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Is it time to revisit the problem young driver?

Mrs Bridie Scott-Parker

(PhD candidate-under-examination)

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Overview

• The ‘young driver problem’ vs the ‘problem young driver’

• Study aim• Methodology • Cluster analysis• Implications• Strengths and limitations • Questions

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The ‘young driver problem’ vs the ‘problem young driver’ [1]

• Two conceptualisations of young drivers– The ‘young driver problem’: All young novice

drivers are at elevated crash risk• Age, inexperience • Australia, 2011: 17-24 year-olds comprised 12% of

the population but contributed 23% of driver fatalities

– The ‘problem young driver’: A subsample of young novice drivers is at greater risk

• Driving behaviour • 15.3% of young offenders in Queensland in 2009

had two or more prior offence convictions

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The ‘young driver problem’ vs the ‘problem young driver’ [2]

• Concepts have influenced government policy, research directions, and interventions– The ‘young driver problem’: Interventions

such as graduated driver licensing • Sound evidence base supporting effectiveness of

this broad countermeasure

– The ‘problem young driver’: How do we identify them?

• Operational definition? False positives

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Identifying Problem Young Drivers [1]

• Personal traits– Eg, Sensation seeking propensity, aggression,

anxiety, normlessness; driving-related aggression

• Five clusters: Drivers in two high risk clusters reported more risky driving behaviours and greater crash-involvement (Ulleberg, 2002)

• Four clusters: Drivers in high risk cluster reported more offences and greater crash-involvement (Wundersitz, 2007)

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Identifying Problem Young Drivers [2]

• Driving behaviours– Eg, Speeding, no seatbelt, driving tired

• Three clusters: 7% of sample were high risk drivers (77% male) who had significantly greater crash-involvement and more speeding violations (Vassallo et al, 2008)

• Preferred driving style– Eg, Multi-Dimensional Driving Style Inventory

• Three styles: Males scored more highly on reckless style, females on anxious and patient/careful styles (Kleisen, 2011)

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Addressing the Young Driver Problem

• Graduated driver licensing (GDL)– Queensland’s GDL program was

considerably-enhanced in July 2007• Learner period: Longer duration, younger age,

logbook, mobile phone restrictions• Provisional period: Two levels, passenger/ vehicle/

mobile phone restrictions, Hazard Perception Test

– Most restrictive programs greatest benefits– BUT.......

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Study Aim

• Young drivers continue to be overrepresented in road crash statistics

• Suggests targeted interventions may be required to improve young driver road safety

• How can we identify problem young drivers?– Personal characteristics?– Attitudes?– Driving behaviours?

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Methodology [1]

• Longitudinal research (online surveys)– Survey One

• 1170 Queensland drivers aged 17-25 years (60% female) who had just progressed from a Learner to a Provisional 1 (P1) driver’s licence

• Explored pre-Licence and Learner experiences

– Survey Two• Six months later, 378 participants (70% female)

completed second survey • Explored first six months of independent driving• Research utilised responses of these participants

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• Cluster analysis based on P1 self-reported driving behaviours (Behaviour of Young Novice Drivers Scale [BYNDS] subscales)– Two-step clustering using Euclidean distance

and Schwartz’s Bayesian Criterion • Designed to minimise within-cluster variance and

to maximise between-cluster variance

• Personal and driving characteristics then examined across the clusters

Methodology [2]

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High risk Medium risk Low risk0

5

10

15

20

25

30

35

40

Transient ViolationsFixed ViolationsMisjudgementRisky ExposureDriver Mood

Cluster

BY

ND

S S

core

Clusters – BYNDS Subscales

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Clusters – P1 Personal Characteristics [1]

CharacteristicHigh Risk n = 49

Medium Risk

n = 163

Low Risk

n = 166p

Gender (Male) 34.7% 29.4% 28.9% = .73

Age (Years, M, (SD)) 17.5 (1.1) 17.8 (1.4) 18.1 (1.6) < .05

Studying (Full-time) 49.0% 51.5% 50.6% =.50

Employed (Full-time) 26.5% 14.7% 13.3% < .01

Car owner 85.7% 81.6% 76.5% =.29

Reside in urban area 65.3% 66.7% 57.0% =.17

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Clusters – P1 Personal Characteristics [2]

Characteristic M (SD)

HighRisk

n = 49

Medium Risk

n = 163

Low Risk

n = 166p

Anxiety 8.4 (2.8) 7.1 (2.6) 6.5 (2.5) < .001

Depression 12.8 (5.0) 10.2 (4.2) 9.8 (4.2) < .001

Reward sensitivity 5.3 (2.6) 3.9 (2.2) 2.4 (2.0) < .001

Sensation seeking 25.1 (6.3) 23.5 (6.1) 19.4 (5.9) < .001

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Clusters – Pre-Licence and Learner Characteristics

