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

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
Figure 1: Breakdown of offenders
(n = 84,468)
High-range
3.7%
Mid-range
90.5%
Low-range
5.8%
Figure 2: Gender of offenders
90.2%
65.1%
50.5%
49.5%
Male
34.9%
Female
9.8%
Low-range
Mid-range
High-range
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 3: Age of offenders
40.5%
17.2%
9.4%
Low-range
Mid-range
High-range
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
17 - 24
25 - 29
30 - 39
40 - 49
50 - 59
60+
Figure 4: Offenders’ licence status
91.7%
86.5%
64.8%
Learner
29.1%
Provisional
3.4% 4.9%
Low-range
4.1% 9.4%
Mid-range
6.1%
Open
High-range
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 5: Offenders’ licence class
70.4%
64.8%
54.6%
Car only
38.5%
Motorcycle
18.5%
24.1%
HV only
Car + HV
Low-range
Mid-range
High-range
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 6: Drink driving offence history
98.6%
95.7%
88.6%
Yes
No
11.4%
1.4%
4.3%
Low-range
Mid-range
High-range
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 7: Unlicensed driving offence
history
100.0%
98.6%
91.7%
Yes
No
8.3%
0.0%
1.4%
Low-range
Mid-range
High-range
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 8: Seat belt offence history
100.0%
96.6%
91.0%
Yes
No
0.0%
3.4%
Low-range
Mid-range
9.0%
High-range
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 9: Other offence history
100.0%
86.4%
63.5%
36.5%
Yes
No
13.6%
0.0%
Low-range
Mid-range
High-range
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
Figure10: Crash history
97%
100
90
80
70
60
50
40
30
20
10
0
93.7%
86%
Crash
No Crash
14%
3%
Low range
6.3%
Mid range
High range
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
100
90
80
70
60
50
40
30
20
10
0
90.5%
91.7%
90.5%
Car
Motorcycle
Heavy vehicle
1.5% 8%
Low range
4% 5.5%
Mid range
6%
2.3%
High range
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
64.4%
70
52.7%
47.3%
60
50
52.5%
47.5%
35.6%
40
Most at fault
Not most at fault
30
20
10
0
Low range
Mid range
High range
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 highrange 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, highrange 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 midrange 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
Questions?
[email protected]
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