent accidents and mitigate the consequences of inevitable accidents. With automated driving functions brought to the fore, active safety systems, which are activated during the pre-crash phase (e.g. emergency braking) before passive safety technologies are firing (e.g. airbag deployment), can help to enhance traffic safety and reduce fatalities through road accidents (see Kompaß & Huber, 2009). Assessment of passive safety components in real and virtual crash tests as well as accident data analyses are substantial methods for ensuring and improving vehicle safety. However, before the launch of newly developed automated functions, their impact on traffic safety needs to be accurately assessed beforehand. Virtual traffic simulation makes a massive contribution to testing these systems for their impact on safety in road traffic. Valid models for the environment, the system and the driver are required, to make resilient statements based on results from traffic simulation (see Eckstein & Zlocki, 2013; Helmer, Wang, Kompaß, & Kates, 2015; Wachenfeld & Winner, 2015). As introduction to this paper, challenges for modeling the behavior of virtual drivers (=agents) will be demonstrated. Especially the necessity of considering driver individuality, which means considering the impact of driver characteristics and driver personality on driver behavior, will be discussed. Previous research on the correlation between driver characteristics and differences in driver behavior is summarized in 1.2. The SCM and its submodules are described briefly in Section 1.3. In Section 2, research questions and assumed hypotheses are presented. Study design and the used methods in the current study are described in Section 3. Results of multiple regression analyses for driver behavior parameters and principal component analysis for the clustering of these parameters are presented and discussed in Sections 4 and 5. The mathematical approach, using Kernel Density Estimation (KDE), for integrating driver individuality into the SCM, is described and further work is outlined in Section 6. 1.1. Prospective safety assessment of ADAS Prospective safety assessment of ADAS is an essential method for calculating the benefit of a technology in terms of traffic safety (see Eckstein & Zlocki, 2013; Kompaß, Helmer, Wang, & Kates, 2015). Virtual experiments in traffic simulation allow analyzing the safety effects of a system prior to its market introduction. Thereby, a large number of different situations can be investigated at reasonable cost and without the risk of harming anybody (see Wachenfeld & Winner, 2015). Different data sources can be used to generate representative traffic simulations, e.g. driving simulator studies, driving studies on the test track, field operational tests and virtual simulation. For reliable statements regarding the effectiveness of the tested function based on the results from virtual testing, valid models for the vehicle, the environment and the driver are needed (see Helmer et al., 2015; Wang, Fahrenkrog, Vogt, Jung, & Kates, 2017). The main challenge in the virtual simulation approach is, that it requires a detailed and correct representation of interactive processes between driver, system and environment (see Mai, 2017). Moreover, modeling human cognition and driver behavior as meticulous as possible, plays a major role regarding the quality of traffic simulation with virtual agents. In the field of driver modeling, several driver models have been set up, which also represent approaches for modeling human cognition and behavior. For further information about other driver behavior models see e.g. Anderson, Matessa, and Lebiere (1997) (ACT-R), Christen and Huang (2008) (TRM) or Delorme (2001) (COSMODRIVE). The driver model, which is described in this paper and which will be enriched by the results of the present paper, is called the Stochastic Cognitive Model (SCM). The SCM contains human cognitive processes, such as information processing and decision making, as well as various aspects of driver behavior (see Mai, 2017; Prokop, Mai, & Weller, 2014; Wang et al., 2017). When taking a look at real traffic on the road, it becomes clear that the way drivers behave is often quite difficult to understand or predict and therefore, even more challenging to model. Drivers differ strongly in their behavior, which makes it obvious that there is a necessity to also consider driver individuality and its impact on driver behavior. Current driver behavior models do not contain such individual differences between drivers and this is where

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the present paper aims at filling a crucial gap of research. In the following, past research on driver behavior modeling and moreover, studies about the role of driver individuality in the driving context in general, will be presented. 1.2. Driver characteristics, personality and driver behavior The way people drive varies widely. Interindividual differences in behavior between drivers need to be considered when lodging the claim for modeling driver behavior adequately. Driver characteristics, such as age, gender, driving experience, and driver personality as well as the perception of one’s own driving skills are evidentially related to differences in (risky) driving behavior, traffic violations and accident risk (e.g. DeJoy, 1992; Finn & Bragg, 1986). In the following, findings of several studies from the current field of research are presented. 1.2.1. Demographics and driving experience There is a large number of studies, in which the impact of demographics and driving experience on driver behavior has been investigated. Laapotti, Keskinen, Hatakka, and Katila (2001) examined self-reported health-related and safety-related driving habits of 1250 adult drivers. Gender differences were found regarding the likelihood of safe behavior (e.g. wearing seat belts, observing speed limits, disapproval of drinking and driving), insofar as women reported higher compliance to safety habits than men. In addition, the usage of seat belts increased with increasing age and higher income. Speed limit compliance increased with age and decreased with better education and higher income. In a survey of 93 drivers between 18 and 50 years, Finn and Bragg (1986) found that compared to peers and older drivers, young drivers judged their own risk of having an accident to be significantly lower than theirs and thereby, tended to overestimate their own driving skills. Consistently, Matthews and Moran (1986) asked 46 drivers between 18 and 50 years about their subjective estimation of their own likelihood of having a traffic accident as well as questions related to their driving performance. Furthermore, they asked them to rate videotaped driving sequences regarding their accident risk. Younger age and optimistic ratings regarding their own driving skills were significantly correlated with lower perception of accident risk regarding their personal likelihood of having an accident and the estimated accident risk in the videotaped scenarios, which they were presented. Indeed, according to statistics from the German federal statistical office, young drivers between 25 and 35 years are the most common victims of traffic accidents (Bundesamt, 2019). Moreover, between January and October 2018 more than 190 000 men were involved in traffic accidents in Germany, whereas the number of female victims ranged around 145 000. Among these victims, more than 2 100 men were killed compared to approximate 650 women (Bundesamt, 2019). Studies investigated further gender effects on driving behavior. In a survey of 136 college students, DeJoy (1992) found significant correlations between gender and perception of their own driving skills, general perception of risk and subjective judgement of the seriousness of acts of risky behavior. Male drivers tended to be more optimistic in judging their own driving skills than female drivers and perceived risky behaviors as less serious and less likely to result in accidents than females. Consistently, several studies found that risky driving behavior seemed to be more common in young male drivers. McKnight and McKnight (2000) carried out a survey with reports from 1000 drivers under the age of 20 and found that young males were significantly more often involved in speeding-related crashes and crashes due to drunk driving than females (see also Shinar, Schechtman, & Compton, 2001). Moreover, they found that age and driving experience were significant predictors for higher rates of novice crashes due to lapses in visual search. Consistently, Arnett, Offer, and Fine (1997) found that in young drivers, male gender was positively correlated with higher sensation seeking, more aggressiveness and reckless driving. In contrast, females more frequently reported crashes due to lapses in visual search at intersections before left turns. Hence, according to the current state of research, age, gender and driving experience seem to be significantly correlated to various driving behaviors, such as risky driving, risk perception and accident likelihood, and need to be taken into account, when accurately modeling driver behavior is aimed at. 1.2.2. Risk propensity, sensation seeking and risky behavior Risky driving behavior has been investigated in several studies and research brought up very similar results: In a survey with 724 college students, Greene, Krcmar, Walters, Rubin, and Hale (2000) found positive correlations between sensation seeking, a risk-taking personality and risky behavior, e.g. drinking and driving or risky driving. In a study from TaubmanBen-Ari, Mikulincer, and Iram (2004), self-reports of 295 combat and service soldiers between 18 and 21 years revealed significant correlations between higher frequencies of reckless and risky driving and disregard for negative consequences as well as challenge and self-efficacy appraisals. Along with risk propensity, several studies have investigated correlations between sensation seeking and differences in driving behavior, accident rates and violations. In a survey of 312 undergraduate students, Dahlen and White (2006) found that higher sensation seeking and driving anger were related to more selfreported aggressive and risky driving behaviors. Consistent with these results, in a survey carried out by Sümer (2003), 295 professional truck, taxi and bus drivers reported more aberrant driving behavior and speeding when they pictured themselves as sensation seekers and as being more aggressive. Further studies based on questionnaires on sensation seeking, aggressiveness and proneness to take risks, also found significant correlations with higher rates of traffic violations, risky behavior and crash involvement (e.g. Arnett et al., 1997; Burns & Wilde, 1995; Greene et al., 2000; Horvath & Zuckerman, 1992; Jonah, 1997; Trimpop & Kirkcaldy, 1997). Moreover, in a survey carried out by Bachoo, Bhagwanjee, and Govender (2013), post-graduate university students with more reported driving anger, sensation seeking, urgency and lack of perseverance in daily activities reported significantly more acts of risky driving behavior. Similar results were reported by

