ripcord iserest deliverable d8 final

8/10/2019 Ripcord Iserest Deliverable d8 Final

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SIXTH FRAMEWORK PROGRAMMEPRIORITY 1.6. Sustainable Development, Global Change

and Ecosystem

1.6.2: Sustainable Surface Transport

506184

Road User Behaviour Model

Workpackage Title Road User Behaviour Model

Workpackage No. WP8 Deliverable No. D8

Authors (per company, if more thanone company provide it together)

Gert Weller, Bernhard Schlag (TUD)

Contributing Authors Ronald Jorna; Martijn van de Leur (Mobycon)

Giovanni Gatti (Poliba)

Status Final

File Name: RIPCORD-ISEREST Deliverable D8.doc

Project start date and duration 01 January 2005, 36 Months


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Deliverable D8 Dissemination Level (PU) Contract N. 506184

December 2007 2 TUD

List of abbreviations

ADT Average daily traffic

AADT Annual average daily traffic

al. Alii (others)

CCR Curvature Change Rate

CCRs Curvature Change Rate of the single curve

e.g. Exempli gratia (for example)

est. estimated

EU European Union

FFOV Functional Field of View

km Kilometer

m Meter

ms Millisecond

p. Page

PDT Peripheral Detection Task

R2 Regression parameter (proportion of variance explained)

r (or R) Radius

RECL Road Environment Construct List

rel. relative

RHT Risk Homeostasis Theory

RT Reaction Time

s Second

SDLP Standard deviation of lateral position

SER Self-Explaining Road

SPF Safety Performance Function

st. steady

t Time


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December 2007 3 TUD

TLC Time-to-line / Time-to-lane Crossing

TTC Time-to-collision / Time-to-contact

TUD Technische Universitt Dresden

UFOV Useful Field of View

v Speed

WP Work-package

% Percent

Degree

(t) Tau


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December 2007 4 TUD

Table of contents (main structure)

List of abbreviations .................................................................................................... 2

Table of contents (main structure) .............................................................................. 4

Table of contents (detailed structure) ......................................................................... 5

List of Figures ............................................................................................................. 8

List of Tables ............................................................................................................ 10

Executive Summary .................................................................................................. 11

1. Theoretical Background .................................................................................. 12

1.1. Introduction .................................................................................................. 12

1.2. Models of driving behaviour: an overview .................................................... 13

1.3.

Information-processing and perception ....................................................... 15

1.4. Driving as a self-paced task: Motivational models ....................................... 19

1.5. Application in rural road design: self-explaining roads ................................. 23

2. Model development and theoretical validation ................................................ 26

2.1. Overview ..................................................................................................... 26

2.2. Processes within the model in detail ............................................................ 26

3. Empirical validation: Methodology .................................................................. 32

3.1.

Formulation of Hypotheses .......................................................................... 32

3.2. Data sources for the testing of the hypotheses ........................................... 33

4. Empirical validation: Results ........................................................................... 36

4.1. Hypothesis 1: Affordances and cues (Data Source A)................................. 36

4.2. Hypotheses 2 and 3: Expectations (Data Source B). ................................... 41

4.3. Hypothesis 4: Workload: Psycho-physiology (Data Source C) .................... 45

4.4. Hypothesis 4: Workload: Reaction times (Data Source D) .......................... 47

4.5. Integration of behavioural data in the Safety Performance Function ........... 55

4.6. Subjective road categorisation .................................................................... 58

5. Empirical Validation: Conclusions ................................................................... 66

6. References ..................................................................................................... 68

7. Annex ............................................................................................................. 73


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December 2007 5 TUD

Table of contents (detailed structure)

List of abbreviations .................................................................................................... 2

Table of contents (main structure) .............................................................................. 4

Table of contents (detailed structure) ......................................................................... 5

List of Figures ............................................................................................................. 8

List of Tables ............................................................................................................ 10

Executive Summary .................................................................................................. 11

1. Theoretical Background .................................................................................. 12

1.1. Introduction .................................................................................................. 12

1.2. Models of driving behaviour: an overview .................................................... 13

1.3. Information-processing and perception ....................................................... 15

1.3.1 Attention, mental models and expectations .......................................... 15

1.3.2 Visual perception: the eye and the useful field of view (UFOV) ............ 17

1.4. Driving as a self-paced task: Motivational models ....................................... 19

1.4.1 Risk Models .......................................................................................... 19

1.4.2 Workload Models .................................................................................. 19

1.4.3 Behavioural adaptation ......................................................................... 22

1.5. Application in rural road design: self-explaining roads ................................. 23

2.

Model development and theoretical validation ................................................ 26

2.1. Overview ..................................................................................................... 26

2.2. Processes within the model in detail ............................................................ 26

2.2.1 Part I: Affordances and cues ................................................................. 26

2.2.2 Part II: Perceptual invariants ................................................................. 28

2.2.3 Part III: Expected and actual workload and risk .................................... 29

2.2.4 Part IV: Feedback ................................................................................. 30

3.

Empirical validation: Methodology .................................................................. 32

3.1. Formulation of Hypotheses .......................................................................... 32

3.1.1 Part I: Affordances and cues ................................................................. 32

3.1.2 Part II: Perceptual invariants ................................................................. 32

3.1.3 Part III: Expected and actual workload and risk .................................... 32

3.2. Data sources for the testing of the hypotheses ........................................... 33

3.2.1 Own simulator experiments (Data Source A) ........................................ 33

3.2.2 Additional collection of data related to available data (Data Source B) . 34

3.2.3

Reanalysis of available data (Data Source C)....................................... 34

3.2.4 Own driving experiments with an equipped vehicle (Data Source D) .... 35


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December 2007 6 TUD

4. Empirical validation: Results ........................................................................... 36

4.1. Hypothesis 1: Affordances and cues (Data Source A)................................. 36

4.1.1 Introduction ........................................................................................... 36

4.1.2 Method .................................................................................................. 36

4.1.3 Selected example: warning signs as formal cues ................................. 38

4.1.3.1. Method and descriptive analysis .................................................... 38

4.1.3.2. Results (selected example) ............................................................ 39

4.1.3.3. Discussion (selected example) ....................................................... 40

4.1.4 Discussion of Results ............................................................................ 41

4.2. Hypotheses 2 and 3: Expectations (Data Source B). ................................... 41

4.2.1 Introduction ........................................................................................... 41

4.2.2

Method .................................................................................................. 42

4.2.3 Summary of results ............................................................................... 43

4.2.4 Discussion of results ............................................................................. 44

4.3. Hypothesis 4: Workload: Psycho-physiology (Data Source C) .................... 45

4.3.1 Introduction ........................................................................................... 45

4.3.2 Method .................................................................................................. 45

4.3.3 Results .................................................................................................. 46

4.3.4 Discussion............................................................................................. 46

4.4. Hypothesis 4: Workload: Reaction times (Data Source D) .......................... 47

4.4.1 Introduction ........................................................................................... 47

4.4.2 Method .................................................................................................. 47

4.4.3 Selected Analysis .................................................................................. 48

4.4.3.1. Description of the selected locations .............................................. 48

4.4.3.2. Results for the selected location ..................................................... 50

4.4.3.3. Discussion for the selected location ............................................... 53

4.4.4

Discussion............................................................................................. 54

4.5. Integration of behavioural data in the Safety Performance Function ........... 55

4.5.1 Introduction ........................................................................................... 55

4.5.2 Method .................................................................................................. 55

4.5.3 Results .................................................................................................. 56

4.5.4 Discussion............................................................................................. 57

4.6. Subjective road categorisation .................................................................... 58

4.6.1

Introduction ........................................................................................... 58

