Insert the title of your presentation herePresented by Name HereJob Title - Date

Feeling of risk as a determinant of

driving behaviour – a simulator study

Britta Lang (TRL), Andrew Parkes (TRL), Michael Gormley (TCD)

21st August 2013, IDBTC, Helsinki

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Overview

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3

Results4

Summary & discussion5

A small history of risk1

Simulator study on risk perception - method

Fuller’s Task Capability Model

A small history of risk

Understanding risk has profound implications for development of effective behaviour change interventions

Dominance of models on individual differences for accident prediction in 1960/70

Focus on upper performance limits of the drivers & stabile ‘traits’ that affect accident risk

Empirical studies show significant associations between personality, perception & cognition characteristics & accidents

However, the proportion of variance explained remains small

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The eighties - the decade of motivational models

Comprehensive models of driver behaviour replace models of accident prediction

Shift is triggered by studies that indicate that motivation influences what drivers perceive & remember (Peltzmann, 1975); situational & motivational factors now assumed as central determinants of driving behaviour

Taylor (1964) posits that drivers create & maintain the desired level of tension/anxiety; EDA tracks the distribution of accidents

Driving now described as a “self-paced task” in which the driver creates the demands of the driving task in interaction with a dynamic road environment

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Motivational models

Examples include:

- Risk-homeostasis model (Wilde, 1982, 1988)

- Zero-Risk Model (Näätänen & Summala, 1974, 1988; Summala, 1988, 1996, 2000)

- Threat-Avoidance Model (Fuller, 1984, 1988)

- Task-Capability Interface Model & Risk-Allostasis (Fuller, 1984; 2000; 2008)

The current debate & experimental work centres on the Task Capability Interface Model (Fuller) & the Zero-Risk Model (Näätänen & Summala), including:- Lewis-Evans & Rothengatter (2009)

- Fuller, McHugh, & Pender (2008)

- Kinnear, Stradling & McVey (2008)

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Fuller’s Task Capability Model

Nobody sets out on a journey to have an accident; until a certain threshold has been surpassed, the statistical risk of being involved in a crash does not enter our minds when driving

What does? According to Fuller it is the subjective risk, the perceived difficulty of the driving task, which manifests as a feeling of risk

The physiological correlate of feeling of risk is arousal, measurable in electro-dermal activity

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The Task Capability Model (Fuller, 2000)

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Capability Task demand

Control

Collision

D > C

C > D

Task difficulty

The Task Capability Model (Fuller, 2000)

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Capability Task demand

Control

Collision

D > C

C > D

Task difficulty

Constitutional features

TrainingEducation Experience

Competence

Human FactorsEnvironment

Other road users

Vehicle

Speed

Road position & trajectory

“Inevitable” age-related deteriorations

Cognitive

- Deceleration of information processing, deterioration of working memory, selective/ divided attention

Perceptual

- Reduced visual & aural acuity, sensitivity to glare

Physical

- Restricted mobility & joint movements (particularly head & neck), reduction of (grip) strength

- Higher need for recovery from physical demands

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This would suggest a reduction in driver capability according to the model & increased ratings of task difficulty & feeling of risk

Research hypotheses

1. Task difficulty & feeling of risk ratings will be significantly associated with speed & with each other. Perceived crash will correlate with feeling of risk only after a certain difficulty threshold has been exceeded.

2. Ratings of task difficulty & feeling of risk will significant increase with driver age

3. Older drivers will adopt lower preferred & maximum driving speeds, reflecting age-related capability decline

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The simulator study

Simulator study at TRL with 30 participants (young, middle-aged, old)

Questionnaire on driving habits & experience

In Part I completion of 12 drives at pre-set speed:- in 2 road environments : urban & dual carriageway

- at 3 speeds (slow, average, fast)

- with other road users present/not present

- Speed estimates & ratings of each drive for:- Feeling of risk: on 7-point Likert scales

- Perceived difficulty: on 7-point Likert scales

- Perceived risk of a crash: as a percentage

In Part II, completion of 8 drives at own speed in:- 2 environments : urban & motorway

- either as fast as possible or at preferred speed

- Speed estimates

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The TRL DigiCar

Honda Civic surrounded by four 3×4 metre projection screens giving 210º front vision & 60º rear vision

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Hypothesis 1: Task difficulty, feeling of risk & crash likelihood in fixed speed condition

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Urban environment, fixed speed Dual carriageway, fixed speed

Hypothesis 1: Association between task difficulty, feeling of risk & crash likelihood

