The effects of road, driver, and passenger presence on drivers’ choice of speed: a driving simulator study

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Transportation Research Procedia 25C (2017) 2066–2080 www.elsevier.com/locate/procedia

World Conference on Transport Research - WCTR 2016 Shanghai. 10-15 July 2016 World Conference on Transport Research - WCTR 2016 Shanghai. 10-15 July 2016

The The effects effects of of road, road, driver, driver, and and passenger passenger presence presence on on drivers’ drivers’ choice of speed: a driving simulator study choice of speed: a driving simulator study a a

Anne Goralzik a,a,*, Mark Vollrath aa Anne Goralzik *, Mark Vollrath

TU Braunschweig, Department of Engineering and Traffic Psychology, Gaussstr. 23, D-38106 Braunschweig, Germany TU Braunschweig, Department of Engineering and Traffic Psychology, Gaussstr. 23, D-38106 Braunschweig, Germany

Abstract Abstract Vehicle speed is a key component in road traffic safety. Extending the notion that road traffic is a complex social system, an Vehicle speeddriving is a keysimulator component in road safety.to Extending notion thatand roadjoint traffic is a complex system,road an experimental study was traffic conducted investigatethethe unique effects of speedsocial limitation, experimental drivingpresence, simulatorand study was conducted to the of unique of speed limitation, road geometry, passenger driver characteristics on investigate drivers’ choice speed and in anjoint urbaneffects environment. These factors were geometry, and driver characteristics drivers’ choice of speed in an urban environment. factorsspeed were only foundpassenger to affect presence, drivers’ choice of speed under a on 50-km/h speed limit but not under a 30-km/h limit.These Moreover, only found to affect drivers’ choice of speed under a 50-km/h speed limit but not under a 30-km/h limit. Moreover, speed compliance was conditional on the level of the speed limit. Implications for road traffic safety are addressed. compliance was conditional on the level of the speed limit. Implications for road traffic safety are addressed. © 2017 2017 The The Authors. Published Published by by Elsevier Elsevier B.V. B.V. © © 2017 The Authors. Authors. Published by Elsevier B.V. Peer-review under responsibility of WORLD CONFERENCE ON ON TRANSPORT TRANSPORT RESEARCH RESEARCH SOCIETY. SOCIETY. Peer-review under responsibility of WORLD CONFERENCE Peer-review under responsibility of WORLD CONFERENCE ON TRANSPORT RESEARCH SOCIETY. Keywords: Speed choice; road traffic safety; speed limit; curvature; lane width; gender; driving experience; passenger presence; driving Keywords: simulation Speed choice; road traffic safety; speed limit; curvature; lane width; gender; driving experience; passenger presence; driving simulation

1. Introduction 1. Introduction Vehicle speed is a key factor in road traffic safety. Not only does the risk of road traffic accidents rise more than Vehicle speed is aankey factor in traffic safety. only does the risk of road and traffic moreElvik, than proportionally with increase in road speed, so does theNot severity of accidents (Aarts vanaccidents Schagen,rise 2006; proportionally with an increase in speed, so does the severity of accidents (Aarts and van Schagen, 2006; Elvik, 2013; Hauer, 2009). Even though the absolute number of fatalities has been found to be higher on rural roads than 2013; Hauer, 2009). Even 2005; thoughOECD/ECMT the absolute number of fatalities been 2006; found Statistisches to be higher on rural roads than on urban roads (Burgess, Transport Researchhas Centre, Bundesamt, 2016), on urban roads (Burgess, 2005; OECD/ECMT Transport Research Centre, 2006; Statistisches Bundesamt, 2016), German accident statistics show that the number of injury accidents on urban roads exceed that on rural roads by a German accident statistics show that the number of injury accidents on urban roads exceed that on rural roads by a

* Corresponding author. Tel.: +49-531-391-3655; fax: +49-531-391-8181. * Corresponding author. Tel.: +49-531-391-3655; fax: +49-531-391-8181. E-mail addresses: [email protected] (A. Goralzik), [email protected] (M. Vollrath) E-mail addresses: [email protected] (A. Goralzik), [email protected] (M. Vollrath) 2214-241X © 2017 The Authors. Published by Elsevier B.V. 2214-241X 2017responsibility The Authors.of Published Elsevier B.V. ON TRANSPORT RESEARCH SOCIETY. Peer-review©under WORLDbyCONFERENCE Peer-review under responsibility of WORLD CONFERENCE ON TRANSPORT RESEARCH SOCIETY.

2352-1465 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of WORLD CONFERENCE ON TRANSPORT RESEARCH SOCIETY. 10.1016/j.trpro.2017.05.400

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factor of 2.8 (Statistisches Bundesamt, 2016). The rate of injury accidents on urban roads is also more sensitive to changes in speed. That is, when vehicle speed increases relative to average traffic speed, the injury accident rate rises more quickly on urban roads than on rural roads (Kloeden et al., 2002). Additionally, larger speed variances between vehicles resulting from different speeds chosen by individual drivers are related to increased accident likelihood (Committee for Guidance on Setting and Enforcing Speed Limits, 1998). This finding is particularly relevant to urban traffic environments where, compared to other road types, traffic volume is generally higher, vehicle-to-vehicle distances are smaller and encounters between vehicles due to a higher intersection density are more frequent (Committee for Guidance on Setting and Enforcing Speed Limits, 1998). The choice of driving speed by individual drivers is therefore central to road traffic safety. The question is to what extent speed choice is influenced by speed regulations and the road layout and to what extent by drivers’ immediate social environment and characteristics innate to the driver. Prior research has shown that drivers’ speed depends on a multitude of factors. One line of research has concentrated on isolating the effects of changes in the road infrastructure (e.g. Bassani et al., 2014; Mackie et al., 2014) while another has sought to explain driver behavior by investigating the relationship between driver characteristics, attitudes, and driving behavior (e.g. Ahie et al., 2015; Rudin-Brown et al., 2014). Both perspectives are still relatively independent of each other. Drivers, however, are exposed to a complex driving environment in which they are not only confronted with a certain set of road characteristics but often interact with a passenger inside the vehicle, too (Fleiter et al., 2010). Additionally, drivers differ regarding gender, age, driving history, and personality dispositions which may affect their choice of speed. Consequently, road characteristics as well as social and driver variables can be expected to interact so that drivers’ choice of speed most likely cannot be traced back to a change in a single parameter. Thus, in the present study we aimed to combine both lines of research by investigating the unique and joint effects of road characteristics, the presence of a passenger, and driver characteristics on individual drivers’ choice of speed on urban roads. As representations of the road infrastructure we examined the effect of speed limitation, roadway curvature, and lane width on speed as each of these are well-established factors associated with observed speed. There are consistent findings that operating speed is correlated with the degree of roadway curvature (Kanellaidis, 1995; Kanellaidis and Dimitropoulos, 1998) and lane width where narrower lanes are linked to lower speed (Fitzpatrick et al., 2001; Montella et al., 2011; Rüger et al., 2014). The presence of speed limits has been found to explain the most variance in operating speeds (Fitzpatrick et al., 2001). However, research has also shown that speed limit changes affect observed speed less than implied by the difference between the old and new speed limit (Islam et al., 2014; Silvano and Bang, 2016), highlighting the need to investigate speed compliance following a speed limit reduction. In this study, we also aimed at isolating the effect of passenger presence on drivers’ speed choice. Passenger presence representing drivers’ social environment has produced mixed results. Many studies are epidemiological in nature and have focused on the link between passenger presence and accident risk. Despite evidence of an overall decrease in accident risk when driving with a passenger (Engström et al., 2008; Lee and Abdel-Aty, 2008; Vollrath et al., 2002), findings are inconclusive when characteristics of the driver such as age or gender are taken into account. In some studies, the protective effect of the passenger was reduced for young drivers but still remained (Engström et al., 2008; Vollrath et al., 2002). Other studies, in contrast, point to an increased accident risk of young, in particular male drivers in the company of a passenger (Lee and Abdel-Aty, 2008; Regan and Mitsopoulos, 2001). To what extent drivers’ choice of speed as a relevant factor of accident risk depends on passenger presence remains unclear from these studies. Because of their post hoc nature, epidemiological studies are also not suitable to pin down the actual effect of passenger presence. Reiß (1998) showed an adverse effect of passengers on accident risk when communicating with the driver but not when being silent. This finding points to the necessity of distinguishing between the effects of mere passenger presence on speed and (the kind of) communication between the driver and the passenger. A considerable body of research on the relationship between driver characteristics and the risk of accidents has shown the importance of gender and driving experience. Male gender accounts for a much greater number of blameworthy speed-related accidents (Clarke et al., 2007) and traffic rule violations (Roman et al., 2015; Simon and Corbett, 1996) than female gender. In the same vein, young novice drivers have been shown to commit more driving