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Dahlen, Martin, Ragan, and Kuhlman (2005), who found in a survey of 224 college students that more sensation seeking, impulsiveness, boredom proneness and driving anger were significantly correlated with more aggressive and risky driving and stronger expressions of driving anger. According to these findings, risk-taking personality traits, such as risk propensity and sensation seeking, are highly correlated with risky, aggressive and aberrant driving behavior as well as with higher rates of crash involvement. 1.2.3. Driving anger Comparable with research on the correlation between risk propensity, sensation seeking and driving behavior, several studies examined the role of driving anger in the observation of differences in behavior in traffic. In a survey carried out by Deffenbacher, Deffenbacher, Lynch, and Richards (2003), significant correlations between more self-reported driving anger and risky behavior as well as anger and aggression while driving were found. Mesken, Hagenzieker, Rothengatter, and DeWaard (2007) carried out a study, including questionnaires and a test drive on the road, and found that drivers, who reported more anger while driving, were driving with higher speed and exceeded speed limits more often than others. In a driving simulator study with 48 university students and employees, Stephens and Groeger (2009) observed higher velocities and more speed limit violations in individuals, who reported more driving anger. Maxwell, Grant, and Lipkin (2005) analyzed self-reports of 245 college students and employees and found that more angry and aggressive drivers demonstrated greater willingness to commit traffic violations, were more often involved in aggressive acts and were more prone to get angry, when they were confronted with impeding situations while they were driving. Underwood, Chapman, Wright, and Crundall (1999) carried out a survey of 100 drivers about driving anger and asked the participants to make notes about their daily trips every day over a small period of time. Results supported the findings of other research studies, indicating more traffic violations, driving errors and lapses as well as higher rates of (near) accidents when higher scores of driving anger were reported. Roidl, Siebert, Oehl, and Höger (2013) conducted research aiming on creating a multivariate model for the prediction of driver behavior with focus on driving anger and driver motivation as predictor variables. In their study, Roidl and colleagues reported that drivers with higher levels of anger as well as highly motivated drivers drove with higher velocities and accelerated stronger in longitudinal and lateral direction. In conclusion, there is evidence for significant correlations between driving anger, speeding, committing traffic violations and frequency of accident involvement. 1.2.4. Patience Less is known about the correlation between patience and observable driving behavior. Jenkins (1976) described different types of behavior patterns and characterized a Type A behavior pattern as the one of individuals with competitive achievement striving, an exaggerated sense of time urgency, aggressiveness and impatience. In a survey of 70 undergraduate and graduate psychology students between 18 and 57 years, Perry (1986) found that impatience in Type A individuals had an impact on higher accident frequency and more traffic rule violations. Due to the lack of further research about the correlation between impatience and differences in driver behavior, the current study considers patience as a personality trait and investigates its correlation with driver behavior parameters, such as velocity or time headway while car following. 1.2.5. Anxiety As is the case for the personality construct of patience, anxiety is very rarely investigated in the context of observing differences in driver behavior between individuals. Some studies indicated that fear was connected with higher levels of perceived risk on the road (e.g. Lerner, Gonzales, Dahl, Hariri, & Taylor, 2005; Mesken et al., 2007). However, little is known about the correlation between anxiety and different aspects of observable driver behavior. 1.2.6. Driver states In addition to the impact of stable driver characteristics and personality traits, driver states, such as fatigue or stress, can also have a significant impact on driver behavior. In a driving simulator study, Witt, Wang, Prokop, and Kompaß (2017) found that drivers tended to drive faster, when they were fatigued than when they were not. In contrast, drivers slowed down when they got stressed by the instructor. Matthews et al. (1998) carried out another driving simulator study and found that stress vulnerability was significantly correlated with lower driving control skills, higher disturbance of moods and greater caution. Briggs, Hole, and Land (2011) investigated the impact of emotional engagement on driving performance. In a conversation on the subject of spiders, frequency of driving errors and visual search of spider-phobic and non-spiderphobic drivers was measured. Results displayed significantly more driving errors, lapses and visual tunneling in the emotionally stronger engaged spider-phobic drivers. Thus, there seems to be an impact of the current state of the driver on driving performance. However, the focus of the present paper lies on the impact of stable driver characteristics and personality traits on driver behavior without the consideration of additionally affecting driver states. 1.2.7. Research gap and value of the current study As reported in the previous sections, research with respect to the correlation between driver personality and driver behavior is in most cases based on data from (mainly standardized) questionnaires. These are valid and highly important tools for gaining detailed information about personal characteristics, subjective opinions or feelings. For this reason, also in the current study, interviews based on standardized psychological questionnaires were conducted. Nevertheless, data from questionnaires or interviews can be distorted by social desirability or cognitive bias and must be interpreted with

