The crash and offence involvement of speeding offenders Barry Watson Presentation to “Under the...

The crash and offence involvement of speeding offenders

Barry Watson

Presentation to “Under the Radar” Traffic Offenders Conference

7 December 2011

CRICOS No. 00213J

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. 00213J

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

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’– Low-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-range5.8%

Mid-range90.5%

High-range3.7%

Figure 1: Breakdown of offenders(n = 84,468)

Low-range Mid-range High-range

50.5%

65.1%

90.2%

49.5%

34.9%

9.8%

Male

Female

Low-range vs. high-range: 2 (1) = 1333.7, p < .001, c= .41Mid-range vs. high-range: 2 (1) = 840.4, p < .001, c= .10

Figure 2: Gender of offenders


9.4%17.2%

40.5% 17 - 2425 - 2930 - 3940 - 4950 - 5960+


Figure 3: Age of offenders


3.4% 4.1% 6.1%4.9% 9.4%

29.1%

91.7%86.5%

64.8%

Learner

Provisional

Open


Figure 4: Offenders’ licence status


70.4%64.8%

54.6%

18.5% 24.1%

38.5% Car only

Motorcycle

HV only

Car + HV


Figure 5: Offenders’ licence class


1.4% 4.3%11.4%

98.6% 95.7%88.6%

Yes No


Figure 6: Drink driving offence history


0.0% 1.4%8.3%

100.0% 98.6%91.7%

Yes No


Figure 7: Unlicensed driving offence history


0.0% 3.4%9.0%

100.0% 96.6%91.0%

Yes No


Figure 8: Seat belt offence history


0.0%

13.6%

36.5%

100.0%

86.4%

63.5%

Yes No


Figure 9: Other offence history

Figure10: Crash history

Low range Mid range High range0

10

20

30

40

50

60

70

80

90

100

3% 6.3%14%

97% 93.7%

86%

CrashNo Crash


Figure11: Vehicle type in crashes


10

20

30

40

50

60

70

80

90

100 90.5% 90.5% 91.7%

1.5% 4% 6%8% 5.5%2.3%

CarMotorcycleHeavy vehicle

Low-range vs. high-range: 2 (1) = 13.7, p < .001, c= .16Mid-range vs. high-range: 2 (1) = 11.8, p = .003, c= .05

Figure13: Most at fault in crashes


10

20

30

40

50

60

70

35.6%

47.3%

52.5%

64.4%

52.7%47.5%

Most at faultNot most at fault

Low-range vs. high-range: 2 (1) = 8.9, p = .003, c= .15Mid-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


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


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

Mark your Diaries!

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

25-28 August 2013, Brisbane

[email protected]

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