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errors and traffic offenses than experienced drivers (Roman et al., 2015). It is therefore interesting to examine whether behavioral differences between novice and experienced drivers as well between male and female drivers are reflected in their choice of speed. Because novice drivers are usually younger with a lower mileage and experienced drivers tend to be older, having had the opportunity to accumulate more kilometers on the road over a longer period of time, we chose to investigate these homogeneous groups as they are usually found in the population. Both studies focusing on the effects of road design and studies centered on driver characteristics to explain vehicle speed usually are associated with a set of methodological limitations. Investigations on the effects of road design typically involve taking point measurements of speed at defined roadside locations and correlating those speed observations with the characteristics of the road (e.g. Bassani et al., 2014). Correlative findings, however, do not permit causal inferences because the influence of third variables not investigated cannot be ruled out. Moreover, such cross-sectional studies do not take into account how different driver characteristics may contribute to different speeds observed. Studies seeking to understand and predict driver behavior, in contrast, often rely on drivers’ selfreports (e.g. see Horvath et al., 2012; Quimby et al., 1999) that may be subject to bias. Thus, it remains unclear to what extent self-reported behavior, intentions, or beliefs are predictive of actual driving behavior. The work by Ahie et al. (2015), for instance, suggests that observed and self-reported speeds do not entirely match. Also, drivers’ evaluations of their own driving behavior and observers’ evaluations have been found to correlate only at a low to moderate level (Amado et al., 2014). In order to close this knowledge gap, we devised an experimental study in the driving simulator in which we systematically varied the factors of interest while tightly controlling the influence of extraneous variables. This way, we minimized the possibility of other variables affecting drivers’ choice of speed and allowing for a causal interpretation of the effects of the factors investigated. Recording actual driver behavior allowed us to establish a direct relationship between our factors of interest and drivers’ speed. Overall, our research aimed to answer the following questions: Do drivers adapt their speed to changes in road characteristics and the presence of a passenger? If so, how can these speed adaptations be characterized? Can driver groups represented by differences in gender and driving experience be distinguished based on their choice of speed? 2. Method 2.1. Participants An initial sample of 63 participants was recruited via online and offline advertisements, as well as the participant data base of TU Braunschweig. Due to driving simulator sickness or technical failure, the data sets of 14 participants were excluded from further analysis, leaving a final sample of 49 participants. Participants received 15 Euros or could choose between 15 Euros and course credit if they were undergraduate psychology students at TU Braunschweig in recognition of their participation in the experiment. Selective sampling was used to create four homogenous subgroups based on age, gender, and driving experience. Participants were assigned to the “Novice” group if they were between 18 and 21 years old, had held their driver’s license for no longer than three years, and had not exceeded a total mileage of 10,000 km. Alternatively, to be assigned to the “Expert” group, participants were required to be 25-60 years old, to have held their driver’s license for at least five years, and to have driven at least 15,000 km in each of the previous three years. Table 1 displays the characteristics of the final sample. Table 1. Final sample characteristics. Subsample