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moderate caution (see Arthur & Graziano, 1996). Therefore, in the present study, a mixed method approach was used and an objective measurement of driver behavior in a dynamic driving simulator was carried out. Although studies guided under laboratory conditions do not provide an unreserved observation of natural behavior, studies have proven the validity of results from driving simulator studies (e.g. Underwood, Chapman, Brocklehurst, Underwood, & Crundall, 2003). However, due to the monitoring situation by the investigator, which the participants were informed about, it can’t be with certainty excluded that the test persons would behave in a different way than they would, if they weren’t aware of being constantly observed. Absolute values, derived from the current study can be susceptible for distortion due to mechanical specifications of the implemented vehicle model or the motion system of the driving simulator. For the analysis and verification of the proposed correlations between driver characteristics and interindividual behavioral differences, this will be acknowledged as a systematic confounding variable. When looking through current scientific literature, there exists no study, which has yet investigated correlations between the broad variety of personality traits, driver characteristics and driver behavior parameters, such as correlations to speeding or behavior while following. In fact, such findings would give important indications for identifying driver characteristics and personality traits that can have a significant impact on how drivers behave in traffic and for generating different stochastic distributions of driver behavior parameters depending on different driver profiles. Filling this gap of knowledge is one of the main targets of the current paper. Therefore, in the present study, a large variety of interindividual personal differences in driving behavior and their correlation to the broad spectrum of driver characteristics and driver personality were investigated. For the sake of completeness, it must be mentioned that in addition to interindividual differences in behavior between drivers, intraindividual differences due to the state of the driver, such as being fatigued or stressed, must be considered. However, in the present paper, only the interindividual differences in behavior between drivers are investigated and discussed. It is assumed that clustering these parameters and thereby, identifying superior driver behavior dimensions to be able to describe different driver profiles, will contribute to the essential knowledge for stochastic driver behavior modeling for virtual traffic simulation (see Fig. 1). For creating virtual agents, parametrization of driving behavior must be well conceived and its conclusiveness must be controlled. To avoid inconsistent behavior of virtual agents, e.g. driving with high velocities and at the same time preferring to drive on the right lane of the highway, which is usually used by slow driving vehicles or trucks, it is useful to cluster these driver behavior parameters to make sure that they are covaried during the simulation. Therefore and for a further investigation of dimensions, under which driver behavior parameters can be aggregated, a principal component analysis (PCA) was calculated. As a result, variability in a large spectrum of single driver behavior parameters, e.g. velocity, lane preference or distance to a preceding vehicle, could be logically linked and can now be covaried within virtual traffic simulation. In the following section, the SCM, into which the results of the current paper will be integrated, is described. 1.3. The stochastic cognitive model – the SCM For agent-based traffic simulation, the SCM, a cognitive driver behavior model, has been developed, which considers cognitive human abilities, such as information processing and decision making, and also takes into account physiological and cognitive limitations of a human driver (e.g. limited preview distance or capacity of workload). Within the scope of this paper, only the basic structure and contents of the modules, which are integrated in the SCM, will be described in brief. For a detailed description of the theories, from which the structure of the SCM and its submodules are derived, see Mai (2017). The SCM is based on different submodules, into which cognitive and behavioral processes are divided. Along with these submodules, human cognition as well as self-determined decision making and behavior can be transferred on virtual drivers in agent-based traffic simulation (see Fig. 2).

Fig. 1. Impact of driver characteristics (=Traits) on driving behavior dimensions.

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Fig. 2. Submodular structure of the SCM.

1.3.1. Gaze control and information acquisition These submodules consider visual perception of stimuli in the traffic surrounding and their further processing. Thereby, the driver’s peripheral and foveal useful field of view as well as top-down and bottom-up controlled gaze distribution processes and basic theories from human selective perception are considered. More than 90% of the perceived information while driving is gathered through the visual sensory channel (see Crundall, Chapman, Phelps, & Underwood, 2003). Therefore, modeling visual perception already covers a major part of information acquisition. Haptic and acoustic information acquisition is not considered in the SCM yet, but will be integrated in the further course of model development. 1.3.2. Mental model This submodule is responsible for the recognition of situation patterns. Current information of the information acquisition submodule as well as information from working memory is processed with information from previous time steps. All gathered information is aggregated to describe the microscopic traffic properties and extract features of the environment that are needed in the next module. 1.3.3. Decision making process Situation Manager and Action Manager. Driver’s decision making process is located in the submodules Situation Manager and Action Manager. The task of these submodules is to assess the current situation according to the information derived from information processing in the Mental Model. Based on the outcome of information processing, a decision is made about the driver’s next action. For action selection, Bernoulli processes based on a sized intensity vector are considered. Actions are stored in the action pattern catalogue into primary (acceleration, deceleration, steering and normal driving), secondary (e.g. light activation, intention signals by activating indicators etc.) and tertiary driving actions (e.g. navigation use, adjusting air condition etc.). 1.3.4. Action implementation Finally, the inputs of the previous submodules are used to change pedal positions – accelerator as well as braking pedal – and steering wheel angle that result in longitudinal, lateral acceleration and yaw rate of the vehicle. By this, the movement of the vehicle for the next time step can be determined. 1.3.5. Driver characteristics By an additional submodule, considering driver characteristics and driver states, individual and interindividual driver differences and their impact on driver behavior will be integrated into the SCM. In a one-way stochastic process, traffic agents are provided with a set of individual driver characteristics that shift, widen or narrow baseline distributions of stochastic parameters in all submodules of the SCM. By this, the overall driver population can be modeled and virtual traffic simulation can be run as realistic as possible. For filling this submodule with valid data, hypotheses on the correlation between driver individuality and driver behavior, were generated. 2. Research questions and hypotheses Several studies found correlations between different driver characteristics and noticeable differences in observable driver behavior. As described in Section 1.2, there are various studies that focused on the influence of driver characteristics on risky driving, proneness to take risks (e.g. speeding) or the likelihood of having an accident. In contrast, studies on the impact of driver characteristics on single driver behavior parameters respectively, e.g. safety distance to preceding vehicles, acceleration, desired velocity, are missing. However, especially for driver behavior modeling, this is a crucial gap of research and lack of knowledge. Differences in driver behavior need to be considered in the construction of an appropriate driver behavior model for the virtual impact assessment of automated driving functions. Considering the necessity of getting a comprehensive understanding of individual factors that are underlying differences in driver behavior, research questions about driver specific influencing variables emerged. Based on various hypotheses on the impact of driver related factors as well as based on identified weaknesses of current driver behavior models, the following research questions were brought to the fore:

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(1) Which driver characteristics have a significant impact on driver behavior parameters? (2) Which driver characteristics explain the most variance in driver behavior parameters? (3) Which principal components underlie the variety of driver behavior parameters? Prior to the experiment, based on a logical path model, hypotheses covering correlating variables and regression coefficients for driver behavior parameters were generated. Fig. 3 illustrates the model, which contains the considered driver characteristics and driver behavior parameters, the hypotheses are focused at. It is assumed that manifest variables, such as age, gender and driving routine, have a significant impact on driving parameters, such as velocity, distance control, acceleration as well as on driver performance. Additionally, latent factors, such as personality traits, e.g. anxiety or risk tolerance, also have a significant impact on how drivers behave in traffic: Hypothesis 1: Manifest variables, in detail age, gender and driving routine have a significant impact on desired velocity, safety distance to the preceding vehicle, acceleration behavior, lane preference and speed limit compliance. Hypothesis 2: Latent personality traits, in detail risk tolerance, subscales of sensation seeking, driving anger, anxiety and patience have a significant impact on desired velocity, safety distance to the preceding vehicle, acceleration behavior, lane preference and speed limit compliance. Hypothesis 3: Age and driving experience have a significant impact on driver performance, defined as lane keeping quality and steering wheel angle speed. Elderly drivers have a poorer lane keeping quality than younger drivers and a higher steering wheel angle speed. Hypothesis 4: Driver behavior can be clustered into three different principal components: dynamics, law conformity and driver performance. For a common understanding of the driving behavior parameters that will be considered in the analysis of the current study, these parameters are clearly defined and summarized in Table 1.

Fig. 3. Logical path model of driver characteristics and driver behavior parameters.

