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

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

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 J

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 J

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)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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