4.6.2 Method .................................................................................................. 59

4.6.3 Results .................................................................................................. 60


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December 2007 7 TUD

4.6.4 Discussion............................................................................................. 65

5. Empirical Validation: Conclusions ................................................................... 66

6. References ..................................................................................................... 68

7. Annex ............................................................................................................. 73


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December 2007 8 TUD

List of Figures

Figure 1: Proportion of accident causation factors according to Treat et al. (1977). . 12

Figure 2: Overview of different driver behaviour models. ......................................... 13Figure 3: Combination of performance levels according to Rasmussen (1986) and the

hierarchical model according to Michon (1985), modified from Donges (1982, in1999). ................................................................................................................ 14

Figure 4: The generic error-modelling system (GEMS) as proposed by Reason,(1990). ............................................................................................................... 15

Figure 5: Hypothetical differences in speed and workload in curves with good (left)and inappropriate design (right) (modified from Fuller 2005). ............................ 20

Figure 6: Workload assessment methods and their relationship within general safetyassessment. ...................................................................................................... 21

Figure 7: Behavioural adaptation: resulting final outcome in safety. ......................... 22

Figure 8: Process model of behavioural adaptation (Weller & Schlag, 2004). .......... 23Figure 9: Model of driving behavior on rural roads ................................................... 26Figure 10: Detailed processes within part III of the driver behaviour model for rural

roads: Safe distance keeping by using perceptual invariants proposed by Lee(1976) and Lee & Lishman (1977), tested e.g. by Yilmaz and Warren (1995).Adapted from Bruce et al. (1996). ..................................................................... 28

Figure 11: Detailed processes within part II of the driver behaviour model for ruralroads: Expected and actual workload and risk. ................................................. 30

Figure 12: Simulator of the Fraunhofer IVI in Dresden that was used for the simulator

experiments (www.ivi.fhg.de). Source: Fraunhofer IVI. ..................................... 33

Figure 13: Experimental vehicle of the Chair of Road Design at TUD. ..................... 35Figure 14: Birds eye view of the simulated road sections in the Fraunhofer IVI

simulator. ........................................................................................................... 37Figure 15: Speed [km/h] averaged across all subjects for a curve with additional signs

(bend ahead and guidance signs in the curve; curve K3) and the comparisoncurve (K19) in the to- and the backwards direction (=R). All curves are leftcurves; driving direction from left to right side. On the x-axis the distance fromcurve beginning is shown. The vertical bars mark the beginning (left bar) andend (right bar) of the curve. ............................................................................... 38

Figure 16: Pictures from the low (left) and the high (right) accident rate curve in pair

No. 2 as used for the collection of the subjective ratings. .................................. 42Figure 17: Pictures of the low accident rate road sections 21 (left side) and the high

accident rate road section 24 (right side) in the to direction (pictures taken fromRoadView TUD). ............................................................................................... 49

Figure 18: Curvature plan of sections 21 (low accident rate curve) and 23 (highaccident rate curve) within the whole road stretch. From right to left: drivingdirection in to-direction; from left to right: driving direction in backwards-direction. .......................................................................................................................... 49

Figure 19: Averaged values for speed, reaction time (measured and interpolated) andfixation duration for the low accident rate curve (section 21). ............................ 51

Figure 20: Averaged values for speed, reaction time (measured and interpolated) and

fixation duration for the high accident rate curve (section 24). .......................... 51


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December 2007 9 TUD

Figure 21: Relation of the percentage increase in reaction time with the percentagedecline in speed (x-axis). Each value on the x-axis represents a single roadsection. Added is the statistics of a linear regression. ....................................... 56

Figure 22: Results of the hierarchical cluster analysis (dendrogram) (SPSS.14). .... 62Figure 23: Average factor values for each picture in the different clusters. .............. 63


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December 2007 10 TUD

List of Tables

Table 1: Matrix of the data sources used and the hypotheses tested. ...................... 33Table 2: Simulator experiments: results of the t-Tests for paired samples for different

speed parameters for K3 (signs) and respective comparison curve (K19) in bothdirections. .......................................................................................................... 39

Table 3: Simulator experiments: results of the t-Tests for paired samples for thedistances of the maximum speed before the curve (200m to beginning of curve)and the minimum speed after the beginning of the curve. Curves K3 and K19. 40

Table 4: Results of the Wilcoxon-Test for paired samples for the subjective ratings forcurves. ............................................................................................................... 40

Table 5: Results of the Wilcoxon-Test for paired samples for the item The curve issharp. ............................................................................................................... 43

Table 6: Results of the Wilcoxon-Test for paired samples for the item The curvegives good information concerning the following curve path. ........................... 43

Table 7: Results of the Wilcoxon-Test for paired samples for the item The roadstretch is demanding . ...................................................................................... 44

Table 8: Results of the Wilcoxon-Test for paired samples for the item The curve isdangerous. ....................................................................................................... 44

Table 9: Statistical differences of physiological data and speed between high and lowaccident rate curves. ......................................................................................... 46

Table 10: Average fixation duration [s]; results of the t-Test for paired samples; lowversus high accident rate curve (section 21 versus 24). .................................... 52

Table 11: Average speed [km/h]; results of the t-Test for paired samples; low versushigh accident rate curve (section 21 versus 24). ............................................... 52

Table 12: Average reaction times [s] interpolated values; results of the t-Test forpaired samples; low versus high accident rate curve (section 21 versus 24). ... 52Table 13: Results of two linear regression analysis of different reaction time

parameters on the percentage decline of speed for different road sections. Oncefor all twelve sections and once without the extremes on both sides. ............... 56

Table 14: Varimax normalized factor loadings of the RECL items after factor analysis. .......................................................................................................................... 61

Table 15: Road-cluster and factor characteristics combined in a matrix. .................. 63Table 16: Results of the regression analysis of the three factors on the speed ratings

for each road picture. ......................................................................................... 64Table 17: Distinctive objective features between the clusters resulting from the

subjective ratings. .............................................................................................. 65


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Executive Summary

The deliverable at hand describes the steps which were undertaken to develop andvalidate a driver and driving behaviour model for rural roads.First, an overview is given of the theoretical background relevant to these steps.

Theories of human perception, information processing, decision making and action ingeneral are included, as well as psychological theories especially developed toexplain driver and driving behaviour.The second part of Deliverable D8 introduces the model which was developed basedon the theoretical work summarized in the first chapter. This model describes,explains and predicts driver and driving behaviour on rural roads.The third part of Deliverable D8 summarizes the steps which were conducted tovalidate this driver and driving behaviour model for rural roads and to test thepossibility to integrate psychological parameters in a safety performance function(SPF). Depending on the different hypotheses derived from the model, the followingdata sources were used for this process:

- existing driving studies,

- additional data collected based on this existing data,- own additional simulator experiments,- own additional driving studies with an equipped vehicle.

Additionally, own laboratory experiments were conducted in a study of subjectiveroad categorization. The results found after analysing the data collected in all thesestudies support the assumptions formulated in the models to a large extent. Forexample, it was shown that high accident rate curves are systematicallyunderestimated concerning demand and risk. Evidence was found that thisunderestimation results in inappropriate speed behaviour which could cause

accidents. Further on, the influence of cues and affordances in influencing drivingbehaviour could be shown and is reported in this deliverable exemplarily for roadsigns. The assumption that workload or risk is higher in high accident rate roadsections, compared to low accident rate road sections was only indirectly supportedby the empirical data. This finding is in line with the results of the study on subjectiveroad categorisation. While we found in general, that these road categories influencebehaviour these categories themselves are built based on affordances and cues,rather than expected workload or risk. Further, we could identify objective criteriawhich could be used to approximate these subjective categories. These results inturn could provide a valuable input towards harmonizing roads in Europe along self-explaining road principles. Finally the integration of psychological variables in a

safety performance function was tested. Despite of the validation process of themodel was successful, it was not possible to assign numeric values to psychologicalparameters, which would be the prerequisite for an integration in the SPF. However,we could show that characteristic speed parameters could be used to approximatee.g. workload. The application of speed prediction models in the SPF thus allows atleast a preliminary solution. By integrating the findings of our studies in future steps,the quality of these speed prediction models could be considerably enhanced. Theintegration of such psychological factors in speed prediction models for rural roadscould ultimately result in a valid safety performance function. The most importantfuture step to do so is to increase the number of cases (i.e. road sections) in thedatabase to be able to derive stable parameter values.