Table 1 Overview of the considered driving behavior parameters. Parameter

Definition

Driving routine (Desired) Velocity [km/h]

Driven mileage in the past year Median velocity while free driving on road sections with no speed limits and no preceding vehicle in front or time headway (THW) to the preceding vehicle >= 2 sec Mean longitudinal acceleration when leaving a speed limited road section

Longitudinal comfort acceleration [m/s2] Lateral comfort acceleration [m/s2] Safety distance [sec] Speed limit violation [km/h] Steering wheel angle speed [rad/s] Lane departure [m] Lane preference [%]

Mean lateral acceleration while driving curvy road sections Median distance to the preceding vehicle in seconds THW Median deviance from given speed limits Mean steering wheel angle speed while driving Deviance of the vehicle from the middle of the current lane in meters Percentage of driving on the respective lanes on a three-lane motorway

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3. Method 3.1. Study design In a driving simulator study, drivers experienced a 70 km long car drive on a German highway. Driving was unassisted and the drivers had full responsibility for longitudinal and lateral movement of the vehicle at all times. Once the experiment started, the participants were driving without any distracting stimuli and with no social interaction with the instructor. The used driving simulator consisted of a full vehicle, mounted inside of a cupola, which was based on a moving hexapod that can translate the entering vehicle data into proper motion of the simulation platform. By means of the moving base of the hexapod, longitudinal as well as lateral movement can be simulated and drivers get haptic feedback that fits to what the drivers are presented in the visual simulation. In the driving simulator, the drivers were presented a 360°-surround-view of the driving scene, which was projected on the inside walls of the cupola. The dynamics of the implemented vehicle model in the driving simulation were similar to a BMW 5 series with low engine power. The driving simulator provided haptic feedback of the mocked acceleration behavior to the driver by pitching, yawing and rolling movements according to the actions of the driver in the simulation. The simulated route followed a standard road course on German highways with moderate curves and mostly straightaways. During the experiment, every participant experienced slightly critical, but common events, such as cut-ins or slow platoons of trucks on the right lane. This was part of the experiment to keep the driver in the loop and to investigate reaction times to critical situations and anticipation of the further development of situations with potential risks as a byproduct. However, the main focus of the analysis was on driver behavior in smoothly moving traffic and unobtrusive traffic sections without critical situations. In total, 50 licensed drivers were recruited by an independent market research agency. To make sure that the sample consisted of a group of drivers of different ages, four age groups were defined, towards which the recruiting of a representative sample of drivers was oriented (18–35 years old, 36–50 years old, 51–65 years old, 66 < years old). The age group was not considered as an additional variable in the further analysis. Before the start of the survey and the subsequent test drive in the driving simulator, every participant filled out and subscribed an informed consent for data acquisition and analysis. Every test person was paid reasonable compensation for their participation in the study. 3.2. Participants 50 participants were recruited by an independent market research agency. Seven participants didn’t show up on the day of the experiment, so the total sample size was reduced to N = 43. Due to simulator sickness, two additional participants dropped out of the experiment and the further data analysis. Thus, the final sample size was reduced to N = 41. Mean age was 46.27 years with a standard deviation of 15.94 years. The sample comprised 17 females and 24 males from the general population. 3.3. Survey and standardized questionnaires Before the test drive, participants filled out several standardized questionnaires containing items regarding demographics, driving experience and personality traits. Thereby, detailed information about the drivers, their individual personality traits and personal attitudes towards behavior in traffic was collected. The administered standardized psychological questionnaires are described in the following section. 3.3.1. Brief Sensation Seeking Scale (BSSS) The BSSS is a short version of the Sensation Seeking Scale (SSS-V), which was constructed by Zuckerman, Eysenck, and Eysenck (1978). It is a self-report measure for sensation seeking, which is a dispositional risk factor for self-endangering behavior, e.g. for unsafe driving. The BSSS consists of eight items in a forced-choice format with responses indicated on a five-level Likert scale (‘‘strongly disagree”, ‘‘disagree”, ‘‘neither disagree nor agree”, ‘‘agree”, and ‘‘strongly agree”). Each of the four primary dimensions of sensation seeking (experience seeking, boredom susceptibility, thrill and adventure seeking, disinhibition) is represented by two items. Internal consistency for the eight items is satisfying with Cronbach’s alpha = 0.76 (Hoyle, Stephenson, Palmgreen, Pugzles Lorch, & Donohew, 2002). 3.3.2. The short scale Risk Proneness-1 (R-1) The short scale R-1 from Beierlein, Kovaleva, and Kemper (2014) measures the self-reported risk proneness of a person with a single item asking for self-consideration of someone’s tendency to take risks (‘‘How do you see yourself - how willing are you in general to take risks?). Main test quality criteria of the R-1 are fulfilled. Test-retest reliability of the R-1 varies around r = 0.74, construct validity around r = 0.57. Based on the R-1, a one-item measurement for self-reported patience was included into the survey (‘‘How do you see yourself – how patient are you in general?”). 3.3.3. Deffenbacher driving anger scale (DAS) – Short form The DAS is a 14-item questionnaire that targets on self-reported driving anger with responses indicated on a five-level Likert scale (‘‘none at all”, ‘‘a little”, ‘‘some”, ‘‘much”, ‘‘very much”) (Deffenbacher, 1994). Drivers were asked how strongly

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they would be annoyed by provoking traffic situations that can potentially cause strong discontent. Internal consistency for the total score is satisfying with Cronbach’s alpha = 0.82 (Villieux & Delhomme, 2007). 3.3.4. State-Trait-Anxiety-Inventory (STAI-T) – Short version The Trait-section of the STAI contains items for measuring anxiety as a general anxious tendency (e.g. ‘‘I lack selfconfidence”, ‘‘I get in a state of tension or turmoil as I think over my recent concerns and interests.”) The 10-item German short version of the STAI-T by Grimm (2009) with responses on an eight-level Likert scale ranging from ‘‘not at all” to ‘‘absolutely” was used which bases on the original version from Spielberger, Gorsuch, and Lushene (1970). For the German version of the STAI-T Cronbach’s alpha for internal consistency varies between 0.88 and 0.94. Test-retest reliability ranges between r = 0.68 and r = 0.96 (Laux, Glanzmann, Schaffner, & Spielberger, 1981). For the further analysis, the percentage of approval was calculated as test score for general anxious tendency. 4. Results In the following section, results of two-sided correlation analyses, multiple linear regression analyses and principal component analysis of the presumed path model are reported. Prior to data analysis, missing values in the data set were evaluated. For modeling driver behavior, it is necessary to find values that represent the comfort acceleration of the driver. We used the end of speed limited sections as reference point for measuring acceleration under exactly the same conditions for every driver. Missing data can be traced back to the fact that some drivers were either ignoring speed limits or were not motivated to driver faster than 120 km/h after speed limit was lifted. In these cases, drivers did not accelerate and the algorithm didn’t find any data points fitting the requirements of the analysis. It was assumed here that missing data was missing at random (MAR) but not missing completely at random (MCAR). Missing values were imputed by using the expectation maximization algorithm (number of iterations = 16), and results were compared with listwise deletion of participants with missing data. Multiple imputation maximizes statistical power, reduces distortion of results due to a missing data bias, and provides an estimate of additional uncertainty resulting from missing data. 4.1. Research question 1: Correlations For the calculation of correlating driver characteristics and driver behavior, variables on the first, second and fourth level of the model (see Fig. 2) are considered. In the following sections results from correlation calculations, multiple linear regression analyses and the principal component analysis of the presumed model are reported. 4.1.1. Age Regarding correlations between variables on the first and on the second level, age correlated significantly with scores on three of the subscales of the BSSS: Boredom Susceptibility, r = 0.317, p < .05; Thrill and Adventure Seeking, r = 0.432, p < .05; Disinhibition, r = 0.360, p < .05). Regarding the fourth level, which contains the observable driver behavior parameters, age correlated significantly with the time headway (in seconds) to the preceding vehicle in front of the driver (r = 0.604; p < .001). Moreover, there was a significant correlation between the driver’s age and speeding parameters. Older drivers chose lower velocities than younger drivers while free driving (r = 0.491, p < .05). Additionally, younger drivers significantly higher exceeded given speed limits compared to older drivers (r = 0.471; p < .05). Mean lateral acceleration decreased with increasing age of the driver (r = 0.350; p < .05). Concerning driving performance parameters, higher values for mean steering wheel angle speed while following the course of the road way were correlating with increasing age of the driver (r = 0.480; p < .05). 4.1.2. Gender There were significant effects of gender on driving routine and driver behavior. Men reported higher mileage during the past year, which was defined as an indicator for driving routine (r = 0.335; p < .05). On driver behavior level, gender correlated significantly with median desired velocity. In accordance with prior research (e.g. Arnett et al., 1997; McKnight & McKnight, 2000), during free driving the male participants were driving significantly faster than women (r = 0.392; p < .05). Mean lateral acceleration was also higher for male drivers (r = 0.327; p < .05). In addition, right keeping factor, which is operationalized by the percentage of driving on the right lane, was lower for male drivers. This means, female drivers were driving significantly more frequently on the right lane than males (r = 0.267; p < .05; one-sided). 4.1.3. Driving routine More experienced drivers were exceeding speed limits greater than less experienced drivers. This means that when drivers reported more driving routine, the tendency to exceed given speed limits increased (r = 0.233; p < .05; one-sided). Matching this finding, drivers with more driving routine drove faster than drivers with less driving routine (r = 0.440; p < .05). Similar observations were found for lateral acceleration (r = 0.360; p < .05) as well as for the tendency to exceed lanes (r = 0.281; p < .05; one-sided). Moreover, experienced drivers were less frequently driving on the right lane than less experienced drivers (r = 0.302; p < .05). These results indicate that with increasing driving routine, drivers take more risks

