1. The crash and offence involvement of speeding offenders Barry Watson Presentation to “Under the Radar” Traffic Offenders Conference 7 December 2011 CRICOS No. 00213J

2. Acknowledgements ARC Linkage project partners: –Queensland Department of Transport & Main Roads –Queensland Police Service –Office of Economic & Statistical Research CARRS-Q research team: –Adjunct Professor Vic Siskind –Dr Judy Fleiter –Angela Watson –David Soole

3. Overview  The role of speeding in crashes and contributing factors to the behaviour  The need to better understand speeding offenders  Characteristics of low-range, mid-range and high-range offenders  Links to other offending behaviour  Implications for speed management policies and practices CRICOS No. 00213J

4. The speeding problem in Australia  As in other countries, speeding is a major factor contributing to road crashes in Australia  Speeding is estimated to contribute to approximately 25% of all fatalities Australia-wide  Research indicates that speeding increases both the incidence and severity of crashes  Speeding is over-represented in: −more severe crashes −crashes involving high-risk groups such as young drivers, motorcycle riders, unlicensed drivers CRICOS No. 00213J

5. Speed management in Australia  Over the last 20 years, Australian jurisdictions have adopted a ‘holistic’ approach to reducing speeding involving: –Road environment improvements (e.g. lower urban speed limits, road treatments) –Enforcement programs (e.g. traffic patrols, fixed & mobile speed cameras, point-to-point cameras) –Education programs (e.g. mass media education) –Intelligent Transport System (ITS) measures (e.g. vehicle activated and variable message signs)

6. Speeding offenders  Historically, speeding drivers have been considered a homogenous group  In comparison to drink driving, there has been little research focus on: –identifying the characteristics of high-range or recidivist speeding offenders –better understanding the motivations of these drivers –tailoring countermeasures to address this group

7. Recidivist drink drivers (1)  International concern about recidivist drink drivers  Strong relationship between repeat offending and high-range BACs  Not a homogenous group, but are more likely that general drivers to: –consume greater amounts of alcohol, experience alcohol-related problems and be alcohol-dependent –exhibit antisocial and deviant tendencies, aggression, hostility, thrill-seeking –to have poor driving histories, to use drugs and a have criminal history

8. Recidivist drink drivers (2)  These findings are consistent with the road safety maxim that: “people drive as they live”  Recidivist drink drivers appear resistant to traditional drink driving countermeasures  This has prompted the development of tailored countermeasures and sanctions such as:  Heavy fines and lengthy suspension periods  Rehabilitation programs  Alcohol ignition interlocks  Vehicle immobilisation, impoundment or forfeiture

9. Aim of the study  To inform the design and implementation of speeding countermeasures by: –examining the demographic characteristics and traffic histories of speeding offenders –comparing the crash and offence histories of low and mid-range offenders with high-range speeding offenders –exploring potential predictors of high-range speeding offenders

10. Method (1)  The data was drawn from a larger study designed to evaluate the impact of speeding penalty changes  Traffic offence data from 1996 to 2007 was obtained for two cohorts of drivers: those convicted of speeding in May 2001 and May 2003  Data obtained included details of: –index offence –previous and subsequent traffic offences –demographic characteristics –licence type and class

11. Method (2)  Cases that were excluded from the analyses included: –Offenders not holding a Queensland licence, since demographic and offence history data was missing –Offenders with missing licence information (3.7%) –Speed camera offences not attributed to individuals, but companies  There were no statistical differences between the two cohorts of offenders on key variables, so they were combined

12. Method (3)  Three classifications of offenders were determined ‘a priori’ –L ow-range: one offence less than 15km/hr over speed limit during study timeframe –Mid-range: at least one offence more than 15km/hr over the speed limit –High-range: 2 or more offences, with at least two being 30 km/hr or more over the speed limit  Due to the large sample size a more stringent alpha rate of.001 was selected and effect sizes examined

14. Low-range vs. high-range:  2 (1) = 1333.7, p <.001,  c =.41 Mid-range vs. high-range:  2 (1) = 840.4, p <.001,  c =.10 Figure 2: Gender of offenders

15. Low-range vs. high-range:  2 (6) = 2166.9, p <.001,  c =.35 Mid-range vs. high-range:  2 (6) = 1721.1, p <.001,  c =.10 Figure 3: Age of offenders

16. Low-range vs. high-range:  2 (2) = 980.2, p <.001,  c =.35 Mid-range vs. high-range:  2 (2) = 1334.2, p <.001,  c =.13 Figure 4: Offenders’ licence status

17. Low-range vs. high-range:  2 (3) = 430.7, p <.001,  c =.23 Mid-range vs. high-range:  2 (3) = 364.2, p <.001,  c =.07 Figure 5: Offenders’ licence class

18. Low-range vs. high-range:  2 (1) = 376.9, p <.001,  c =.22 Mid-range vs. high-range:  2 (1) = 346.3, p <.001,  c =.07 Figure 6: Drink driving offence history

19. Low-range vs. high-range:  2 (1) = 417.8, p <.001,  c =.23 Mid-range vs. high-range:  2 (1) = 876.3, p <.001,  c =.11 Figure 7: Unlicensed driving offence history

20. Low-range vs. high-range:  2 (1) = 454.8, p <.001,  c =.51 Mid-range vs. high-range:  2 (1) = 271.8, p <.001,  c =.06 Figure 8: Seat belt offence history

21. Low-range vs. high-range:  2 (1) = 2082.9, p <.001,  c =.51 Mid-range vs. high-range:  2 (1) = 1265.8, p <.001,  c =.13 Figure 9: Other offence history

22. Figure10: Crash history Low-range vs. high-range:  2 (1) = 358.6, p <.001,  c =.21 Mid-range vs. high-range:  2 (1) = 286.2, p <.001,  c =.06

23. Figure11: Vehicle type in crashes Low-range vs. high-range:  2 (1) = 13.7, p <.001,  c =.16 Mid-range vs. high-range:  2 (1) = 11.8, p =.003,  c =.05

24. Figure13: Most at fault in crashes Low-range vs. high-range:  2 (1) = 8.9, p =.003,  c =.15 Mid-range vs. high-range:  2 (1) = 3.0, p =.081,  c =.03

25. Limitations  Relied on data collected for administrative purposes that can be incorrectly recorded or incomplete  The criteria for determining low, mid and high- range offending was somewhat arbitrary  Different classification of offenders may produce a different pattern of results

26. Implications for road safety (1)  Repeat, high-range speeding offenders are more likely to be male, younger, provisional licence holders and motorcycle riders  There is an association between repeat, high- range speeding and an increased involvement in crashes and other offences  Repeat, high-range speeding offenders appear to be a particularly problematic group of drivers  Mid-range speeding offenders also have an elevated involvement in offences and crashes

27. Implications for road safety (2)  Need to refine existing speed management strategies and consider tailored sanctions for repeat, high-range speeding offenders: −vehicle impoundment −intelligent speed adaption (ISA) −ongoing enhancement of rehabilitation programs  The effectiveness of increased fines for repeat, high-range offenders remains unclear  Additional sanctions may also be warranted for mid-range offenders

28. Implications for road safety (3)  Further research is required into: –the impact of current speed enforcement practices and sanctions on the behaviour of mid- range and high-range offenders –strategies to enhance the detection of speeding offenders (eg. point-to-point speed enforcement) –the psychological and social factors contributing to speeding recidivism to inform public education and offender management programs

29. Mark your Diaries! International Council on Alcohol, Drugs and Traffic Safety Conference (T2013) 25-28 August 2013, Brisbane Questions? b.watson@qut.edu.au