n

Female novices

12

19.1

0.7 18-20

Male novices

12

19.4

Female experts

12

Male experts

13

39.1 12.1 26-59

Age M

SD

Years licenced Range

M

SD

1.7

0.5

0.8 18-20

1.7

37.4 10.2 25-57

18.4 20.8

Mileage (km) M



SD

5,333

3,284

0.6

4,500

2,620

9.9

20,611

5,626

12.1

39,641

39,812

4

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2.2. Experimental design A five-factor mixed design was used to investigate the unique and joint of effects of speed limitation, roadway curvature, lane width, and the presence of a passenger on speed (see Table 2). Each of these factors was varied at two levels and presented as within-subjects variable. A further between-subjects factor “Order of passenger presence” was introduced to control for order effects and therefore not of primary interest (see this section below). The speed limit represented by signposts was fixed at 50 km/h and 30 km/h, being the predominant speed limitations on German urban roads. Roadway curvature was varied by presenting the road as either straight or curved. Curved sections included only a right-hand bend to avoid confounding effects on speed by different curve directions and consisted of a straight section (60 m) at the beginning, followed by a total of 183 m of curved sections, and then a straight section (57 m) again at the end. The radii of the curved sections ranged between 108 m and 156 m, with the strongest inflection in the middle of the curve. The lane was either wide (3.45 m) or narrow (2.75 m), the specifications of which conformed to the German guidelines for urban road design (cf. Forschungsgesellschaft für Straßen- und Verkehrswesen e.V., 2007). Passenger presence as the final within-subjects factor was varied by participants driving alone or with a passenger present on the passenger seat. One of three female confederates aged between 20 and 32 years and not known to the participants acted as the passenger. In order to distinguish between the effect of passenger presence and the effect due to the interaction between driver and passenger, the passenger was instructed not to initiate conversation with the driver and to respond to questions or remarks briefly without inviting further conversation. To increase the participants’ impression of being observed, the passenger carried a clipboard and pretended to take notes on the participants’ driving behavior. The combination of the four within-subjects factors resulted in 16 scenarios presented to each participant. Half of these were experienced with a passenger and half were experienced alone in two separate drives. To counterbalance the order by which the passenger was introduced (“Order of passenger presence”) across participants, participants in each of the four subsamples were randomly assigned either to the passenger-first or passenger-second condition. The 16 scenarios were part of a total of 48 scenarios presented to each participant in two drives with 24 scenarios per drive. Varying other motorized traffic at five levels, the additional 32 scenarios were irrelevant to the research questions at hand and are therefore not presented in this paper. Several steps were taken to ensure counterbalancing of the scenario sequences. No scenario was presented twice within one drive and no participant was presented with the same sequence of scenarios twice. Scenario sequences were designed such that two straight sections and two curved sections alternated. Construction of a Latin square (Bradley, 1958) yielded 24 different scenario sequences which were intended to be used once in each of the four subsamples so as to evenly distribute the scenario sequences across subsamples. Table 2. Experimental design.

(WS) Roadway curvature Straight

Curved

(WS) Lane width

(WS) Lane width

Wide

Narrow

Wide

Narrow

(WS) Speed limit

(WS) Speed limit

(WS) Speed limit

(WS) Speed limit



50

30

Yes (BS) Order of passenger presence

No

Yes



50

(WS) Pass. (WS) Pass. No

30

Yes

No

Yes



50

(WS) Pass. (WS) Pass. No

30

Yes

No

Yes



50

(WS) Pass. (WS) Pass. No

30

(WS) Pass. (WS) Pass. Yes

No

Yes

No

Passenger in 1st drive











Passenger in 2nd drive











Note. WS = within-subjects factor, BS = between-subjects factor. Pass. = passenger presence.

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This, however, failed due to an error in the script rendering the scenario sequences. Effectively, 16 scenarios sequences were realized on completion of the data collection period, with participants who came from the same subsample and who were assigned to the same passenger order receiving identical scenario sequences. Eight of these sequences were unique and four sequences occurred twice across subsamples. Potential position effects resulting from a failure to counterbalance the scenario sequences are addressed in section 3.1. 2.3. Measures Driving behavior was recorded at a 100 Hz sampling rate. The arithmetic mean of speed within each of the 16 scenarios examined was used as dependent variable. The grouping variables gender and driving experience (as a compound of age, number of years licensed, and annual or total mileage) defining the four subsamples were recorded using a questionnaire. Further self-report measures the results of which are not reported in this paper pertained to participants’ usual driving style, commuting to the workplace, history of blameworthy traffic accidents, difficulty/ease of driving in the driving simulator, as well as the behavioral dispositions Sensation Seeking and driving anger that have been discussed as relevant to driving (e.g. Greaves and Ellison, 2011; Schwebel et al., 2006; Roidl et al., 2013). 2.4. Apparatus The experiment was conducted in the fixed-base driving simulator of the Department of Engineering and Traffic Psychology at TU Braunschweig which consists of a mock-up vehicle cockpit with a regular steering wheel and two adjustable car seats positioned in front of three screens. The screens measured approximately 2 × 2 m each on which the virtual driving environment was projected at a resolution of 1920 × 1080 pixels each. The center screen was positioned approximately 2 m away from the driver seat (the precise distance varied slightly depending on how the participants adjusted the driver’s seat) and connected to the outer screens such as to yield an approximate 180° field of view from the driver’s position. The rear-view mirror was projected onto the front screen at a resolution of 300 × 150 pixels with the top left corner of the rear view-mirror positioned at 1100 × 250 pixels. Three 7“ color LCD screens (resolution of 1280 × 768 pixels each) were mounted on a dummy dashboard at the typical position of the tachometer and the side mirrors and displayed instantaneous speed and the view to the rear, respectively. In addition to the gas and brake pedal, a gear shift and clutch pedal were part of the default setup. However, the latter were not needed because an automatic transmission based on the vehicle dynamics of a BMW 5 model was simulated in this experiment. Wind and engine sounds were simulated with a 5.1 sound system. Custom simulation software (SILAB 4.0, WIVW GmbH, https://wivw.de/en/silab) was used to render the driving environment and record vehicle parameters such as speed, acceleration, and lane position. 2.5. Driving scenarios The driving scenarios were embedded in an urban environment featuring a single carriageway with one lane for each direction of travel (for an example see Fig. 1a). Two- to four-story houses adjoining pavements with trees and lampposts were placed on either side of the road to resemble the center of a mid-size town. The road geometry and road signs conformed to the German guidelines for urban road design (cf. Forschungsgesellschaft für Straßen- und Verkehrswesen e.V., 2007) and the German road regulations (cf. Bundesministerium der Justiz und für Verbraucherschutz, 2013), respectively. Y-junctions were placed in the simulation to allow for in- and outflow of other traffic. Priority signs at these points ensured that the driver always had the right of way. Whenever a lead vehicle was introduced, its appearance was timed such that the driver could continue driving at the same speed as before. Occasional oncoming traffic (8 vehicles per scenario) at variable time intervals served to ensure that the driver stayed within the limits of the lane. Oncoming traffic never crossed the driver’s path. Vehicles travelling in the driver’s direction were introduced according to the experimental design.

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Fig. 1. Driving scenarios with a) the driving environment from the driver’s perspective and b) the overall scenario structure.