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and behave superior in traffic. These findings are contrary to what has been found by other researchers, who reported risky behavior and higher crash rates among novices (DeJoy, 1992; McKnight & McKnight, 2000). 4.1.4. Risk tolerance Drivers with higher scores on the R-1, in detail, drivers with higher proneness to take risks, scored higher on the subscale for Boredom Susceptibility of the BSSS (r = 0.289; p < .05; one-sided). Considering driver behavior parameters, higher risk tolerance correlated significantly with higher mean velocity while free driving (r = 0.425; p < .05), higher mean longitudinal acceleration after exiting a road section with speed limits (r = 0.332; p < .05), higher mean lateral acceleration (r = 0.323; p < .05) and lower compliance to the obligation to drive on the right side of the road on German highways (r = 0.404; p < .05). These findings match findings of prior research (e.g. Dahlen & White, 2006; Jonah, 1997). 4.1.5. Sensation seeking and subscales Drivers, who scored higher on the Thrill and Adventure Seeking subscale of the BSSS, were driving significantly faster than drivers with lower scores (r = 0.284; p < .05; one-sided). Matching these results, these drivers were exceeding speed limits significantly further than drivers with lower values on the Thrill and Adventure Seeking scale (r = 0.363; p < .05). With increasing scores on this subscale, drivers tended to driver faster (r = 0.300; p < .05; one-sided) and to greater exceed given speed limits (r = 0.306; p < .05; one-sided). These findings are consistent with current literature on the correlation between sensation seeking and speeding or risky driving (e.g. Dahlen & White, 2006; Trimpop & Kirkcaldy, 1997). 4.1.6. Patience Drivers, who described themselves as being more patient, scored lower on three of the subscales of the BSSS: Boredom Susceptibility (r = 0.269; p < .05; one-sided), Thrill and Adventure Seeking (r = 0.275; p < .05; one-sided) and Disinhibition (r = 0.261; p < .05; one-sided). There were no significant correlations with driver behavior parameters on the fourth level. Patience will therefore not be considered in the further analysis. 4.1.7. Driving anger Higher scores on the DAS correlated significantly with higher scores on the Boredom Susceptibility subscale of the BSSS (r = 0.277; p < .05; one-sided). Moreover, drivers that reported higher tendency to get angry about potentially upsetting behavior of other traffic participants (e.g. ‘‘Someone is accelerating while you are trying to overtake him/her.”) kept larger distances to preceding vehicles (r = 0.314; p < .05). However, these drivers were ignoring the obligation to drive on the right side of the road more often than drivers with lower scores concerning self-reported driving anger (r = 0.341; p < .05). These findings are consistent with results from studies about the correlation between driving anger and driving behavior (e.g. Mesken et al., 2007; Stephens & Groeger, 2009). 4.1.8. Anxiety General tendency for being anxious correlated significantly with faster reaction base time to critical cut-ins (time to collision < 2.6 sec) (r = 0.329; p < .05; one-sided). In addition, more anxious drivers kept a longer distance to the preceding vehicle than less anxious drivers (r = 0.334; p < .05). Supporting hypothesis 1 and hypothesis 2, there were significant correlations between several driver characteristics and driver behavior parameters. Hypothesis 3 presumed that age and driving experience have a significant impact on driving performance. Findings on steering wheel angle speed support this hypotheses and indicate that with increasing age, drivers are steering more bumpily than younger drivers. Lane departure wasn’t significantly correlated to age or driving routine. 4.2. Research question 2: multiple linear regression analysis Multiple linear regression analyses were run for velocity, acceleration, safety distance, speed limit violation, exceeding lanes, driving on the right lane and steering wheel angle speed. 4.2.1. Velocity Age, risk tolerance, driving routine and gender were significant predictors for median speed while free driving (Table 2). A significant regression equation was found with which 52.2% of the variance in the investigated driver’s median speed can be explained: (F(4, 36) = 9.815, p < .001; R2 = 0.522). Driver’s predicted median speed while free driving is 147.723– 9.993 (female gender) 0.614 (age) + 4.586 (driving routine) + 5.515 (risk tolerance), whereas age is measured in years, risk tolerance is operationalized as the test score on the R-1 and driving routine is measured as driven kilometers in the past year (see Figs. 4–6). Predictors were screened negative for multicollinearity (VIF  1) and positive for variance homogeneity, which means that both criteria for running linear regression analyses were fulfilled. Partial effect size was g2P = 0.52. 4.2.2. Acceleration Longitudinal acceleration was significantly correlated with risk tolerance. As predictor for longitudinal acceleration risk tolerance explains 11% percent of the variance in the mean acceleration of the drivers. A significant regression equation was

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Table 2 Regression analysis for the prediction of velocity. Predictor

t

p

ß

(Intercept) Age (female) Gender Driving routine Risk tolerance

10.323 3.572 1.716 1.928 2.516

0.001 0.095 0.062 0.016

-0.417** -0.212 0.241 0.301*

N = 41. * p < .05. ** p < .01.

Fig. 4. Scatter plot with regression trend line for age and velocity.

Fig. 5. Scatter plot with regression trend line for risk tolerance and velocity.

found (F(1, 39) = 5.535; p < .05) with R2 = 0.124, which equals 0.249 + 0.024 (risk tolerance). Risk tolerance was tested positive for variance homogeneity. As described in the previous section lateral acceleration was significantly correlated with several driver characteristics (gender, age, driving routine, risk tolerance). We ran a multiple linear regression analysis and found a significant regression equation (F(4, 36) = 4.015, p < .05) with R2 = 0.309. Driver’s mean lateral acceleration is equal to 0.653 0.098 (female gender) 0.005 (age) + 0.044 (driving routine) + 0.041 (age). Predictors were screened negative for multicollinearity (VIF  1) and positive for variance homogeneity). Partial effect size was g2P = 0.11. 4.2.3. Safety distance Age, anxiety as well as driving anger were significantly correlated with time headway to the preceding vehicle. A significant regression equation was found (F(3, 37) = 15.202, p < .001) with R2 = 0.552. Safety distance to preceding vehicle equals 0.557 + 0.004 (age) + 0.002 (anxiety) + 0.004 (driving anger), whereas driving anger was measured as test score on the DAS. Predictors were tested negative for multicollinearity (VIF  1) and positive for variance homogeneity. Partial effect size was g2P = 0.55.