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1. Theoretical Background

1.1. Introduct ion

A study originally published by Treat et al. (1977) revealed that human factors are tobe blamed for the majority of accidents (see Figure 1).

Human (95.4%)

Environment (44.2%)Vehicle (14.8%)

47.8%

0.4%

34.8%6.4%

1.6%2.6%

6.4%

Figure 1: Proportion of accident causation factors according to Treat et al. (1977).

While the statistics suggest that the roads are hardly to be blamed for accidents,analysis on a site basis reveals that human errors occur in specific sites more often

than in other sites. This is notably true for rural roads: despite rural roads ranking byfar highest concerning the number of people killed the danger associated with themis clearly underestimated by drivers (Ellinghaus & Steinbrecher, 2003).The majority of accidents at these sites is due to a mismatch between environment(the road) and human characteristics. This is depicted by the high proportion ofaccident causation factors as interaction between environment and human (seeFigure 1).The following pages give an overview of how road environments interact with humanproperties and how both factors have to be taken into account in order to designsafer rural roads.


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1.2. Models of driving behaviour: an overview

This chapter gives a short overview of different driver and driving behaviour models.The aim is to make the reader familiar with the most important terms. While details of

some models are explained further in the text, others will only be mentioned here. Amore detailed discussion of different models can be found in Michon (1985) orRanney (1994) and the internal report 8.1 (Weller, Schlag, Gatti, Jorna, & Leur,2006).

Hierarchical models(e.g. Michon, 1985)

Control loop models(e.g. Durth, 1974)

take into account complex interactions(road / driver / vehicle) in driving

emphasis on individualdifferences, static in nature

Functionalmodels

Taxonomicmodels

cognitivemodels

(e.g. Rumar, 1985)

Riskmodels

(e.g. Wilde, 1994)

motivationalmodels

(driving is a self-pacedtask)

informationprocessing

models

theory of directperception(Gibson, 1986)

Workloadmodels

(e.g. Fuller, 2005)

constitute a framework of the driving task in which other theories can be integrated

Figure 2: Overview of different driver behaviour models.

Hierarchical (Michon, 1985) and control loop models (Durth, 1974) serve as a

framework for other theories. A widespread hierarchical model developed by Michon(1971, 1979, cited from 1985) and Janssen (1979, cited from Michon, 1985) seesdriving as a hierarchical problem solving task that comprises three different levels.These levels can be divided by the specific task requirements on each level, the timeframe needed to carry them out, and the cognitive processes involved. Thehierarchical task model of Michon finds its equivalent in the distinction betweendifferent performance or behaviour levels proposed by Rasmussen (1986).Rasmussen distinguished between knowledge-based, rule-based, and skill-basedlevels of a task in general. Both models can be combined as proposed by Donges(1982, cited from 1999) (see Figure 3).


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Strategic Level

Manoeuvring Level

Control Level

Feedback Criteria

Route

Speed Criteria

Knowledge-based Behaviour

Rule-based Behaviour

Skill-based Behaviour

Sensory Input Action

Feature

Formation

Stimulus

Reaction

Automatisms

Recognition Association Stored rules

Identification Decision Planning

Figure 3: Combination of performance levels according to Rasmussen (1986) and the

hierarchical model according to Michon (1985), modif ied from Donges (1982, in 1999).

The left section in Figure 3 represents the different task levels proposed byRasmussen, while the right section represents the model by Michon. The strategic ornavigational level comprises all processes concerning trip decisions, like where to go,when to go, what roads to take, and what modes of transport to use. Decisions on

this level are rare and take longest in comparison to the other levels. Due to theirnature they are processed in a more or less aware mode, but become habits in caseof constant repetition. On the manoeuvring level decisions are made within seconds.Typical manoeuvres are overtaking, turning, or gap acceptance. Behaviour on themanoeuvring level is both influenced by motivational and situational variables. Otherterms used to describe the manoeuvring level are tactical or guidance level. Finally,decisions on the control level are made rather automatically within a very short timerange as stimulus response reactions. Typical tasks on this level are lane keeping orgear shifting. These are both conducted without conscious information-processing byexperienced drivers. The terms operational or stabilisation level are usedconcurrently.Whether a task is situated on the knowledge-based, rule-based, or skill-based level,depends to a great amount on the familiarity with the task and the environment.Higher order processes situated on the knowledge-based level in general requiremore cognitive resources than lower level processes. Higher and lower levels ofprocessing are usually referred to as controlled or automatic processing according toSchneider and Shiffrin (1977) and Shiffrin and Schneider (1977).The knowledge whether the behaviour under observation is situated on the automaticor the control level is very important as the strategies to change this behaviourdepend on these levels. Only controlled processes can be modified by awarenesscampaigns, while behaviour on the automatic level needs constant reshaping.

The following Figure 4 gives a flow chart of how decision making and problem solvingmight be arranged in driving. Note that higher order processes are only used whenlower order processes do not lead to the desired output.


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Skill-Based Level

Routine actions in a

familiar environment

Attentional checks on

progress of action

ProblemRule-Based

Level

Knowledge-

Based Level

Consider local state

information.

Find higher level

analogy

Revert to mental

model of the

problem space.

Analyse more

abstract relations

between structure

and function.

Apply stored rule IF

(situation) THEN

(action).

Infer diagnosis and

formulate corrective

actions. Apply

actions. Observe

results, ... etc.

OK? OK?

Is problem

solved?

Is the pattern

familiar?

Subsequent attempts

Goal

state

Yes

Yes

Yes

No

No

No

None Found

Figure 4: The generic error-modelling system (GEMS) as proposed by Reason, (1990).

The crucial point for rural road design is that people, in general, rather rely on pre-programmed behavioural sequences found on the skill-based level, than revert tohigher-order processes. This is because the latter processes require more resources.Similar, rule-based behaviour will be preferred to knowledge-based behaviour as humans, if given a choice, would prefer to act as context-specific pattern recognizersrather than attempting to calculate or optimize (Rouse, 1981, cited from Reason,1990, p. 65 ).

1.3. Information-processing and perception

1.3.1 Attention, mental models and expectations

Human perception and information processing is influenced by two concurrentsystems, a bottom-up and a top-down pathway.In short, top-down processing means that the driver has formed some kind ofhypothesis on what to expect in a given situation. Bottom-up processing in contrastmeans that attention is guided by stimuli in the environment, without higher order

cognitive functions.Processes involved in top-down processing are attention, experience, motivation andexpectations. Expectations in turn are formed from past experiences. The more