All driving scenarios were identical in overall structure (see Fig. 1b). Each of the experimental variations (B) was preceded and followed by filler sections (A and C) which served to create a logical transition between the experimental variations as well as to introduce and remove the lead or following vehicle. The beginning of the filler section A and the ending of the filler section C were identical to allow for an independent combination of driving scenarios irrespective of the experimental variation. Between experimental variations, filler section C was immediately followed by filler section A so that transitions between driving scenarios were not obvious to the driver. The filler sections had a speed limit of 40 km/h which resulted in participants either decelerating or accelerating depending on the speed limit in the experimental variation. The lane in the filler sections was 3.1 m wide as a midpoint between the lane widths in the experimental variations. Filler section A comprised a slight lefthand bend whereas filler section C comprised a sharper right-hand bend. However, the curvature in the filler sections was smaller than in the experimental variations. The driving environment within the experimental variations was identical to that in the filler sections A and C apart from the experimental variations realized. As an exception, the experimental variations also featured pedestrians moving along the pavement on either side of the road. At no point did pedestrians cross the road. One complete set of driving scenarios took approximately 25 minutes to complete. 2.6. Procedure The data were collected between April and June 2015. At the beginning of each 1.5-h experimental session, participants were informed that their driving behavior would be recorded to investigate typical urban driving behavior and were asked to sign a consent form, as well as to complete a demographic questionnaire. Participants then completed a 10-minute training course in the driving simulator to familiarize themselves with the simulated vehicle dynamics and driving environment. Next, participants were presented with the 16 experimental scenarios as part of a total of 48 experimental scenarios in two separate test drives of equal length, lasting about 25 minutes each. Half of the 16 scenarios were completed in the first drive either with the passenger or alone and the other half of the 16 scenarios was completed in the second drive where passenger presence depended on whether participants were accompanied by a passenger in the first test drive or not. The two drives were separated by a break during which the passenger either joined or left. Prior to each drive, participants received a written instruction on the driving task to follow. In the driving-alone condition, they were asked to follow the main road and informed that the (German) traffic regulations were in effect, though without explicitly asking for traffic rule compliance. Specifically, they were instructed to drive as they would usually do and choose the speed at which to drive. In the passenger conditions, the instruction was the same but incorporated two changes: participants were informed that a passenger would be present who would watch their driving behavior. Additionally, the instruction to drive as usual was dropped to avoid masking any effect of the passenger’s presence. The passenger was only present for the length of one drive and silently observed the participants, pretending to record their driving behavior. Upon completion of the test drives, participants filled out three questionnaires, one of

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which tailored to this study, on the self-report measures mentioned in section 2.3. Participants were then fully debriefed as to the purpose of the study and compensated for their participation. 3. Results 3.1. Data analysis Each of the 16 experimental variations was split into three 100-m sections in order to examine whether the road geometry and speed limit of the filler sections affected speed at the respective beginnings and endings of the experimental variations. Because statistical analysis showed that mean speed differed significantly across the three sections, only the middle 100 m section of each experimental variation was subjected to further analysis. In order to rule out effects on speed solely due to the order of passenger presence between drivers rather than the experimental factors of interest, an independent-samples t-test was conducted which established the equivalence of mean speed regardless of whether participants were accompanied by the passenger in the first or second test drive (t40.23 = 0.39, p = .70). The factor passenger order was therefore dropped from all further analyses. The failure to completely counterbalance the order of the driving scenarios within the four subsamples and across the two drives was addressed by visually inspecting the distributions of the eight relevant driving scenarios within and across the sequences of the total 24 driving scenarios composing a test drive. Separate examination by passenger order revealed that these eight scenarios were generally clustered together either at the beginning, in the middle, or at the end of a test drive. Within each subsample, however, these scenarios clusters were evenly distributed across the scenario sequences. With scenario position effects corrupting the interpretation of the effects of the experimental variations on speed being thus unlikely, no adjustment to the subsequent analyses was made. All statistical tests are reported at a 5% significance level. Detailed statistical results not reported due to lack of space may be requested from the corresponding author. 3.2. Effects of infrastructure In a first step, a 2 (speed limit) × 2 (roadway curvature) × 2 (lane width) within-subjects analysis of variance (ANOVA) was conducted to assess the unique and joint effects of infrastructural changes on mean speed irrespective of the influence of passenger presence or the driver variables gender and driving experience. Table 3 shows the results of the ANOVA for those scenarios in which participants drove alone. The effect of curvature on speed was generally unaffected by changes in lane width which was the case for both speed limit levels. Table 3. Within-subjects effects of road characteristics (speed limit, curvature, and lane width) on mean speed in the driving-alone condition (N = 49). Effect



Speed limit

460.59

.00

.91

Curvature



10.40

.00

.18

Lane width



7.22

.01

.13

Speed limit × curvature



18.23

.00

.28

Speed limit × lane width



9.04

.00

.16

Longitudinal design × lane width



0.99

.33

.02

Speed limit × curvature × lane width



1.17

.29

.02

F1, 48



p

Note. Results significant at the .05 significance level are highlighted in bold.

𝜂𝜂"#

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Anne Goralzik et al. / Transportation Research Procedia 25C (2017) 2066–2080 Author name / Transportation Research Procedia 00 (2017) 000–000

a)

Wide 36 34 32 30 28

Straight

Curved

50 km/h

Wide

Narrow

Mean speed [km/h]

Mean speed [km/h]

b)

30 km/h

2073

Narrow

52

50 48 46 44

Straight

Curved

Fig. 2. Mean speed under a) the 30-km/h speed limit and b) the 50-km/h speed limit, split by roadway curvature (straight, curved) and lane width (wide, narrow) in the driving-alone condition. Dashed horizontal lines represent the respective speed limit.

However, there were two significant interactions between speed limit and curvature as well as between speed limit and lane width. Post hoc simple effects tests revealed that changes in roadway curvature and lane width only had an effect on speed when the speed limit was 50 km/h. More specifically, participants adopted a higher speed in straight sections (M = 48.8 km/h) than in curved sections (M = 47.0 km/h, p = .00). Mean speed was also higher when the lane was wide (M = 48.8 km/h) than when it was narrow (M = 47.0 km/h, p = .00). Under the 30-km/h speed limit, both changes in curvature (Mstraight = 34.0 km/h, Mcurved = 34.1 km/h, p = .79) and lane width (Mwide = 34.1 km/h, Mnarrow = 34.0 km/h, p = .76) were irrelevant to the participants’ choice of speed. Fig. 2 illustrates this pattern, demonstrating not only the dependency of the effects of roadway curvature and lane width on the level of the speed limit but also a differential effect of the speed limit on speed limit compliance. Mean speed under the 30km/h limit was unaffected by changes in the road geometry and consistently above the speed limit by approximately 4 km/h. In contrast, mean speed under the 50-km/h limit was consistently lower than the limit and reflected the complexity of the scenario with mean speed being the highest in the straight/wide section and lowest in the curved/narrow section.

Fig. 3. Mean speed distribution across drivers (N = 49) in the driving-alone condition split by speed limit. The shaded boxes represent 50 % of all values or 1 IQR, solid horizontal lines in the boxes represent the median, whiskers extending from the top and bottom of the boxes each display values at a maximum of 1.5 × IQR, the individual point reflects an extreme case. The dashed horizontal lines represent the respective speed limit.