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Fig. 6. Scatter plot with regression trend line for driving routine and velocity.

4.2.4. Speed limit violation Linear regression analysis was run for speed limit violation. As described in the further section, speed limit violation was significantly correlated to several factors (age, driving routine, risk tolerance, thrill and adventure seeking and disinhibition). Age and risk tolerance were significant predictors for 33.3% of the variance in speed limit violation. Thrill and adventure seeking as well as disinhibition were no significant predictors and were therefore removed from regression equation (F (3, 37) = 6.157, p < .05) with R2 = 0.333. Exceeding speed limits equals 6.686 0.269 (age) + 1.464 (driving routine) + 1.757 (risk tolerance). Predictors were tested negative for multicollinearity and positive for variance homogeneity. Partial effect size was g2P = 0.35. 4.2.5. Driving on the right side of the road Scores on the DAS as well as risk tolerance were significant predictors for compliance to the obligation to drive on the right side of the road. A significant regression equation was found (F(2, 38) = 5.735, p < .05) with R2 = 0.232. Percentage of driving on the right lane equals 0.313 0.023 (risk tolerance) 0.003 (driving anger). Predictors were tested negative for multicollinearity (VIF  1) and positive for variance homogeneity. Partial effect size was g2P = 0.23. 4.2.6. Steering wheel angle speed Steering wheel angle speed was significantly correlated to the driver’s age. A significant regression equation was found (F (1, 39) = 11.682, p < .05) with R2 = 0.230. Steering wheel angle speed equals 0.20 + 0.001 (age). Variance homogeneity was tested and was fulfilled. Partial effect size was g2P = 0.20. 4.3. Research question 3: Principal component analysis (PCA) Understanding why drivers with different characters act differently in traffic, e.g. regarding to speeding, traffic rule compliance or driver performance, is a crucial step for varying stochastic driving parameters in traffic simulation more realistic. Several stochastic driver behavior parameters have already been implemented into the SCM, which are not yet systematically attributed with values. To generate smooth driver behavior of the simulated SCM-agents, it is necessary to link parameters with shared variance, such as longitudinal and lateral acceleration or speeding and susceptibility to exceed speed limits. A multi-level model, containing manifest variables, latent variables, driver behavior parameters and presumed principal components, to which driver behavior parameters can be reduced, was set up (see Fig. 3). By means of a PCA, presumed behavior dimensions were tested for common shared variability in the driver behavior parameters. In the following, results of the analysis are reported and the final adapted path model is illustrated (Fig. 8). Initially, factorability of the driver behavior parameters was examined in several single steps. Firstly, it was observed that each parameter correlated at least 0.3 with at least one other parameter, indicating reasonable factorability. Secondly, the Kaiser-Meyer-Olkin measure of sampling adequacy was 0.55, which is slightly below the commonly recommended value of 0.6 but is still acceptable. Bartlett’s test of sphericity was significant (v2 (28) = 159.73, p < .001). The diagonals of the anti-image correlation matrix were all over 0.3, most of them over 0.5. Finally, communalities were all above 0.6, further confirming that each item shared some common variance with other driving parameters. Due to these pre-tests on factorability of the considered parameters, it was assumed that parameters were suitable for a valid factor analysis. For identifying subcategories of driver behavior parameters, a PCA was calculated. Initial eigenvalues show that the first three factors explained 40.39%, 18.00% and 14.29% of the variance respectively. Further factors had eigenvalues below 11%. The three-factor-solution was further examined using varimax rotations of the factor-loading matrix. Thus, 72.67% of the variance was explained and best fit of the threefactor-solution was indicated by (1) the prior drawn path model with three parameter clusters based on theoretical assumptions; (2) the ‘leveling off’ of eigenvalues on the scree plot after three factors (see Fig. 7).

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Fig. 7. Scree plot for principal component analysis.

All parameters had primary loadings over 0.6. Three parameters had cross-loadings (velocity, lateral acceleration, safety distance). Based on prior assumptions, these parameters were assigned to the best fitting component respectively. The rotated factor-loading matrix for this final solution is presented in Table 3. The bold values represent the highest factor loadings of the driving behavior parameters on the respective component. Results from the PCA indicate the necessity to adjust the initial structure of the multi-level model on the third and fourth level as some of the considered driver behavior parameters were loading on different components than was previously assumed. Furthermore, some of the driver personality factors (patience, experience seeking and boredom susceptibility) need to be removed from the initial model, as results from regression analyses have shown that they were no significant predictors for driver behavior (see Fig. 8). Factor loadings indicate a different interpretation of the principal components than

Table 3 Rotated component matrix. Component Velocity Speed limit violation Lane preference/compliance to drive on the right side of the road Longitudinal acceleration Lateral acceleration Safety distance Lane departure Steering wheel angle speed

1 0.724 0.808 0.856 0.0.91 0.538 0.228 0.369 0.236

2 0.624 0.238 0.139 0.751 0.610 0.702 0.158 0.001

Extraction method: Principal Component Analysis. Rotation method: Varimax with Kaiser Normalization.

Fig. 8. Adjusted logical path model of driver characteristics, driver personality and driver behavior parameters.