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similar the new situation is to a past situation, the stronger these expectations will befor the current situation. These expectations in turn help the driver to direct attentionto locations where he assumes to find relevant information. The totality ofexpectations related to a specific situation form a mental model or internalrepresentation of the whole situation. Other terms in relation to mental models are

schemata or scripts. All represent implicit or explicit knowledge of situations oractions.Due to its nature, top-down processing requires more time than bottom-upprocessing. Nevertheless it still increases efficiency and effectiveness in humanbehaviour due to its simplification in comparison to nature. Second, the use of mentalmodels is automatic rather than conscious and therefore needs less resources inworking memory. Top-down processing further guides attention to relevant stimuliand therefore allows an efficient allocation of attentional resources. Finally it allowsthe driver to actively search and infer missing information.This advantage can easily become a disadvantage when the current situation ismisinterpreted, e.g., on the basis of inappropriate expectations and misguided

attention. Therefore, internal representations can be the underlying cause behindfaulty actions or faulty assumptions themselves (Hacker, 2005; Norman, 1981;Reason, 1990). Further, the stable nature of internal representations makes themhard to be changed by single actions.Concerning top-down processes it should be taken care that the road characteristicsare in line with the drivers expectations (top-down). In order to change wrong mentalmodels, feedback has to be provided in case of inappropriate behaviour.On the other hand perception is a bottom-up process, meaning, amongst others, thatenvironmental stimuli guide attention as well. Whether attention will be attracted to astimulus or not, depends on the physical characteristics of this stimulus. As the focusof attention is very narrow due to the characteristics of the eye (see below) the stimuliwill first be perceived by peripheral attention. Peripheral attention is captured moreeasily by moving objects. Stationary objects with low luminance contrasts will behardly detected by human vision.Therefore, it has to be taken care that non-relevant information does not captureattention (bottom-up) in locations that are supposed to be dangerous while on theother hand relevant information has to be designed to attract attention.The relevance of expectations and mental models for rural road design is in factalready tackled in the engineering concept of consistency (e.g., concerningcurvature). Consistency, in this context, means that the driver expects the followingroad section to be similar to the preceding road section, unless indicated by some

environmental cue. Besides being used in design guidelines for rural roads (e.g.RAS-L: FGSV, 1995), consistency is an important aspect of safety. Lamm et al.(2006) successfully applied the following three criteria to assess the safety level ofrural roads:

- design consistency as indicated by the design speed,- operating speed consistency as indicated by differences in V85between

successive elements,- and consistency in driving dynamics, mainly based on side friction.

Accidents often occur when the drivers expectations do not match the road situation,that is, the road is not consistent.


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1.3.2 Visual perception: the eye and the useful f ield of v iew (UFOV)

Most information needed for driving is taken up predominantly visually.Understanding vision, therefore, helps to understand and explain safe or unsafebehaviour on rural roads.

In the retina of the human eye, two different light receptor cells (rods and cones) withdifferent characteristics are to be found. The uneven distribution of these cells in theretina is the reason for an approximately linear degradation of many visual functionswith eccentricity from the fovea. Referring to this degradation, often the terms foveal,parafoveal (near but not in the fovea) and peripheral or ambient vision are used.Object identification, which requires deep processing, is only possible in foveal visionand in a very narrow cone around the point of fixation. In contrast to foveal vision,peripheral vision allows a broad area to be scanned without identifying objects. It canbe seen as alerting system for saccades (very fast eye movements) to bring theobject of interest into foveal vision. Peripheral vision is further very important for thecorrect perception of speed. These different visual systems are related to two

different pathways of information processing in the brain (Milner & Goodale, 1995).Both the areas of foveal and peripheral vision are limited and subject to change. Todescribe these changes and the areas affected, different terms are in use:

- functional field of view (FFOV)- useful field of view / of vision (UFOV)- visual field- tunnel vision

UFOV can decrease because of different reasons. One of the reasons is changes indemand or workload (see below). Related to demand, some authors see complexityto be the reason behind diminishing UFOF size (Miura, 1990; Recarte & Nunes,2000). Decreased UFOV size due to higher speeds is as well reported (e.g., Land &

Horwood, 1995). The importance of peripheral vision for speed perception could beshown by Cavallo & Cohen (2001) who found that correct speed estimation issignificantly reduced when the size of the visual field, and thus peripheral vision, isdiminished. Recarte and Nunes (2000) used the spatial distribution of fixations todescribe these changes. However, when discussing effects on peripheral vision it isimportant to note that the terms introduced above are not used consistently betweenauthors.Within this framework of perceptual processes further characteristics of humanperception have to be taken into account when dealing with secondary rural roadsafety. Some of them are summed up as follows (for further aspects see e.g., Bruce,Green, & Georgeson, 1996):

- The human eye needs time to adapt to different light conditions. Thetime for rods and cones to adapt from brightness to darkness takeslonger than vice versa and might take up to 30 minutes for rods (vonCampenhausen, 1993). This is relevant when entering tunnels oralleyways in daylight.

- The human eye needs time to accommodate from near to far and viceversa. This accommodation is relevant when drivers direct theirattention from inside the car (e.g., speedometer) to outside the car.Accomodation is faster from near to far than vice versa.

- Human information processing capabilities are limited. When the

amount of information is too high, relevant information might not beperceived by the driver.


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- The human eye is only sensitive for light of a very narrow bandwidthand high contrasts. Given contrast sensitivity, it has to be assured thatvisual information can be perceived in the environment and backgroundwhere it is presented.

- Foveal vision is very restricted but identification of objects is only

possible when they are fixated.- Human perception depends on the context and is relative to other

stimuli, as shown by psychophysics (Weber, Fechner, Stevens,overview e.g., in Goldstein, 2005)

A theory, which stresses the importance of visual perception, was developed byGibson (1986). This theory of direct perception highlights the importance ofcharacteristics present in the environment and the influence of ecological invariants.Time-to-collision (TTC) or Tau and time-to-line-crossing (TLC) (Godthelp, Milgram, &Blaauw, 1984) are examples of such invariants. Further, Gibson assumes thatinformation is directly picked up from the inherent properties of the objects. Theseproperties are called affordances. Affordances convey a meaning to the observer in

the sense of being able (e.g., climbable). They thus serve as cue to prompt therespective behaviour at the same time.Contrary to Rumars model (1985), Gibson uses a mere bottom-up approach. Bothagree however, that perception is an active process. While Rumar stresses theimportance of cognitive factors, Gibson sees movement as the crucial aspect ininformation acquisition. Movement of the body and the eye help to perceive theproperty of objects and environments. Therefore, the human body as a wholebecomes the organ of perception, and not the eye alone. Through movement,information of depth, distance, or speed is conveyed to the driver. This information isperceived directly from the rate of change in the texture or the so called optic flowfield. The optic flow field can be imagined as a bunch of vectors created by changesin light due to movement. The focus of the flow field specifies the direction where theobserver is heading. Warren et al. (1991) showed that circular heading whennegotiating a curve is also derived from the optic flow field.But even without movement, objects convey information through their texture andocclusion of their contour by other objects (examples are given in Bruce et al., 1996).While human perception becomes effective through the use of this information, it canbe a source of error itself as is shown by optic illusions.With perception being the basis for action, environmental design to support desirablebehaviour is crucial in designing safer roads.The following principles derived from characteristics in visual perception should be

known by road designers:- highly textured environments usually diminish speeds- roadside objects should follow road geometry in order to support the

drivers expectations- the perceived characteristics of road elements are more important for

behaviour than the real characteristics. By applying visual elements theperceived characteristics can be changed.

In case mere perceptual measures are not possible due to environmental constraints,road designers can still revert to traditional measures like posting speed limits onsigns and enforcing compliance with cameras. In fact, there are several studies thatindicate that these measures reduce speed and accidents (for an overview see Elvik

& Vaa, 2004).


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1.4. Driving as a self-paced task: Motivational models

While the models on the role of perception in driving rather highlight commoncharacteristics of the whole driver population, motivational models take into accountinteractions between general mechanisms and individual differences.

The unifying assumption of motivational models is that they stress the self-pacednature of the driving task. Two concepts that could thus be called motivational arerisk and workload. Closely related is the concept of behavioural adaptation.