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Table 4. Overall effects of passenger presence, gender, and driving experience on mean speed under each speed limit N = 49). Effect

V

F4, 42

p

𝜂𝜂"#

30 km/h







Between-subjects





- Gender



.11

1.34

.27

.11



- Driving experience



.06

0.68

.61

.06



- Gender × driving experience



.09

0.97

.43

.09



Within-subjects





- Passenger presence



.16

2.06

.10

.16



- Passenger presence × gender



.05

0.60

.66

.05



- Passenger presence × driving experience



.07

0.80

.53

.07



- Passenger presence × gender × driving experience



.06

0.62

.65

.06

50 km/h





- Gender

.09

1.03

.41

.09

- Driving experience

.10

1.19

.33

.10

- Gender × driving experience

.26

3.69

.01

.26

- Passenger presence

.22

2.88

.03

.22

- Passenger presence × gender

.14

1.68

.17

.14

- Passenger presence × driving experience

.09

0.99

.42

.09

- Passenger presence × gender × driving experience

.08

0.88

.48

.08

Between-subjects

Within-subjects

Note. Results significant at the .05 significance level are highlighted in bold. V = Pillai-Bartlett trace.

The main effect of speed limit held independently of the effects of curvature and lane width. Fig. 3 displays the distribution of the mean speeds aggregated across the factors roadway curvature and lane width for both speed limits, underscoring the significant main effect of speed limit. Despite large variations in individual mean speeds between participants, Fig. 3 suggests that while a majority of the participants exceeded the 30-km/h speed limit, only a minority of them exceeded the speed limit of 50 km/h. A moderate correlation of mean speeds under the 30-km/h speed limit and the 50-km/h speed limit (r = .44, p = .00) indicated that participants who adopted higher speeds under the 30-km/h speed limit tended to adopt a higher speed under the 50-km/h speed limit, too. 3.3. Combined effects of infrastructure, driver variables, and passenger presence In a second step, two separate 2 × 2 × 2 mixed design multivariate ANOVAs (MANOVAs), one for the 30-km/h speed limit and one for the 50-km/h limit, were run to analyze additional effects of passenger presence, gender, and driving experience on mean speed. Passenger presence was the within-subjects factor and gender and driving experience were between-subjects factors. The four scenario combinations “straight/wide”, “straight/narrow”, “curved/wide”, and “curved/narrow” resulting from the variations of roadway curvature and lane width served as different dependent variables in the analyses. Splitting mean speed into four separate dependent variables not only allowed to assess the global effects of passenger presence, gender, and driving experience on mean speed but also provided a more detailed picture of how these variables affected mean speed in each scenario combination.

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Mean speed based on gender and driving experience Novice

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Fig. 4. Mean speed for each combination of roadway curvature and lane width in scenarios with a 50-km/h speed limit, split by a) gender and driving experience (M = male, F = female), and b) passenger presence (A = alone, P = passenger). Dashed horizontal lines represent the speed limit.

The Pillai-Bartlett trace was chosen as multivariate test statistic because of its robustness to violations of statistical assumptions when sample sizes are sufficiently equal (Bray and Maxwell, 1985). The results of both MANOVAs are presented in Table 4. Under the 30-km/h speed limit, neither gender nor driving experience nor passenger presence had an effect on mean speed. Under the 50-km/h speed limit, there was a significant interaction effect between gender and driving experience on overall mean speed. Individually, however, neither of these factors had an effect. Univariate ANOVAs to follow up the gender × driving experience interaction showed that this 2 interaction held when the road was straight and wide (F1, 45 = 7.33, p = .01, h p = .14) but not in the other scenarios (also see Fig. 4a). Simple effects analyses revealed that in the straight/wide scenarios, male novices adopted a higher mean speed than male experts (ΔM = 4.4 km/h, p = .00). Although the univariate ANOVAs generally indicated no significant interaction effect between gender and driving experience for the straight/narrow sections, curved/wide sections, and curved/narrow sections, post hoc testing showed that male novices tended to drive faster than male experts in the straight/narrow section, too (ΔM = 3.1 km/h, p = .09). On a descriptive level, Fig. 4a displays a general tendency of male novices to drive faster than male experts in all scenarios, with group differences most pronounced in the less complex straight scenarios. For female participants, there was no effect of driving experience. In the straight/wide sections, male novices also drove faster than their female counterparts (ΔM = 3.4 km/h, p = .02). On the contrary, gender did not moderate the mean speed chosen in the “Expert” group. Participants, on average, adhered to the 50-km/h speed limit in all scenarios with exception of the male “Novice” group who exceeded the speed limit in the straight/wide configuration. The MANOVA on the 50-km/h limit scenarios also showed a significant global main effect of passenger presence on mean speed (see Table 4). Univariate ANOVAs following up the global effect of passenger presence, however, revealed that the passenger effect was specific to the scenario investigated. While mean speed in the 2 straight/wide sections (ΔM = 0.1 km/h, F1, 45 = 0.07, p = .79, h p = .00) and the curved/wide sections (ΔM = -0.7 2 km/h, F1, 45 = 0.91, p = .35, h p = .02) was unaffected by passenger presence, participants drove significantly faster 2 with the passenger in the straight/narrow sections than without (ΔM = 1.2 km/h, F1, 45 = 4.45, p = .04, h p = 09). Conversely, there was a trend for participants to drive slower in the curved/narrow sections with the passenger 2 compared to when they drove alone (ΔM = -1.0 km/h, F1, 45 = 3.22 p = .08, h p = .07). As Fig. 4b shows, when the straight road narrowed, drivers failed to adapt their speed in the presence of the passenger. In contrast, in a narrow curve, driving with a passenger caused participants to adapt their speed more strongly than driving alone. The overall effect of the road geometry, demonstrated by a decrease in speed with increasing scenario complexity, is illustrated in Fig. 4 regardless of passenger presence, gender, or driving experience with mean speed being the highest in the straight/wide sections and lowest in the curved/narrow sections.

