1. Is it time to revisit the problem young driver? Mrs Bridie Scott-Parker (PhD candidate-under-examination) 1

2. Overview The ‘young driver problem’ vs the ‘problem young driver’ Study aim Methodology Cluster analysis Implications Strengths and limitations Questions 2

3. 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 3

4. 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 4

5. 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) 5

6. 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) 6

7. 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....... 7

8. 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? 8

9. 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 9

10. 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 10 Methodology [2]

11. 11 Clusters – BYNDS Subscales

12. Clusters – P1 Personal Characteristics [1] Characteristic High Risk n = 49 Medium Risk n = 163 Low Risk n = 166 p 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 owner85.7%81.6%76.5%=.29 Reside in urban area65.3%66.7%57.0%=.17 12

13. Clusters – P1 Personal Characteristics [2] Characteristic M (SD) High Risk n = 49 Medium Risk n = 163 Low Risk n = 166 p Anxiety8.4 (2.8)7.1 (2.6)6.5 (2.5)<.001 Depression12.8 (5.0)10.2 (4.2)9.8 (4.2)<.001 Reward sensitivity5.3 (2.6)3.9 (2.2)2.4 (2.0)<.001 Sensation seeking25.1 (6.3)23.5 (6.1)19.4 (5.9)<.001 13

14. Clusters – Pre-Licence and Learner Characteristics Characteristic High Risk n = 49 Medium Risk n = 163 Low Risk n = 166 p Pre-Licence driving22.4%13.5%8.4%<.05 Inaccurate logbook36.7%20.9%9.0%<.001 Unsupervised driving18.4%14.1%6.0%<.05 Crashed car10.2%1.8%3.0%<.05 Offence detected2.0%3.7%1.8%=.55 14

15. 15 Learner BYNDS Subscales

16. Clusters – P1 Behaviours [1] Characteristic High Risk n = 49 Medium Risk n = 163 Low Risk n = 166 p Crashed car26.5%11.1%3.0%<.001 Offence detected28.6%12.9%5.4%<.001 Talked self out of ticket16.3%2.5%1.8%<.001 16

17. 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 17 Clusters – P1 Characteristics [1]

18. Clusters – P1 Characteristics [2] Characteristic M (SD) High Risk n = 49 Medium Risk n = 163 Low Risk n = 166 p 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 18

19. Clusters – P1 Characteristics [3] Characteristic M (SD) High Risk n = 49 Medium Risk n = 163 Low Risk n = 166 p 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 19

20. 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? 20

21. 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? 21 Implications [2]

22. 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 22 Implications [3]

23. 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) 23 Implications [4]

24. 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 24

25. 25 Questions? Contact Details: Bridie Scott-Parker PhD Candidate-under-examination Email: b.scott-parker@qut.edu.aub.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