1.4.1 Risk Models

The central aspect for risk models is the distinction between subjective and objectiverisk. Klebelsberg (1982) defines objective risk as the measurable probability of havingan accident, while subjective risk is the estimated risk by the driver through theperception of the road environment. According to Klebelsberg, situations are unsafeas soon as subjective risk is lower than objective risk. This is because drivers adjust

their behaviour according to subjective, not objective risk.The concept of subjective risk as relevant mechanism for driving behaviour wasfurther developed by Wilde (1988; 1994). Originally called theory of risk homeostasis(RHT) it was later termed the theory of target risk. In short, the theory states thataccident rates per unit time remain equal, despite objective improvements, as driversadjust their behaviour so that their subjective risk equals their more or less constanttarget risk. Elvik & Vaa (2004) sum up the shortcomings of the theory but at the sametime agree with other researchers that the theory has identified importantmechanisms, which should be taken into account when explaining accident causationmechanisms. A theory applicable on the individual level was developed by Ntnen& Summala (1976).

1.4.2 Workload Models

Due to the shortcomings of risk theories, Fuller (2005) developed a theory based onthe comparison between task demand and human capability. The resulting outcomeof this comparison is the amount of workload a driver experiences. In general,workload is lowest and performance is best at medium levels of demand. Both under-and overload caused by a mismatch between demand and capability are detrimentalon performance, although compensation due to additional effort invested is possible(see e.g. de Waard, 1996).According to Fuller (2005) driving is save as long as capability exceeds demand.Besides being a function of the objective environmental characteristics, the demandof the driving task at a given time or location, depends on the speed level selected bythe driver. The demand of a difficult situation can be substantially decreased bylowering the speed. In order to keep workload at a medium, optimal level, thesituation has to convey the necessary information to the driver in advance.The effects of early versus late presentation of appropriate information on workloadare depicted in Figure 5.


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Station [m]

Speed

Horizontal alignment

Workload

[1/ R]

End ofcurve

Begin of

curve

Station [m]

Speed

Horizontal alignment

Workload

[1/ R]

End of

curve

Begin of

curve

Figure 5: Hypothetical differences in speed and workload in curves with good (left) and

inappropriate design (right) (modified from Fuller 2005).

In the left image early information leads to early, smooth speed reduction and asubsequent steady level of workload. In the right image, curve characteristics are

perceived too late, leading to a high and sudden decrease in speed, which in turnresults in a massive increase in workload.Despite Figure 5 suggesting otherwise different forms of demand, capability andworkload are distinguished. This distinction is mainly based on Wickens (e.g., 1991)who distinguished resources according to the task characteristics, the senses used totake up and process the information and, finally, the modality with which the resultingaction is carried out. Depending on these categories, human resources are regardedas being independent. Therefore, it is often better to present critical informationauditory and not visually as the visual system in driving is usually subject to verymuch other visual information.For the assessment of workload different techniques are in use. Which workload

measurement technique is used, depends first of all, on the quality of the measuresas described by O`Donnell and Eggemeier (1986, cited from de Waard, 1996;Wickens, 1992) and the requirements and restrictions of the experimental situation.Usually the following five techniques are distinguished:

- self-report measures

- primary task measures

- secondary task measures (dual task paradigm)

- physiological measures

- visual occlusion.

As the most important contributor to the amount of workload in road safety is theamount of demand (not the capacity of the single driver) the road characteristics haveto be assessed with equal care. The following characteristics are a selection of themost important elements contributing to demand on rural roads:

- vertical and horizontal alignment

- deduced parameters like curvature and consistency

- road furniture, including lines

- surrounding vehicles

- environmental conditions at the time of the assessment.


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Demand and workload assessment together with the five measurement techniques ofworkload, as explained in the report, are shown in Figure 6 as part of a general safetyassessment procedure for rural roads.

assessment of objective task demand:

(horizontal and vertical alignment; curvature andconsistency; environment; etc.)

assessment of long-term consequences of

workload

(e.g. monotony, fatigue)

assessment of workload

(visual, mental,

physical)

primary task measures (speed

acceleration, posit ion, etc.)

secondary task measures

self-report measures

occlusion (if applicable)

psycho-physiological

measures

assessment of

traits & state

relation to objective

consequences

(accidents)

Figure 6: Workload assessment methods and their relationship within general safety

assessment.

Due to the self-paced nature of the driving task and interactions between parameters,the exact amount of demand is hard to determine. Nevertheless, some approachesprovided good results in determining demand. Wagner & Richter (1997) and Wagner(2000) proposed a procedure based on video ratings. They combined several criteriarated in advance as useful by engineers and psychologists. The criteria selectedwere divided into three groups:

- Information-uptake: amount; variability; contrast; spatial and temporal density;visual guidance.

- Road quality: surface; orientation possibilities and compatibility withexpectations; early perception of danger.

- Sensorimotor aspects of car driving: hand; foot; coordination and automaticprocessing of motor response.

The resulting scale (ANSITAX) was presented to different expert groups and resultedin high reliability, both between groups and within groups at different times. Jointassessment by psychologists and engineers proved successful in a study conductedin Switzerland, too (Allenbach, Hubacher, Huber, & Siegrist, 1996).


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1.4.3 Behavioural adaptation

Behavioural adaptation describes the phenomenon that people adapt their behaviour

to changing situational demands. In 1990 the OECD (1990) defined behaviouraladaptation as: those behaviours, which may occur following the introduction ofchanges to the road-vehicle-user system and which were not intended by theinitiators of the change; Behavioural adaptations occur as road users respond tochanges in the road transport system, such that their personal needs are achieved asa result, they create a continuum of effects ranging from a positive increase in safetyto a decrease in safety (p. 23). Summaries of studies dealing with behaviouraladaptation can be found in the OECD report (1990). Whether the net outcome ispositive or negative depends on the amount of not intended factors due tobehavioural adaptation as shown in Figure 7.

Road safety

measure

Net resulting final

outcome

(accidents, etc.)

Other risk factors(effect of behavioural

adaptation not intended)

Target risk factors(effect as intended by

engineering factors)

Figure 7: Behavioural adaptation: resulting final outcome in safety.

One could argue similar to RHT that behavioural adaptation implicates that soleengineering measures would not result in a reduction of accidents. In fact there arepublications supporting this assumption. When comparing data from a 14 year period(1984-1997) of 50 US states it was found that the downward trend in fatalities is dueto demographic factors, an increase in passive safety and improvements in medicaltechnology (Noland, 2003). Improvements in infrastructure did sometimes even havenegative effects suggesting behavioural adaptation. Infrastructure included total lanemiles, average number of lanes, lane width and percentage of each road class.

Curvature, shoulder width, separation of lanes and presence of roadside hazards arenot included but it is implicitly assumed that newer roads are built in a safer way.Noland (2003) provoked with the conclusion: Results strongly refute the hypothesisthat infrastructure improvements have been effective at reducing total fatalities andinjuries. (p. 599).Rothengatter (2002) states however, that adaptation in fact occurs but that theeffects are not strong enough to eat up positive impacts of safety measures.Somewhat contrary Dulisse (1997) points out that the effects of behaviouraladaptation are sometimes even underestimated due to methodological shortcoming(for example, inclusion of drivers who wore seat belts even before wearing was madecompulsory).

The different findings concerning the amount of behavioural adaptation can beexplained by the multiple factors that influence the occurrence of behaviouraladaptation. These factors were summarized in a model developed by Weller &


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Schlag (2004) (see Figure 8). Similar aspects are named by Bjrnskau (1994; citedfrom Elvik & Vaal, 2004).

Changes in vehicle or

environment

Objective enhancement of

safety margins?

Subjective enhancement of

safety margins?

Subjective enhanced utility of

adaptation?

Adaptation

Advertising,

Information, etc.

Feedback to driver

Driver personality:

h Sensation Seeking

hAge, etc.

Driving motives

N

o

A

d

a

p

t

a

ti

o

n

Potential

changes in:

hTrust

hSituational

Awareness

hAttention

hWorkload

hLocus of

Control No

No

No

Yes

Yes

Yes

Figure 8: Process model of behavioural adaptation (Weller & Schlag, 2004).