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3.4. Summary of results Changes in road geometry showed to have an effect on mean speed that was conditional on the level of the speed limit. Whereas participants, on average, reduced their speed when the roadway became narrow or curved under the 50-km/h speed limit, participants did not respond to changes in the road geometry by adapting their speed in the 30km/h scenarios. In fact, mean speed in the scenarios with a 30-km/h limit was consistently above the speed limit while it was in compliance with and somewhat below the speed limit in the 50-km/h scenarios, pointing to a differential effect of driving scenario complexity at different speed limits. The overall effect of the 30-km/h vs. 50km/h speed limits on mean speed was, however, independent of changes in roadway curvature and lane width. Mean speed under the 30-km/h limit remained unaffected by the presence of a passenger or when separately analyzed by gender and driving experience. However, under the 50-km/h speed limit passenger presence, gender, and driving experience did have a scenario-specific effect in addition to the general decrease in mean speed with increasing scenario complexity from straight/wide to curved/narrow. In the straight/wide configuration, the effect of driving experience was stronger for male participants than for female participants, with male novices adopting a higher mean speed than male experts. Male novices tended to drive fastest and male experts slowest in all scenarios, with group differences most pronounced in the less complex straight scenarios. Except for the straight/wide scenario, female participants, regardless of driving experience, chose a speed that was similar to that of male novices. The effect of passenger presence was the same for male and female participants as well as novice and expert drivers. Driving with a passenger compared to driving alone only had an effect in narrow sections that depended on roadway curvature: the passenger led to faster driving when the road was straight but caused participants to drive slower in curved road sections. 4. Discussion The purpose of this study was to investigate the unique and joint effects of changes in speed limitation and road geometry, passenger presence, and driver characteristics on drivers’ choice of speed in an urban driving environment. In particular, we were interested in how drivers adapt their speed to different driving situations and whether speed adaptation differs depending on gender and driving experience. To this end, we conducted an experimental study in the driving simulator. The most noticeable finding was the drivers’ differential response to each speed limit. Even though driving speed was generally lowered in road sections with a 30-km/h speed limit compared to sections with a 50-km/h speed limit, the extent to which drivers complied with the speed limit depended on the level of the speed limit. Drivers’ excessive speed in scenarios with a 30-km/h speed limit is consistent with previous research in which observed speeds averaged at around 40 km/h on urban residential streets with a 30-km/h speed limit (Dinh and Kubota, 2013b) and the majority of drivers reported to speed frequently on such streets (Dinh and Kubota, 2013a). In road sections with a 50-km/h speed limit, in contrast, drivers tended to adopt a speed below the limit. The differential effect of speed limits has been observed before. According to a survey by Anastasopoulos and Mannering (2016) drivers exceed the speed limit to a relatively greater degree under a lower speed limit and to a relatively smaller degree under a higher speed limit. In the same vein, relatively more drivers report to drive above the speed limit when the speed limit is lower rather than higher (OECD/ECMT Transport Research Centre, 2006). Risk allostasis theory (Fuller, 2011) maintains that driver actions are guided by the perceived difficulty of a driving situation which drivers aim to keep within an acceptable range by balancing their own capabilities and driving task demands. Differential speed limit compliance in our study may be interpreted from this perspective. In an otherwise identical driving environment, a 30-km/h speed limit creates a lower task demand (or less complex situation) than a 50-km/h speed limit by granting drivers more time to react and correct potential driving errors. As a result, driver capability may have exceeded task demand in this situation, leading drivers to compensate for this imbalance by increasing their speed beyond the speed limit to maintain a subjectively acceptable level of task difficulty. Interpreting the differential response to the speed limit as a result of varying scenario complexity is supported by the differential effects of road geometry on speed. Changes in roadway curvature and lane width affected

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participants’ choice of speed only under the 50-km/h speed limit, leading to lower speeds in curves and narrow sections. Under the 30-km/h speed limit, changes in the road geometry had no effect on speed, indicating that irrespective of road geometry all scenarios were comfortable to navigate at the same speed. This finding suggests that drivers lacked a plausible reason to comply with the 30-km/h speed limit and therefore chose a speed according to what they deemed appropriate in the given situation. Indeed, previous research implies the same effect. Drivers’ preferred speed was found to exceed the speed limit under favorable conditions such as when other traffic is absent (Dinh and Kubota, 2013a; Goldenbeld and van Schagen, 2007) or on straight and wide roads (Goldenbeld and van Schagen, 2007). Similarly, the finding that observed speed remained well above the speed limit after reducing the speed limit but leaving the characteristics of the road unchanged (cf. Islam et al., 2014; Silvano and Bang, 2016) suggests that in order to facilitate speed limit compliance, drivers require plausible reasons justifying the need to slow down. Compliance with the speed limit is particularly important on roads with a lower designated speed limit as they are usually located in urban residential areas where encounters with vulnerable road users such as pedestrians and cyclists are more frequent and, as a consequence, crashes are more severe. The observation that a 5% reduction in average speed is linked to a 10% reduction in injury accidents (OECD/ECMT Transport Research Centre, 2006) emphasizes the benefit of decreasing driving speed and the necessity of speed limit compliance. One possibility to make speed limits more “self-explaining” is to adjust road and roadside features so as to match operating and design speeds (Theeuwes and Godthelp, 1995). Implementations of self-explaining roads using complex visual properties to calm traffic in residential areas have already shown promising, albeit not uniformly positive results (Charlton et al., 2010). It would therefore be interesting to investigate whether additional information signs explaining the necessity to slow down may enhance the effect of self-explaining road features on speed. Increasing the complexity of road characteristics, however, may come at the cost of increased driver workload with potentially detrimental effects on traffic safety. Future research may address this by exploring how different road complexity levels affect speed. The presence of a passenger compared to driving alone, overall, had little effect on drivers’ choice of speed. While drivers made no adjustments in speed when driving with a passenger under the 30-km/h speed limit, the effects of the passenger’s presence on speed were mixed under the 50-km/h limit, somehow pointing to an effect of scenario complexity. The lack of a clear effect of passenger presence in the present study indicates that the effects of passenger presence reported in previous studies (e.g. Regan and Mitsopoulos, 2001) were likely due to the interaction between the driver and the passenger rather than the passenger’s mere presence. The study by Regan and Mitsopoulos (2001) also showed an effect of the passenger’s gender. In the present study, however, male and female drivers did not respond differently to the passenger, a fact that may be attributed to a lack of interaction between the driver and the passenger. Given that participants were in a safe experimental situation, passenger effects on speed that could, in principle, be observed may not have been revealed in the present study. An on-road driving study may thus help distinguish whether the effect of passenger presence depends on the interaction between the driver and passenger or whether passenger effects differ depending on the relationship between the driver and passenger. Mirroring the results of Anastasopoulous and Mannering (2016), the driver characteristics gender and driving experience only had an effect on drivers’ choice of speed in scenarios with a 50-km/h speed limit. Here, novice male drivers consistently adopted a higher speed than male expert drivers. Female drivers’ speed, in contrast, was unaffected by driving experience. The highest speed of all groups in this study was chosen by novice male drivers, a group which is found to be frequently involved in accidents. Interestingly, novice male drivers exceeded the speed limit mainly in the apparently easy and low-risk situations (straight and wide sections) which could mean that novice male drivers either overestimate their driving capabilities or perceive task demands differently than other driver groups. This finding is in line with other research (Roman et al., 2015) and underscores the importance of novice male drivers as a high-risk driver group. The large speed variance between drivers and the significant correlation between speeds under different speed limits point to individual differences in speed preferences. Although speed was generally adjusted to the driving situation at hand, the preference to adopt a relatively higher or lower speed appears to be stable in drivers (cf. Ahie et al., 2015). This result indicates that it is worthwhile to consider driver variables both in traffic flow simulations as well as for measures targeted at increased speed limit compliance and reduced speed dispersion on urban roads.