Pearson Product Moment Correlations

Task difficulty & feeling of risk

Low Average High

Urban .86** .82** .81**

Dual carriageway .74** .82** .87**

Feeling of risk & crash likelihood

Urban .59** .70** .45**

Dual carriageway .14 .11 .34**

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** p< .01

Hypothesis 1: Crash likelihood

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Percentage of participants who rate crash likelihood > 0

Hypothesis 1: Speed as a predictor of task difficulty

Urban roads Dual carriageway

Speed r2 Beta t r2 Beta t

Task difficulty 0.37*** 0.61 10.67 0.20*** 0.55 6.68

Feeling of risk 0.42*** 0.65 11.37 0.25*** 0.50 7.78

Crash risk 0.12*** 0.35 4.90 0.06** 0.23 3.21

Other road users r2 Beta t r2 Beta t

Task difficulty n.s. 0.04** 0.20 3.15

Feeling of risk n.s. 0.03** 0.19 2.92

Crash risk n.s. n.s.

Age r2 Beta t r2 Beta t

Task difficulty 0.06*** 0.25 4.3 n.s.

Feeling of risk 0.06*** 0.24 4.45 n.s.

Crash risk 0.07*** 0.27 3.94 n.s.

*** p< .001

Summary Hypothesis 1

Feeling of task difficulty & risk are highly correlated as predicted by the model

Contrary to expectation crash likelihood is significantly associated with task difficulty in the urban environment even in the low speed condition & significant proportion of respondents estimate it to be >0 even in the low speed condition in both environments

Speed explains a significant proportion of the variance in task difficulty. However, presence of other road users can have sg. impact (albeit small), if it has potential impact on driver actions

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Hypothesis 2: Age effects for task difficulty & feeling of risk

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Main effect age: (F(2, 27)= 4.75, p<.05, partial η2=.26)

Main effect age: (F(2, 27)= 3.45, p<.05, partial η2=.20)

Task difficulty, urban environment Feeling of risk, urban environment

Hypothesis 2: Age effects for crash likelihood

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Interaction age x speed: (F(4, 27)= 2.9, p<.05, partial η2=.18)

Crash likelihood, urban environment

Summary Hypothesis 2

Age effects between older are only prevalent in the urban road environment, not on the dual carriageway potentially due to the dual carriageway being less demanding

Age effects identified are in the expected direction, with older drivers rating task difficulty, feeling of risk and crash likelihood significantly higher than young drivers do

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Hypothesis 3: Adopted speeds in free-drive condition

Speed F(1, 24)= 72.71, p>.001, η2=.75Age F(2, 24)= 13.07, p>.001, η2=.53Speed * Age F(2, 24)= 4.61, p<.05, η2=.26

Urban environment

Hypothesis 3: Adopted speeds in free-drive condition

Speed F(1, 24)= 62.77, p>.001, η2=.72Risk F(1, 24)= 34.17, p>.001, η2=.59Risk * Age F(2, 24)= 3.97, p<.05, η2=.25

Dual carriageway

Summary Hypothesis 3

Young drivers in urban environments adopt sg. higher speeds (preferred & maximum) than middle aged & older drivers

On dual carriageways, young drivers adopt sg. Higher speeds (preferred & maximum) than middle aged & older drivers; older drivers adopt the lowest speeds if other road users are present

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Study limitations

Study limited to subjective ratings of risk; need to include physiological measures in follow-on study

Study did not include an objective measure of driver capability; therefore observed age-effects cannot be attributed to reductions in capability

Similar to Fuller’s work the study focused very much on speed as the main manipulator of task difficulty & experimental findings may reflect this; exploration of other sources of risk is needed

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Conclusions

Older drivers appear to be more risk sensitive & have lower preferred task difficulties

It remains unclear at this stage whether this is attributable to an age-related reduction in capability or the expression of a greater desire for driving “comfort”

Fuller’s model, while intuitive, is regrettably unspecific regarding the interplay of age & experience & the prediction of the impact of age

His notion of driving as a self-paced task is likely to not apply in situations that have been highlighted as particularly difficult for older drivers (junctions)

The empirical work in relation to the model has focused on an incremental increase of task difficulty; how does this relate to hazards

Estimates of crash likelihood appear to be fraught with difficulty; whilst people readily produce them, they don’t appear to be intuitive statisticians

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Questions?

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Thank you

Britta LangPrincipal Psychologist – 21/08/2013

Tel: 01344 770024Email: blang@trl.co.uk