According to this model, the implemented measure has first to provide the objectivepossibility to change ones behaviour in an unsafe way. Second, the driver has toperceive this possibility. Whether the change is perceived depends on thecommunication of the measure through media information or advertisements on onehand and on direct feedback to the driver on the other hand. To result in adaptation,the change in behaviour further has to be perceived as being positive for the driver(utility maximization). This function is different between different driver groups (e.g.,age groups), as well as within the same driver group (e.g., driver while being in ahurry or not). Independent of this chain of action (objective enhancement, subjectiveenhancement, utility maximization), there is a second path that leads to adaptation,namely direct change of genuine psychological variables. These changes are a directoutcome of changes in the environment (or the car) and the following changes in the

nature of the driving task. When the driving task becomes more easy due to changesin the alignment (straight instead of curved), workload might decrease and speedmight be increased as a consequence. In fact, workload is seen as being equallyimportant as risk to explain driving behaviour.

1.5. Application in rural road design: self-explaining roads

Research results on information-processing and perception as described in thepreceding chapters, were applied in the development of a high successful roaddesign concept, the self-explaining road or SER concept.In short, the term self-explaining already implicates the meaning of SER design:

roads designed along SER principles should elicit appropriate behaviour solely dueto their perceived design and without further need on the side of the driver to


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consciously elaborate the required behaviour. Obviously, how this is achieved indetail requires further explanation.Before application in the field of traffic, principles of self-explaining design weredeveloped by Donald A. Norman in his book The design of everyday things (1998).According to these principles road design should follow cultural standards or physical

analogies as stored in mental models. Only where this mapping principle is not self-explaining a conceptual model has to be provided with the help of additional cues likesigns. These cues should follow the principle of visibility to allow the driver tocorrectly predict the outcome of his actions. Visibility, first of all, means thatinformation:

- has to be physically visible and mentally recognizable (for everyone)

- has to be presented at locations and in a way that are in accordance withhuman expectations

- must guide behaviour in a self-explaining way: no explanation needed.

However, visibility in Normans sense exceeds this meaning as visibility ofbehavioural outcomes is included, too. It is related to feedback, which communicatesthe appropriateness of behaviour to the driver. Thus, self-explaining properties haveto be added by self-enforcing impact if things are not used appropriately. Affordancesin the sense of Gibson have to be provided by the design without additionalinformation.Despite the term self-explaining suggesting otherwise, behaviour associated with aspecific design or design element has to be learned in the first place. In caseunknown objects or new design elements are encountered, the behaviour elicited isdetermined by the degree of similarity to the original object. While some roads arehighly self-explaining, like motorways, rural roads seem to lack this quality.

Therefore, implementing self-explaining road design will lead to substantiate changesin the perceived characteristics of rural roads. Some of these characteristics will notbe self-explaining the first time they are encountered. In this case, the appropriatebehaviour has to be learned. The ways how this learning is done have to be known inorder to be successful. In general, the following four principles are applicable:

- Explicit or purposeful learning due to information and education.

- Observational learning in the sense of Bandura (for a summary on Bandura,see Gerrig & Zimbardo, 2005; Schlag, 2004).

- Contingency management; refers to the way feedback is given, both in timeand type (for a review see as well Schlag, 2004).

- Stimulus control (antecedent to behaviour).

Especially the last three principles will contribute to learning the appropriatebehaviour, concurrently. In contrast to the first one, no special effort is required fromroad authorities, except that the principles are applied consistently throughout thewhole road system.These principles were developed amongst others as consequence to human errorresearch in driving. Some conclusions, which were derived from this line of research,were published by Hale et al. already in the nineties (Hale, Stoop, & Hommels, 1990).Theeuwes & Godthelp (1995) and Theeuwes (2000) further elaborated theseprinciples. They were summed up by Theeuwes (2000, p. 21) as follows:


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- Roads should consist of unique road elements (homogeneous within onecategory and different from all other categories).

- Roads should require unique behavior for a specific category (homogeneouswithin one category and different from all other categories).

- Unique behavior displayed on roads should be linked to unique roadelements (e.g., woonerfs: obstaclesslow driving, freeway: smoothconcretefast driving).

- The layout of crossings, road sections, and curves should be linked uniquelywith the particular road category (e.g., a crossing on a highway shouldphysically and behaviorally be completely different from a crossing on a ruralroad).

- One should choose road categories that are behaviorally relevant.

- There should be no fast transitions going from one road category to the next.

- When there is a transition in road category, the change should be markedclearly (e.g., with rumble strips).

- When teaching the different road categories, one should not only teach thename of, but also the behavior required for, that type of road.

- Category-defining properties should be visible at night as well as in thedaytime.

- The road design should reduce speed differences and differences in directionof movement.

- Road elements, marking, and signing should fulfill the standard visibilitycriteria (Theeuwes, 2000, p. 21).


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2. Model development and theoretical validation

2.1. Overview

Based on report 8.1, a model of the regulation of driver and driving behaviour on ruralroads has been developed, assuming three main factors and an additional feedbackloop, which influence behaviour and thus safety:

- Part I: Affordances and cues are used as long as they are present and as longas they are known and perceived by the driver,

- Part II: Perceptual invariants are used for the short-term regulation of drivingbased on visual perception

- Part III: Expected and actual level of workload and risk are used in ahomeostatic process to regulate behaviour whenever the two othermechanisms are not sufficient and

- Part IV: Feedback.

Figure 9: Model of driving behavior on rural roads

Figure 9 gives an overview of these mechanisms and their relationship which will beexplained in the following in detail.

2.2. Processes within the model in detail

Readers who are not familiar with the preceding reports are advised to read thefollowing chapter in order to understand the ideas in the model that will be validated

in the course of this report.

2.2.1 Part I: Affordances and cues

The driver perceives the road and the road environment ahead and its inherentproperties. These properties convey a message to the driver that can be enough tobe effective in regulating driver and driving behaviour. In fact, this is the aim of self-explaining road design(see e.g. Theeuwes, 2000). The question is of course howroad and environmental properties regulate behaviour in detail. Environmentalproperties and suggested behaviour are associated through knowledge in thefurthermost sense. This knowledge is learnt and does not have to be explicit.Learningof the association between property and behaviour is achieved through amultitude of ways by (for summaries see e.g. Funke & Frensch, 2006; Koch, 2005;Schlag, 2004):


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- Classical conditioning,- instrumental learning through operant conditioning,- social learning,- implicit and- explicit (purposeful) learning.

Classical conditioning uses innate associations between a certain stimulus and asubsequent behaviour. When the stimulus is shown together with another stimulus(for some time and in a predefined way), this new stimulus will afterwards elicit thebehaviour without the original stimulus. Operant conditioning means that a positive ornegative consequence follows an act performed by a person before. The close andconsistent relationship between a certain antecedent (stimulus) and a certainbehaviour is called contiguity and the relationship between a behaviour displayedand a certain consequence is called contingency. Without contiguity the intendedbehaviour will not become associated with the stimulus. Without contingency thebehaviour will not be associated with its consequences (Schlag, 2004). Theseconsequences are rewards or punishments in the furthermost sense of the words.