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One limitation of the present experiment is related to the methodology used. Due to the lack of motion feedback and distortions in the optical flow field in the driving simulator absolute speed differences obtained may not generalize to comparable on-road driving scenarios. Generalizability involves the correspondence of absolute numerical values within a statistically acceptable margin of error on the one hand and the relative correspondence of the direction and the magnitude of effects on the other (cf. Mullen et al., 2011). Knapper et al. (2015) conducted a study examining the validity of driving speed measured in the driving simulator which they supplemented by a nonexhaustive literature review. They showed that while evidence for absolute validity is inconsistent, with some studies showing a higher speed in the driving simulator and others a lower, the majority of studies – including their own – were able to establish relative validity of speed data obtained in the driving simulator. It is important to note that, unlike in real driving, speed limit enforcement was not part of our driving simulation. The absence of negative consequences may have led to higher speeds in the driving simulator. Given that speed-relevant factors in real driving can be expected to be confounded with the effect of speed enforcement, however, our results may be interpreted as showing the pure effects of road, driver, and passenger presence on speed. We therefore conclude that our results are a reasonably valid reflection of on-road driving speed patterns under the condition that drivers judge the likelihood of speed enforcement as being low. 5. Conclusion In this study, speed limitation and road geometry had the strongest effects on drivers’ speed choice, thus being promising targets for effective speed management measures. We conclude that in order for speed limit changes to be effective, it is important to consider their credibility in relation to road and roadside features. To a lesser extent, speed adaptation was also shown to depend on driving experience and the presence of a passenger. Further research is needed to assess whether different driver groups would benefit from group-specific or even individualized speed management measures. Our findings highlight the benefit of investigating the effects of road characteristics, driver, and the social driving environment in conjunction but also in isolation. This was only rendered possible using an experimental driving simulation framework, which provides a safe and controllable driving environment and, thus, allows identifying causal relationships. The present study therefore provides valuable input for practical traffic safety considerations but also for modeling driver behavior in traffic flow simulations. Acknowledgements This research has been funded by the German Research Foundation (DFG) through the Research Training Group SocialCars (GRK 1931). We wish to thank Doris Sonntag for her technical support by configuring the driving simulation as well as Madlen Ringhand, Matthias Powelleit, and Alexander Liebing for their helpful comments on an earlier version of this manuscript. References Aarts, L., van Schagen, I., 2006. Driving speed and the risk of road crashes: A review. Accident Analysis & Prevention 38 (2), 215–224. 10.1016/j.aap.2005.07.004. Ahie, L.M., Charlton, S.G., Starkey, N.J., 2015. The role of preference in speed choice. Transportation Research Part F: Traffic Psychology and Behaviour 30, 66–73. 10.1016/j.trf.2015.02.007. Amado, S., Arõkan, E., Kaça, G., Koyuncu, M., Turkan, B.N., 2014. How accurately do drivers evaluate their own driving behavior? An on-road observational study. Accident Analysis & Prevention 63, 65–73. 10.1016/j.aap.2013.10.022. Anastasopoulos, P.C., Mannering, F.L., 2016. The effect of speed limits on drivers' choice of speed: A random parameters seemingly unrelated equations approach. Analytic Methods in Accident Research 10, 1–11. 10.1016/j.amar.2016.03.001. Bassani, M., Dalmazzo, D., Marinelli, G., Cirillo, C., 2014. The effects of road geometrics and traffic regulations on driver-preferred speeds in northern Italy. An exploratory analysis. Transportation Research Part F: Traffic Psychology and Behaviour 25, 10–26. 10.1016/j.trf.2014.04.019.

14

Anne Goralzik et al. / Transportation Research Procedia 25C (2017) 2066–2080 Author name / Transportation Research Procedia 00 (2017) 000–000