3 0.036 0.148 0.029 0.181 0.320 0.456 0.735 0.734

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was assumed in hypothesis 4: Law Conformity dropped out of the model and driving parameters for Dynamics were subdivided into Speed and Cruise Control and Dynamics. Factor loadings on the third component, which was declared as Driver Performance, were consistent with the previous assumptions in the initial model. 5. Summary and discussion In a driving simulator study with 43 participants and a total number of 3010 driven km on the highway, significant correlations between driver characteristics (gender, age, driving routine), personality traits (risk tolerance, anxiety, thrill seeking, disinhibition and driving anger) and several driver behavior parameters were found. For the most part, findings were consistent with results from previous research. In detail, correlations between age, gender, sensation seeking and risky driving behavior in regard to driving with higher velocities and neglecting speed limits could be proven (e.g. Arnett et al., 1997; DeJoy, 1992; McKnight & McKnight, 2000; Shinar et al., 2001). Current research indicates that especially young drivers are overrepresented in traffic accidents due to inappropriate speed and have a stronger tendency to risky driving (e.g. DeJoy, 1992; Finn & Bragg, 1986; Laapotti et al., 2001; Shinar et al., 2001). According to reports on causes for accidents with personal injury, two of the main reasons for traffic accidents are driving with inappropriate speed and driving too close (Destatis, 2019). These findings indicate a connection between young age, risky driving and crash rates. In contrast, with increasing driving routine, drivers tended to exceed speed limits greater than inexperienced drivers. The reason for this finding may be, that with increasing routine, drivers learn to read the road and to appropriately estimate risks. For the most part of the route, the road wasn’t very curvy and drivers, who were very experienced may not have seen the necessity to reduce speed. As they were told to drive just as they would, if they were driving in their own car, it is possible that these drivers also usually tend to drive faster than the given speed limits, if they aren’t expecting to be controlled or if they are convinced that it is safe to drive faster. Moreover, higher values in risk tolerance were significantly correlated with driving with higher velocities, stronger longitudinal and lateral acceleration and shorter distances to the preceding vehicle. Also, consistent with previous research, significant correlations between subscales of the sensation seeking scale (disinhibition, thrill and adventure seeking) and driving with higher velocities and a stronger tendency to exceed speed limits were found (e.g. Burns & Wilde, 1995; Horvath & Zuckerman, 1992; Jonah, 1997). By means of multiple linear regression analysis, 11.0–52.2% of variances in driver behavior parameters for speeding, accelerating and driver performance parameters (e.g. steering wheel angle speed) could be explained by specific driver characteristics – most among them explained more than 30% of variance in the considered parameters. These results can be used to generate different driver profiles for virtual traffic simulation and thereby get closer to the representation of the true population in a virtual testing environment. In addition, by means of a principal component analysis, we found three largely independent components to which driver behavior parameters can be reduced. These behavior dimensions can be declared as Speed and Cruise control, Dynamics and Driver Performance. Henceforth, by this reduction of dimensions, driver profiles for virtual SCM-agents can be generated and stochastic driver behavior parameters can be systematically attributed with values. Thereby, in contrast to the current state of the driver model, different agents with different predispositions and behavior preferences in terms of vehicle guidance can be generated. By this, a more realistic representation of the general driver population can be achieved. Despite the difficulty of transferring the findings of the present driving simulator study on driver behavior in real traffic, the standardized laboratory conditions of the experiment are a major advantage for the interpretation of the results. Nevertheless, for further validation of the results, data from naturalistic driving studies are needed. Therefore, a naturalistic driving study is currently conducted to compare the participants’ behavior in the driving simulator with their behavior in traffic on the highway. For ideal conditions and elimination of additional uncontrolled variance in the collected data, the same test persons as in the present study are invited to participate in this follow-up study to maximize internal validity and minimize possible disruptive factors of unequal samples. 6. Practical application of results and further research With this detailed investigation of underlying factors for individual behavioral patterns in traffic, it is possible to enable the SCM to consider driver individuality in modeling driver behavior for traffic simulation. Thereby, the SCM can simulate agents more realistic and stochastic parametrization of driver behavior can be adjusted to the individual condition of the spawned agents. Moreover, findings of the current state of the art could be proven as well as further knowledge about which individual factors contribute to which aspects of driving was generated. In addition, clustering of the driving parameters in superior dimensions has been analyzed for the first time. First and foremost, this gives useful information for the design of the user surface in a traffic simulation environment. In addition, implications for the underlying logic of the connected generation of the variance in the respective parameter distributions are provided. Regarding the SCM, an initial prototype has been implemented by generating multidimensional distribution functions with Kernel Density Estimation (KDE). KDE is a statistical method of estimating the true probability density given some sampled data points from an unknown density. One of the key problems is selecting the smoothing parameter, also called kernel bandwidth, which is crucial for the performance of the estimator. KDE via diffusion is a so-called nonparametric density estimation, where no bandwidth parameter has to be selected nor tuned. The bandwidth selection is replaced by adapting the smoothing properties of a linear diffusion process. In other words, the kernel of the estimator, in conventional methods often a Gaussian kernel, is in this method

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constructed as the transition density of a diffusion process. As a result, this estimator has a reduced asymptotic bias and mean square error with an at the same time increased local adaptability compared to a standard Gaussian distribution curve. In other words, this sophisticated algorithm simply takes the sampled data points from the driving simulator study as input and generates a discrete density estimate, e.g. as an array. Based on this density estimation, new virtual agents can be generated for virtual simulation. Initial virtual agents have already been generated and consistency with original data has been checked. The present paper provides a diversified generated knowledge on factors, which have a significant impact on driver behavior. However, further research in this field can deliver information about additional factors that have not been considered in the current investigation. Impulsiveness and aggressiveness have not been targeted in the present study. Based in previous literature, these factors can also have an impact on driver behavior. Therefore, further studies should integrate these components into their analyses. Moreover, as there may be other factors, which are influenced by individual characteristics, further research must consider differences in perception, e.g. risk perception or perception of other relevant stimuli in the surrounding. Also, as automated driving functions are increasingly integrated into the driving task, and driver behavior will change massively, e.g. focusing on secondary tasks while driving, such as texting or writing emails, distraction from the primary task must be considered as an additional driver state. As a helpful byproduct of the present study, results can be used for the development of automated driving functions ins so far, as that the systems can be adapted to the driver’s skills and preferences, such as desired velocity or tolerated safety distance and intensity of acceleration. References Anderson, J. R., Matessa, M., & Lebiere, C. (1997). ACT-R: A theory of higher level cognition and its relation to visual attention. Human-Computer Interaction, 12(4), 439–462. Arnett, J. J., Offer, D., & Fine, M. A. (1997). Reckless driving in adolescence: ‘State’ and ‘trait’ factors. Accident Analysis and Prevention, 29, 57–63. Arthur, W., Jr, & Graziano, W. G. (1996). The five-factor model, conscientiousness, and driving accident involvement. Journal of Personality, 64(3), 593–618. 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. Beierlein, C., Kovaleva, A., & Kemper, C. (2014). Eine Single-Item-Skala zur Erfassung von Risikobereitschaft: Die Kurzskala Risikobereitschaft-1 (R-1). [A single item scale for the measurement of risk proneness: The short scale Risk Proneness-1 (R-l)]. Köln, Germany: GESIS. Briggs, G. F., Hole, G. J., & Land, M. F. (2011). Emotionally involving telephone conversations lead to driver error and visual tunnelling. Transportation Research Part F: Traffic Psychology and Behaviour, 14(4), 313–323. Burns, P. C., & Wilde, G. J. S. (1995). Risk taking in male taxi drivers: Relationships among personality, observational data and driver records. Personality and Individual Differences, 18, 267–278. Christen, F., & Huang, Q. (2008). The driver model of the traffic flow simulation PELOPS–modelling and application possibilities. In 2nd Berlin Expert Conference on Driver Modelling Driver Modelling in Science and Economy (pp. 1–16). Crundall, D., Chapman, P., Phelps, N., & Underwood, G. (2003). Eye movements and hazard perception in police pursuit and emergency response driving. Journal of Experimental Psychology: Applied, 9(3), 163. Dahlen, E. R., & White, R. P. (2006). The Big Five factors, sensation seeking, and driving anger in the prediction of unsafe driving. Personal and Individual Differences, 41, 903–915. 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 and Prevention, 37, 341–348. Deffenbacher, J. L. (1994). Development of a driving anger scale. Psychological Reports, 74, 83–91. Deffenbacher, J. L., Deffenbacher, D. M., Lynch, R. S., & Richards, T. L. (2003). Anger, aggression, and risky behavior: A comparison of high and low anger drivers. Behavioral Research and Therapy, 41, 701–718. DeJoy, D. M. (1992). An examination of gender differences in traffic accident risk perception. Accident Analysis and Prevention, 24(3), 237–246. Delorme, D. (2001). Modeling driver cognition. In International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design: Colorado. Unfallursachen – Fehlverhalten der Fahrzeugführer bei Unfällen mit Personenschaden. (2019). Retrieved February 27, 2019, from: Statistisches Bundesamt (2019). Verkehrsunfälle. Fachserie 8, 7. Retrieved February 28 from: Eckstein, L., & Zlocki, A. (2013). Safety potential of ADAS – combined methods for an effective evaluation. International technical conference on the Enhanced Safety of Vehicles (ESV). Seoul, South Korea. Finn, P., & Bragg, B. W. (1986). Perception of the risk of an accident by young and older drivers. Accident Analysis & Prevention, 18(4), 289–298. Greene, K., Krcmar, M., Walters, L. H., Rubin, D. L., & Hale, J. L. (2000). Targeting adolescent risk-taking behaviors: The contribution of egocentrism and sensation-seeking. Journal of Adolescence, 23, 439–461. Grimm, J. (2009). State-Trait-Anxiety Inventory nach Spielberger. Deutsche Lang- und Kurzversion. – Methodenforum der Universität Wien. Wien, Germany: MF-Working Paper. Helmer, T., Wang, L., Kompaß, K., & Kates, R. (2015). Safety performance assessment of assisted and automated driving by virtual experiments: Stochastic microscopic traffic simulation as knowledge synthesis. IEEE 18th Intelligent conference on Intelligent Transportation Systems, 2019–2023. Las Palmas de Gran Canaria, Spain. Horvath, P., & Zuckerman, M. (1992). Sensation seeking, risk appraisal and risky behavior. Personality and Individual Differences, 14(1), 41–52. Hoyle, R. H., Stephenson, M. T., Palmgreen, P., Pugzles Lorch, E., & Donohew, R. L. (2002). Reliability and validity of a brief measure of sensation seeking. Personality and Individual Differences, 32, 401–414. Jenkins, C. D. (1976). Recent evidence supporting psychologic and social risk factors for coronary disease. New England Journal of Medicine, 294(19), 1033–1038. Jonah, B. A. (1997). Sensation seeking and risky driving: A review and synthesis of the literature. Accident Analysis and Prevention, 29(5), 651–665. Kompaß, K., Helmer, T., Blaschke, C., & Kates, R. (2014). Ganzheitliche und integrale Fahrzeugsicherheit. ATZ - Automobiltechnische Zeitschrift, 116, 10–15. Kompaß, K., & Huber, W. (2009). Integrale Sicherheit – Effektive Wertsteigerung in der Fahrzeugsicherheit. Technischer Kongress: VDA. Kompaß, K., Helmer, T., Wang, L., & Kates, R (2015). Gesamthafte Bewertung der Sicherheitsveränderung durch FAS/HAF im Verkehrssystem: Der Beitrag von Simulation. Haus der Technik, (137). Renningen, Germany. Laapotti, S., Keskinen, E., Hatakka, M., & Katila, A. (2001). Novice drivers‘ accidents and violations — A failure on higher or lower hierarchical levels of driving behavior. Accident Analysis and Prevention, 33, 759–769. Laux, L., Glanzmann, P., Schaffner, P., & Spielberger, C. D. (1981). Das State-Trait-Angstinventar (Testmappe mit Handanweisung, Fragebogen STAI-G Form X 1 und Fragebogen STAI-G Form X 2). Weinheim: Beltz. Lerner, J. S., Gonzales, R. M., Dahl, R. E., Hariri, A. R., & Taylor, S. E. (2005). Facial expressions of emotion reveal neuroendocrine and cardiovascular stress responses. Biological Psychiatry, 58, 743–750.