Whereas punishment and reward in traffic safety could be monetary, they are usuallyconstantly given in the form of positive or negative feelings (feeling of safety/danger;feeling of comfort/discomfort; etc.) or to a far lesser extent conflicts and accidents.Social learning means that someone learns from watching someone else doingsomething and from the consequences of this behaviour. Implicit learning is difficultto define (for a summary see e.g. Frensch, 2006) but usually means that the fact thatsomething is learnt at all is not conscious. Implicit learning is the contrary to explicitlearning. Explicit learning is done whenever learning is done on purpose.The second question is how the properties of the environment elicit this knowledge.In psychology there are different theories on how this is done. Two concepts whichare useful in our context are the concept of affordances and the concept of cues. Theterm affordanceswas created by Gibson (1986) within his theory of direct perception(for a summary of Gibsons theory of affordances see e.g. Jones, 2003). According toGibson, objects have properties which become affordances in relation to theproperties of an individual (here: the driver). An affordance conveys a meaning to theobserver in the sense of being able, for example being climbable. Similarly, roadelements convey a meaning to the driver: the element is drive-able within a certainspeed and attention range. This is what we call the suggested range of speed andattention. However, the direct approach to perception is not the only possible wayhow to explain this range of possible behaviours. They can as well be explained bybehaviouristic theories that are to a large extent based on conditioned responses.

Here, characteristics of the road or environment serve as discriminative stimuli.These discriminative stimuli give a hint to the driver which consequences to expectwhen showing the respective behaviour. Knowledge and anticipation of theseconsequences will then result in the respective behaviour being shown in the case ofexpected positive consequences or not shown in the case of negative consequences(avoidance behaviour). A road sign, for example, can almost be called the archetypeof the discriminative stimulus (Fuller & Santos, 2002, p. 49). However, this does notmean that a certain behaviour is elicited automatically. There are somepredispositions for that: first of all, the sign (or any other discriminative stimulus) hasto be perceived, which might be impeded by different filters (see e.g. Rumar, 1985).Second, the wanted behaviour has to be associated with the respective

discriminative stimulus which is not necessarily the case. The reason for a lack ofassociation can be a lack of feedback or inconsistent or unreliable informationconveyed by the stimulus. The third prerequisite is that the driver perceives his


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behaviour to be under his own control, which is called self-efficacy. Taken all theseprerequisites together, it is to be preferred when the whole situation serves asintegrated discriminative stimulus (Fuller, 1984). In both cases (single elements orwhole situation), the appropriate behaviour can be associated so closely to thestimulus that the stimulus literally prompts or triggers the behaviour. In this case, the

stimulus is often termed cue and the terms stimulus - or bottom-up control ofbehaviour are used. In relation to attention, this kind of control is called automatic,exogenous control of behaviour, in contrast to intentional, endogenous control(Posner, 1980). Automatic, exogenous control of behaviour is faster, less resourceconsuming (lower workload) (Schneider, Dumais, & Shiffrin, 1984), often morereliable and gives less opportunities for individual differences. Thus, the guidanceimpact is higher and more reliable and it may be advantageous when fast andappropriate actions are required by almost all drivers at any time. While themechanisms of exogenous control are valuable for traffic safety, they can lead toerrors themselves (besides the ones already named above). This is the case whenthe wrong affordances or misleading cues are present in a situation which

subsequently automatically leads to inappropriate behaviours.Exogenous behavioural guidance can be explained and supported by cues andaffordances as well as by the appropriate use of perceptual invariants as described inthe next chapter.

2.2.2 Part II: Perceptual invariants

Finally, speed and path are regulated by perceptual invariants (Bruce et al., 1996).These perceptual invariants are Tau and Tau dot (Lee, 1976) and the deducedvariables TTC(Lee, 1976) and TLC(Godthelp et al., 1984; Van Winsum & Godthelp,1996). These perceptual invariants are used by the driver to remain within the

boundaries of the lane-tube (Summala, 1996). A simplified way how drivers use Tauand Tau dot to regulate distance and speed is shown in the following Figure 10.

Figure 10: Detailed processes within part III of the driver behaviour model for rural roads: Safe

distance keeping by using perceptual invariants proposed by Lee (1976) and Lee & Lishman

(1977), tested e.g. by Yilmaz and Warren (1995). Adapted from Bruce et al. (1996).


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In contrast to the two aforementioned mechanisms, perceptual invariants can becalculated without having to know the properties of the individual driver. Howeveruseful this might be, perceptual invariants are only used for the short-term regulationof behaviour and therefore are not enough to explain the entity of the complexbehaviour regulation on rural roads.

2.2.3 Part III: Expected and actual workload and risk

However, affordances or cues do not necessarily have to be present or they mightnot be known to the driver. In this case, the driver has to guess which behaviour isappropriate. This is done by comparing the expected level of workloadand riskwiththe preferred workload and risk level. Which of these two parameters is actually usedis topic of ongoing discussions. In literature evidence for both parameters is found(e.g. Fuller, 2005; Gerald J. S. Wilde, 1994; Gerald J. S. Wilde, 2001). In our studieswe found a very strong correlation between rated demand and rated risk whichmakes it likely that drivers do not really distinguish between those two variables (see

report 8.2 for details). Differences will be found however, when drivers are asked torate the objective risk of an accident (Fuller, 2005). In our research we found thatdrivers in this case take into account their assumptions of how other drivers willbehave, which will not influence their personal behaviour. Regardless of thediscussion concerning the relevance of the respective parameters, it is much moreimportant to understand how this process is done. Workload is the effect ofsituational demand on the driver, depending on the drivers resources. At this point itis important to notice that situational demand in driving depends on thecharacteristics of the road and the speed with which this road is driven. This meansthat workload will differ in the same situation for the same driver when this driver isforced

1to drive through the situation with different speed. Further, workload depends

on the capabilities or resources of the driver. These capabilities differ both betweendrivers but within drivers as well. They depend on the current motivation and thecurrent state of the driver as well as on longer-lasting traits and organic variables likeage or driving experience. When different drivers or the same driver at differentoccasions are forced to drive the same road with the same speed, they willexperience different levels of workload. Usually however, driving is a self-paced task,which means that the driver can choose his preferred speed. This is done in order toassure medium levels of workload and risk. In reality workload and risk oscillatearound this optimal level which is called homeostasis. This homeostatic regulation isdone pro-actively based on expectations concerning the road ahead and re-activelyas a result of feedback to the current workload and risk situation. In case of pro-active regulation the driver generates expectations concerning workload and riskahead of the current position. The entity of expectations concerning a situation formsa mental model (for a summary see Weller et al., 2006). To do so, the followingvariables are combined:

- the perceived road ahead with- the information from the road just passed and- the individual knowledge of how situations usually develop.

1The term forced is used here to indicate that the driver usually does not do this. However, it can be

achieved in experimental sessions or even when the situation does not have enough degrees of

freedom (e.g. due to other cars).


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These three input parameters can at the same time result in wrong assumptions. Thisis the case when:

- the road ahead is perceived as less demanding than it actually is, when- the road ahead (static and dynamic situation) differs fundamentally from the

road just passed (e.g. in the case of design inconsistencies) or- the individual knowledge is inappropriate.

The following Figure 11 sums up the processes described above.

Figure 11: Detailed processes within part II of the driver behaviour model for rural roads:

Expected and actual work load and risk .

2.2.4 Part IV: Feedback

Last not least, the selected behaviour itself (be it speed, path or attention) and itsconsequences, influence future behaviour through feedback. This actual behaviourchanges the actual experienced workload and risk and might thus influence the

future preferred level of workload and/or risk, as well as the expectations concerningfuture workload. The actual behaviour influences as well the perceived road aheadthrough a change in the perceptual invariants. Further, the experiences made with


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the current behaviour serve as knowledge-base for future situations and mighttherefore influence directly the perception of the road ahead (e.g. former neutralstimuli become cues through experience, see above). Of course, inappropriatebehaviour is enforced as well in case of missing or wrong (here: not negative)feedback. As is well-known from aggression research: if people know that they are

doing something wrong but no consequence follows, they perceive this lackingfeedback as positive reinforcement, strengthening the wrong behaviour.