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Bradley, J.V., 1958. Complete counterbalancing of immediate sequential effects in a latin square design. Journal of the American Statistical Association 53 (282), 525–528. 10.1080/01621459.1958.10501456. Bray, J.H., Maxwell, S.E., 1985. Multivariate analysis of variance. Sage, Newbury Park, CA. Bundesministerium der Justiz und für Verbraucherschutz, 2013. Straßenverkehrs-Ordnung (StVO). www.gesetze-iminternet.de/bundesrecht/stvo_2013/gesamt.pdf. Accessed 14 June 2016. Burgess, M., 2005. Contrasting rural and urban fatal crashes 1994-2003. DOT HS 809 896, National Highway Traffic Safety Administration. www-nrd.nhtsa.dot.gov/Pubs/809896.pdf. Accessed 14 June 2016. Charlton, S.G., Mackie, H.W., Baas, P.H., Hay, K., Menezes, M., Dixon, C., 2010. Using endemic road features to create self-explaining roads and reduce vehicle speeds. Accident Analysis & Prevention 42 (6), 1989–1998. 10.1016/j.aap.2010.06.006. Clarke, D.D., Ward, P., Truman, W., Bartle, C., 2007. Fatal vehicle-occupant collisions: An in-depth study. Road safety research report No. 75. Department for Transport, London. Committee for Guidance on Setting and Enforcing Speed Limits, 1998. Managing speed: Review of current practice for setting and enforcing speed limits. Special Report No. 254, Transportation Research Board, National Research Council, Washington, D.C. http://onlinepubs.trb.org/onlinepubs/sr/sr254.pdf. Accessed 14 June 2016. Dinh, D.D., Kubota, H., 2013a. Drivers' perceptions regarding speeding and driving on urban residential streets with a 30km/h speed limit. IATSS Research 37 (1), 30–38. 10.1016/j.iatssr.2012.12.001. Dinh, D.D., Kubota, H., 2013b. Speeding behavior on urban residential streets with a 30km/h speed limit under the framework of the theory of planned behavior. Transport Policy 29, 199–208. 10.1016/j.tranpol.2013.06.003. Elvik, R., 2013. A re-parameterisation of the Power Model of the relationship between the speed of traffic and the number of accidents and accident victims. Accident Analysis & Prevention 50, 854–860. 10.1016/j.aap.2012.07.012. Engström, I., Gregersen, N.P., Granström, K., Nyberg, A., 2008. Young drivers - reduced crash risk with passengers in the vehicle. Accident Analysis & Prevention 40 (1), 341–348. 10.1016/j.aap.2007.07.001. Fitzpatrick, K., Carlson, P., Brewer, M., Wooldridge, M., 2001. Design factors that affect driver speed on suburban streets. Transportation Research Record 1751 (1), 18–25. 10.3141/1751-03. Fleiter, J.J., Lennon, A., Watson, B., 2010. How do other people influence your driving speed? Exploring the ‘who’ and the ‘how’ of social influences on speeding from a qualitative perspective. Transportation Research Part F: Traffic Psychology and Behaviour 13 (1), 49–62. 10.1016/j.trf.2009.10.002. Forschungsgesellschaft für Straßen- und Verkehrswesen e.V., 2007. Richtlinien für die Anlage von Stadtstraßen: RASt 06. FGSV-Verlag, Cologne. Fuller, R., 2011. Driver control theory: From risk difficulty homeostasis to risk allostasis, in: Porter, B.E. (Ed.), Handbook of traffic psychology. Academic Press, London, Waltham, MA, pp. 13–26. Goldenbeld, C., van Schagen, I., 2007. The credibility of speed limits on 80 km/h rural roads: The effects of road and person(ality) characteristics. Accident Analysis & Prevention 39 (6), 1121–1130. 10.1016/j.aap.2007.02.012. Greaves, S.P., Ellison, A.B., 2011. Personality, risk aversion and speeding: An empirical investigation. Accident Analysis & Prevention 43 (5), 1828–1836. 10.1016/j.aap.2011.04.018. Hauer, E., 2009. Speed and safety. Transportation Research Record: Journal of the Transportation Research Board 2103, 10–17. 10.3141/210302. Horvath, C., Lewis, I., Watson, B., 2012. Peer passenger identity and passenger pressure on young drivers’ speeding intentions. Transportation Research Part F: Traffic Psychology and Behaviour 15 (1), 52–64. 10.1016/j.trf.2011.11.008. Islam, M.T., El-Basyouny, K., Ibrahim, S.E., 2014. The impact of lowered residential speed limits on vehicle speed behavior. Safety Science 62, 483–494. 10.1016/j.ssci.2013.10.006. Kanellaidis, G., 1995. Factors affecting drivers' choice of speed on roadway curves. Journal of Safety Research 26 (1), 49–56. 10.1016/00224375(94)00024-7 Kanellaidis, G., Dimitropoulos, I., 1998. Investigation of current and proposed superelevation design practices on roadway curves. Transportation Research Circular E-C003, 1–12.. Kloeden, C.N., McLean, A.J., Glonek, G., 2002. Reanalysis of travelling speed and the risk of crash involvement in Adelaide South Australia. CR 207. Australian Transport Safety Bureau, Canberra. casr.adelaide.edu.au/speed/RESPEED.PDF. Accessed 14 June 2016. Knapper, A., Christoph, M., Hagenzieker, M., Brookhuis, K., 2015. Comparing a driving simulator to the real road regarding distracted driving speed. European Journal of Transport and Infrastructure Research 15 (3), 205–225. Lee, C., Abdel-Aty, M., 2008. Presence of passengers: Does it increase or reduce driver's crash potential? Accident Analysis & Prevention 40 (5), 1703–1712. 10.1016/j.aap.2008.06.006. Mackie, H.W., Charlton, S.G., Baas, P.H., Villasenor, P.C., 2013. Road user behaviour changes following a self-explaining roads intervention. Accident Analysis & Prevention 50, 742–750. 10.1016/j.aap.2012.06.026. Montella, A., Aria, M., D’Ambrosio, A., Galante, F., Mauriello, F., Pernetti, M., 2011. Simulator evaluation of drivers’ speed, deceleration and lateral position at rural intersections in relation to different perceptual cues. Accident Analysis & Prevention 43 (6), 2072–2084. 10.1016/j.aap.2011.05.030. Mullen, N., Charlton, J., Devlin, A., Bédard, M., 2011. Simulator validity: behaviors observed on the simulator and on the road, in: Fisher, D.L. (Ed.), Handbook of driving simulation for engineering, medicine, and psychology. CRC Press, Taylor & Francis Group, Boca Raton, 13-113-18.

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OECD/ECMT Transport Research Centre, 2006. Speed management. OECD Publishing, Paris. http://www.itfoecd.org/sites/default/files/docs/06speed.pdf. Accessed 14 June 2016. Quimby, A., Maycock, G., Palmer, C., Buttress, S. The factors that influence a driver's choice of speed: A questionnaire study. TRL Report 325, Crowthorne. www.20splentyforus.org.uk/UsefulReports/TRLREports/trl325DriverSpeed.pdf. Accessed 14 June 2016. Regan, M.A., Mitsopoulos, E., 2001. Understanding passenger influences on driver behaviour: Implications for road safety and recommendations for countermeasure development. Report No. 180. Monash University Accident Research Centre, Melbourne. www.monash.edu/__data/assets/pdf_file/0004/216436/muarc180.pdf. Accessed 14 June 2016. Reiß, J., 1998. Das Unfallrisiko mit Beifahrern. Shaker Verlag, Aachen. 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. 10.1016/j.jsr.2013.08.002. Roman, G.D., Poulter, D., Barker, E., McKenna, F.P., Rowe, R., 2015. Novice drivers’ individual trajectories of driver behavior over the first three years of driving. Accident Analysis & Prevention 82, 61–69. 10.1016/j.aap.2015.05.012. Rudin-Brown, C.M., Edquist, J., Lenné, M.G., 2014. Effects of driving experience and sensation-seeking on drivers’ adaptation to road environment complexity. Safety Science 62, 121–129. 10.1016/j.ssci.2013.08.012. Rüger, F., Puruck, C., Schneider, N., Neukum, A., Färber, B., 2014. Validierung von Engstellenszenarien und Querdynamik im dynamischen Fahrsimulator und Vehicle in the Loop, in: 9. Workshop Fahrerassistenzsysteme - FAS 2014, Walting. 26.-28.03.2014, pp. 137–146. Schwebel, D.C., Severson, J., Ball, K.K., Rizzo, M., 2006. Individual difference factors in risky driving: The roles of anger/hostility, conscientiousness, and sensation-seeking. Accident Analysis & Prevention 38 (4), 801–810. 10.1016/j.aap.2006.02.004. Silvano, A.P., Bang, K.L., 2016. Impact of speed limits and road characteristics on free-flow speed in urban areas. Journal of Transportation Engineering 142 (2), 04015039-1-04015039-9. 10.1061/(ASCE)TE.1943-5436.0000800. Simon, F., Corbett, C., 1996. Road traffic offending, stress, age, and accident history among male and female drivers. Ergonomics 39 (5), 757– 780. Statistisches Bundesamt, 2016. Verkehr: Verkehrsunfälle 2014. Fachserie 8 Reihe 7, Wiesbaden. https://www.destatis.de/DE/Publikationen/Thematisch/TransportVerkehr/Verkehrsunfaelle/VerkehrsunfaelleJ2080700147004.pdf?__blob=p ublicationFile. Accessed 14 June 2016. Theeuwes, J., Godthelp, H., 1995. Self-explaining roads. Safety Science 19 (2-3), 217–225. 10.1016/0925-7535(94)00022-U. 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. 10.1016/S0001-4575(01)00064-1. Wilmot, C.G., Khanal, M., 1999. Effect of Speed limits on speed and safety: A review. Transport Reviews 19 (4), 315–329. 10.1080/014416499295420.