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Mai, M. (2017). Fahrerverhaltensmodellierung für die prospektive, stochastische Wirksamkeitsbewertung von Fahrerassistenzsystemen der Aktiven Fahrzeugsicherheit. Göttingen: Cuvillier Verlag. Matthews, G., Dorn, L., Hoyes, T. W., Davies, D. R., Glendon, A. I., & Taylor, R. G. (1998). Driver stress and performance on a driving simulator. Human Factors, 40, 136–149. Matthews, M. L., & Moran, A. R. (1986). Age differences in male drivers’ perception of accident risk: The role of perceived driving ability. Accident Analysis & Prevention, 18(4), 299–313. Maxwell, J. P., Grant, S., & Lipkin, S. (2005). Further validation of the propensity for angry driving scale in British drivers. Personality and Individual Differences, 38, 213–224. McKnight, A. J., & McKnight, A. S. (2000). The behavioral contributors to highway crashes of youthful drivers. 44th Annual conference of the Association for the Advancement of Automotive Medicine. Chicago, IL. Mesken, J., Hagenzieker, M. P., Rothengatter, T., & DeWaard, D. (2007). Frequency, determinants, and consequences of different drivers’ emotions: An onthe-road study using self-reports, (observed) behavior, and physiology. Transportation Research Part F, 10(6), 458–475. Perry, A. R. (1986). Type A behavior pattern and motor vehicle drivers’ behavior. Perceptual and Motor Skills, 63(2), 875–878. Prokop G., Mai, M., Weller, G., & Heyne, F (2014). Fahrerverhaltensmodell zur prospektiven Effektivitätsbewertung der Aktiven Sicherheit. 16. Technischer Kongress des VDA. Hannover, Germany. Roidl, E., Siebert, F. W., Oehl, M., & Höger, R. (2013). Introducing a multivariate model for predicting driving performance: The role of driving anger and personal characteristics. Journal of Safety Research, 47, 47–56. Shinar, D., Schechtman, E., & Compton, R. (2001). Self-reports of safe driving behaviors in relationship to sex, age, education and income in the US adult driving population. Accident Analysis and Prevention, 33(1), 111–116. Spielberger, C. D., Gorsuch, R. L., & Lushene, R. E. (1970). State-Trait Anxiety Inventory, Manual for the State-Trait Anxiety Inventory. Palo Alto, CA: Consulting Psychologist Press. Stephens, A. N., & Groeger, J. A. (2009). Situational specificity of trait influences on drivers’ evaluations and driving behavior. Transportation Research Part F, 12(1), 29–39. Stylidis, K., Hoffenson, S., Wickman, C., Söderman, M., & Söderberg, R. (2014). Corporate and customer understanding of core values regarding perceived quality: Case studies on Volvo Car Group and Volvo Group Truck Technology. Procedia CIRP, 21, 171–176. Sümer, N. (2003). Personality and behavioral predictors of traffic accidents: Testing a contextual mediated model. Accident Analysis and Prevention, 35, 949–964. Taubman-Ben-Ari, O., Mikulincer, M., & Iram, A. (2004). A multi-factorial framework for understanding reckless driving—appraisal indicators and perceived environmental determinants. Transportation Research Part F: Traffic Psychology and Behaviour, 7(6), 333–349. Trimpop, R., & Kirkcaldy, B. (1997). Personality predictors of driving accidents. Personality and Individual Differences, 23(1), 147–152. Underwood, G., Chapman, P., Wright, S., & Crundall, D. (1999). Anger while driving. Transportation Research Part F, 2, 55–68. Underwood, G., Chapman, P., Brocklehurst, N., Underwood, J., & Crundall, D. (2003). Visual attention while driving: Sequences of eye fixations made by experienced and novice drivers. Ergonomics, 46(6), 629–646. Villieux, A., & Delhomme, P. (2007). Driving Anger Scale, French adaptation: Further evidence of reliability and validity. Perceptual and Motor Skills, 104(3), 947–957. Wachenfeld, W. & Winner, H. (2015). Virtual assessment of automation in field operation – A new runtime validation method. In Workshop Fahrerassistenzsysteme. Walting, Germany (pp. 161–170). Wang, L., Fahrenkrog, F., Vogt, T., Jung, O., & Kates, R. (2017). Prospective safety assessment of highly automated driving functions using stochastic traffic simulation. In 25th International Technical Conference on the Enhanced Safety of Vehicles (ESV) National Highway Traffic Safety Administration. Detroit, MI. Witt, M., Wang, L., Prokop, G., & Kompaß, K. (2017). Kognitive Fahrerverhaltensmodellierung in der virtuellen Bewertung von Fahrerassistenzsystemen unter Berücksichtigung der Ausprägungen menschlichen Fahrens. 8. Tagung Fahrerassistenz, München, Germany. Zuckerman, M., Eysenck, S. B., & Eysenck, H. J. (1978). Sensation seeking in England and America: Cross-cultural, age, and sex comparisons. Journal of Consulting and Clinical Psychology, 46(1), 139.