CharacteristicHigh Risk n = 49

Medium Risk

n = 163

Low Risk

n = 166p

Pre-Licence driving 22.4% 13.5% 8.4% < .05

Inaccurate logbook 36.7% 20.9% 9.0% < .001

Unsupervised driving 18.4% 14.1% 6.0% < .05

Crashed car 10.2% 1.8% 3.0% < .05

Offence detected 2.0% 3.7% 1.8% = .55

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High risk Medium risk Low risk0

5

10

15

20

25

30 Transient ViolationsFixed ViolationsMisjudgementRisky ExposureDriver Mood

Cluster

BY

ND

S S

core

Learner BYNDS Subscales

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Clusters – P1 Behaviours [1]

CharacteristicHigh Risk n = 49

Medium Risk

n = 163

Low Risk

n = 166p

Crashed car 26.5% 11.1% 3.0% < .001

Offence detected 28.6% 12.9% 5.4% < .001

Talked self out of ticket 16.3% 2.5% 1.8% < .001

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• Changes in driver behaviour over time– High risk cluster

• Significant increase in all BYNDS subscale scores apart from Misjudgement (stable)

– Medium risk cluster • Significant increase in all BYNDS scores apart from

Fixed violations (stable) and Misjudgement (decrease)

– Low risk cluster• Stable Transient and Fixed violations and Risky

exposure, and decrease in Misjudgement and Driving in response to mood

Clusters – P1 Characteristics [1]

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Clusters – P1 Characteristics [2]

Characteristic M (SD)

High Risk n = 49

Medium Risk

n = 163

Low Risk

n = 166p

Dangerousness of ‘bending’ road rules(dangerous not dangerous)

2.4 (1.1) 2.0 (1.0) 1.6 (0.8) < .001

‘Risky driver’ (not risky risky) 3.8 (1.4) 2.4 (1.1) 1.9 (1.0) < .001

‘Safe driver’ (not safe safe) 4.2 (1.4) 5.0 (1.3) 5.4 (1.2) < .001

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Clusters – P1 Characteristics [3]

Characteristic M (SD)

High Risk n = 49

Medium Risk

n = 163

Low Risk

n = 166p

Likelihood of ‘bend’ (not likely likely)

4.7 (1.5) 3.6 (1.8) 2.3 (1.4) < .001

Intentions to ‘bend’ (no intention intention)

4.1 (1.6) 3.1 (1.6) 1.8 (1.2) < .001

Willingness to speed(not willing willing)

9.9 (4.2) 6.8 (3.6) 4.9 (3.8) < .001

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Implications [1]

• As Learners, more drivers in the high risk cluster reported – Pre-Licence driving– Unsupervised driving– Inaccurate logbooks– Crash-involvement

• Potential early indicators?• Targeted interventions needed during Learner

period?

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• High risk cluster drivers reported significant increase in self-reported risky driving over time from Learner to independent P1 driving

• As P1 drivers, the high risk cluster drivers reported greater offence and crash-involvement– Reliance on crashes (multitude of contributors) and

offences (enforcement constraints) is problematic BUT

– Negative outcomes appear to be a good indicator of a potential problem young driver

• Targeted interventions needed during the earliest phase of independent driving?

Implications [2]

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• Once identified, what do we do with problem young drivers?– GDL not reaching them?– GDL reaching but not having desired effect?– They know they are risky so education

unlikely to be successful– Likely a range of interventions needed

Implications [3]

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• Brief interventions (sensation seeking/speeding)– Psychosocial (anxiety, depression)– Resilience (resist impulses/peer pressure)

• In-vehicle technology (intelligent speed adaptation, alcohol ignition interlocks)

• Greater parental involvement/monitoring (pre-Licence, unsupervised driving, risky P1 driving)– Active supervision (Learner non-compliance)– Sharing of family vehicle

• Exposure reduction measures (reduce rewards/ sensation seeking opportunities)

Implications [4]

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Strengths and Limitations• Self-report data

– Difficult to investigate any other way

• Low response rate, high attrition – Despite incentives – Flooding during longitudinal second-wave

• Greater participation of females – No significant difference in gender across clusters

• Generalisability of findings – Young novices with 6 months driving experience – Longitudinal research participants’ reflected

Queensland’s ARIA profile

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Questions?

Contact Details: Bridie Scott-Parker

PhD Candidate-under-examination

Email: b.scott-parker@qut.edu.au

Acknowledgements: Supervisory team (Prof Barry Watson,

Dr Mark King, Dr Melissa Hyde)

Mark your Diaries!

International Council on Alcohol, Drugs and Traffic Safety Conference (T2013)

25-28 August 2013, Brisbane