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3. Empirical validation: Methodology

Whether the model developed above is supported by empirical data, was tested inseveral steps. The results are reported in detail in the internal report 8.2 (Weller &Schlag, 2007) and are summarized in the following sections by using prototypical

results.

3.1. Formulation of Hypotheses

The empirical validation of a model requires the formulation of consistent hypothesesthat can be falsified with empirical data. The model that has been developed aboveallows the formulation of such hypotheses and the subsequent testing with empiricaldata. The general research paradigm used is the comparison between two or moreroad elements that are similar concerning their geometry, but differ in their respectiveaccident rate or other parameters that constitute the independent variable in the

hypothesis. The dependant variables can be subjective ratings, as well as driver anddriving behaviour data. The formulation of hypotheses follows the three main parts ofthe model as described above.

3.1.1 Part I: Affordances and cues

Hypothesis 1: The presence, respective absence, of cues results in differencesconcerning both subjective ratings of demand and risk, as well as driving behaviour.The direction of change depends on the message the cue conveys. In the case ofwarning signs, the respective road element should be rated more dangerous anddemanding, while at the same time driving behaviour should be less risky (indicated

by e.g. lower speeds).

3.1.2 Part II: Perceptual invariants

Perceptual invariants are used for the regulation of driving behaviour on the controllevel. The fact that drivers use perceptual invariants when driving, has been shown inseveral publications and is widely accepted (see report 8.1., Weller et al., 2006).Thus, theoretical validation as above may be sufficient. Therefore, no hypothesesconcerning perceptual invariants were developed or will be tested in this project.

3.1.3 Part III: Expected and actual workload and risk

In this part of the model several assumptions are made. These assumptions concernboth the regulation of speed and the general level of safety and are as follows:

- Hypothesis 2: If expected geometry or situation do not match actual geometryor situation, the situation is unsafe.

- Hypothesis 3: If expected workload or risk is higher than preferred workload orrisk, speed will be reduced.

- Hypothesis 4: If actual workload or risk is higher than expected workload orrisk the situation is unsafe. Using the research paradigm formulated above,workload or risk should be comparable before the curve (expected WL or risk)but different in the curve (actual WL or risk), whereas it should be higher in the

high accident rate curve.


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3.2. Data sources for the testing of the hypotheses

In order to test the above hypothesis, different data sources and different methodswere used. These data sources and methods are summarized in the following matrixand are explained in more detail below. As we originally started our work with

available data, the order of the data sources in the matrix does not followchronological order but thematic considerations derived from the model (see lastcolumn in Table 1).

Table 1: Matrix of the data sources used and the hypotheses tested.

Datasource

Method: Used for testinghypothesis No.:

A.) Own simulator experiments Hypothesis 1

B.) Additional collection of data based on available data Hypothesis 2 & 3

C.) Reanalysis of available data Hypothesis 4D.) Own driving experiments with an equipped vehicle Hypothesis 4

The following sections briefly describe the data and the methods used.

3.2.1 Own simulator experiments (Data Source A)

Part I of the model assumes that cues and affordances play a central role ininfluencing driver and driving behaviour on rural roads. In order to determine theinfluence of cues and affordances, we conducted experiments in the simulator of theFraunhofer Institute for Transportation and Infrastructure Systems IVI in Dresden

2

(see Figure 12). Details concerning the road and experimental details can be found inthe corresponding chapter.

Figure 12: Simulator of the Fraunhofer IVI in Dresden that was used for the simulator

experiments (www.ivi.fhg.de). Source: Fraunhofer IVI.

2Further information concerning the simulator:

http://www.ivi.fraunhofer.de/frames/english/projects/eng_fahrsimulator_strasse.htm


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The advantage of simulator studies is that they allow the systematic variation ofindependent variables in a strict experimental laboratory setting. Due to the nature ofsimulator studies, these independent variables can as well constitute dangeroussituations that otherwise cannot be tested. These advantages are of course tradedfor a lower external validity when compared to driving experiments in the field, which

we conducted as well (see Data Source D).

3.2.2 Additional col lection of data related to available data (Data SourceB)

Prior to conducting own driving experiments, existing data was reanalysed withrespect to the needs of RiPCORD-iSEREST. This data was collected at TU Dresdenduring a project funded by DFG3 . The project was a joint project of the Chair ofWork- and Organizational Psychology (Prof. P. Richter) and the Chair of RoadPlanning (Prof. G. Weise). For this past project, 31 subjects drove 12 differentstretches of two-lane rural roads in the German federal state of Saxony. The length of

the road sections varied between two and seven kilometres, with the majority beingaround three kilometres in length. Some of the sections were driven again by thesame subjects (although only 21 in comparison to the original 31 due to experimentaldropout) after one year in order to ensure longitudinal stability. The roads were notaltered during this one year period. All drives were recorded on video (front view). Inaddition to speed, psycho-physiological data was recorded during the drives (seeData Source C). The videos from this study, together with the road geometry and theaccident data of these road sections, were used by the Chair of Traffic andTransportation Psychology at TUD to collect subjective ratings in a comparison ofhigh and low accident rate curves.

3.2.3 Reanalysis of available data (Data Source C)

The psycho-physiological data from the Richter et al. study described above (Richter,Wagner, Heger, & Weise, 1998; Richter, Weise, Wagner, & Heger, 1996) wasreanalysed with respect to the needs of RiPCORD-iSEREST. The following psycho-physiological data were recorded during the drives:

- electrocardial measures (ECG): heart rate (beat and beat-to-beat interval)- electrooculogram (EOG): blink rate- electrodermal measures (EDA): tonic skin conductance level (SCL) and single

phasic reactions.

The data of this study was re-analysed to answer questions that were not part of theoriginal data analysis. This reanalysis is described in detail in the respective chapterand report 8.2. (Weller & Schlag, 2007).

3DFG (Deutsche Forschungsgemeinschaft). Project name: Fahrverhalten und psychophysiologische

Aktivierung von Kraftfahrern als Bewertungskriterien der Gestaltungsgte von

Straenverkehrsanlagen (Driving behaviour and psychophysiological activation of car drivers as

assessment criteria for road design). Deutsche Forschungsgemeinschaft DFG Project Number Ri

671/2-1. Richter et al. (1996); Richter et al. (1998), (Heger & Weise, 1996), (Wagner, 2000).


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3.2.4 Own driv ing experiments with an equipped vehicle (Data Source D)

Driving experiments constitute the via regia when knowledge concerning realdriving behaviour is to be increased. At the same time, driving experiments are acomplex, demanding, and costly method. Nevertheless we decided to conduct

additional own driving experiments with an equipped vehicle. The following reasonsaffected this decision:- the shortage of appropriate, available existing data (see as well above),- the fact that the psycho-physiological data (data source C, see above) turned

out to be not as sensitive to our units of analysis (single road elements incontrast to whole road stretches) as expected,

- the nature of our hypotheses which are related to safety, operationalized as(the absence of) accidents.

In order to test Hypothesis 2 which is especially related to safety (see above),additional, new driving experiments were conducted at TUD with an equipped vehicle

of the Chair of Road Design at TUD (see Figure 13).

Figure 13: Experimental vehicle of the Chair of Road Design at TUD.

Besides parameters of driving behaviour like speed, driver behaviour was recordedwith the help of an integrated, contact-free eye tracker (Smart Eye). The test routeconsisted of around 2 * 40km of rural roads in the German Federal State of Saxony.Details concerning the test route, the sample and the parameters collected aredescribed in detail below.


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4. Empirical validation: Results

4.1. Hypothesis 1: Af fordances and cues (Data Source A).

4.1.1 Introduction

Part I of the model assumes that cues and affordances play a central role ininfluencing driver and driving behaviour on rural roads. Determining the validity of thisassumption and the extent to which it is valid was the aim

ripcord iserest deliverable d8 final

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