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Is it time to revisit the problem young driver?

Is it time to revisit the problem young driver? . Mrs Bridie Scott-Parker (PhD candidate-under-examination) . Overview . The ‘ young driver problem ’ vs the ‘ problem young driver ’ Study aim Methodology Cluster analysis Implications Strengths and limitations Questions.

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Is it time to revisit the problem young driver?

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

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

  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

  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

  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)

  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)

  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.......

  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?

  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

  10. Methodology [2] • 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

  11. Clusters – BYNDS Subscales

  12. Clusters – P1 Personal Characteristics [1]

  13. Clusters – P1 Personal Characteristics [2]

  14. Clusters – Pre-Licence and Learner Characteristics

  15. LearnerBYNDS Subscales

  16. Clusters – P1 Behaviours [1]

  17. Clusters – P1 Characteristics [1] • 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 Fixedviolations and Risky exposure, and decrease in Misjudgement and Driving in response to mood

  18. Clusters – P1 Characteristics [2]

  19. Clusters – P1 Characteristics [3]

  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?

  21. Implications [2] • 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?

  22. Implications [3] • 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

  23. Implications [4] • 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)

  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

  25. 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

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