Upload
lisa-nutting
View
217
Download
0
Tags:
Embed Size (px)
Citation preview
Probabilistic Models of Motorcyclists' Injury Severities in Single- and Multi-vehicle Crashes
Peter T. Savolainen, Ph.D.Wayne State University
Fred Mannering, Ph.D.Purdue University
Overview
Background Research Objectives Methodology Multi-Vehicle Crash Severity Model Single-Vehicle Crash Severity Model Conclusions
Background
Motorcycle Crashes by Year
2,000
2,500
3,000
3,500
4,000
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995
Year
Crash
es
Motorcycle Fatalities by Year
40
60
80
100
120
140
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995
Year
Fatalities
Background
Motorcycle Fatalities by Year
0
20
40
60
80
100
120
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Year
Fata
liti
es
Motorcycle Crashes by Year
1,5001,700
1,9002,1002,3002,500
2,7002,900
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Year
Crash
es
Background
Ridership increasing Riding population
changing Training Age Gender
Bike design Speed Power Safety
Repealed helmet laws
Motorcycle Registrations per Year
80,000
90,000
100,000
110,000
120,000
130,000
140,000
150,000
1995 1997 1999 2001 2003 2005
Year
Re
gis
tra
tio
ns
Research Objective
To develop probabilistic models of motorcycle crash-injury severity using Indiana crash data from 2003 to 2005 Single-vehicle Multi-vehicle
Single- vs. Multi-Vehicle
Crashes by Month
0%2%4%
6%8%
10%12%14%
16%18%20%
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Month
Perc
en
t o
f T
ota
l C
rash
es
Single-Vehicle Multi-VehicleAge 38.5 38.3Female 8% 9%Helmet Usage 34% 41%Completed Training Program 6% 7%Passenger 15% 17%Bike Age 10.1 7.7Alcohol Use 2% 5%Wet Pavement 4% 3%
Methodology – Multi-Vehicle
Multinomial logit (MNL) model with,
Sin = βi Xin + εin
Sin is the function that determines the probability of severity i for crash n, Xin is a vector of measurable characteristics
(motorcyclist and roadway characteristics) that determine the severity level for crash n,
βi is a vector of estimable coefficients, and εin is an error term accounting for unobserved effects
influencing the injury severity of crash n
Methodology – Multi-Vehicle
if εin are assumed to be extreme value distributed (see McFadden, 1981), then a standard multinomial logit model results,
where Pn(i) is the probability that crash n will result in severity i and I is the set of possible injury severity levels (PDO/Possible, Non-incapacitating, Incapacitating, Fatal).
i inn
I In I
EXPP i
EXP
β X
β X
Methodology – Multi-Vehicle
Elasticity
Pseudo-elasticity
1expexpexp
expexp
n n
ni
nik
II IIiniinikini
Iinikini
Px XxX
xX
ikikni
nik
nik
niPx xiP
P
x
x
PE ni
nik
1
Multi-Vehicle Crash Severity Model
Injury Severity
No Evident Injury(PDO or Possible)
FatalInjury
IncapacitatingInjury
Non-IncapacitatingInjury
Some Multi-Vehicle Crash Severity Model Findings
Severity level; No injury:
Factors decreasing no-injury likelihood:
Alcohol use (other motorist) (65%) Head-on collision (35%) Motorcycle age
Some Multi-Vehicle Crash Severity Model Findings
Severity level; Incapacitating injury:
Factors increasing incapacitating-injury likelihood:
Motorcyclist speeding (50%) Motorcyclist age (4.2% per 1% increase in age) Vertical curve (81%) Horizontal curve (45%)
Some Multi-Vehicle Crash Severity Model Findings
Severity level; Fatality: Factors increasing fatality likelihood:
Motorcyclist at fault (126%) Motorcyclist speeding (116%) Head on collision (566%)
Factors decreasing fatality likelihood:
Helmet use (right angle) (61%)
Methodology – Single Vehicle
If εin are correlated (crash severity levels share unobserved effects):
where Pn(ji) is the probability of crash n resulting in injury severity j conditioned on the injury severity being in injury-severity category i, J is the conditional set of outcomes (conditioned on i), I is the unconditional set of outcome categories, LSin is the inclusive value (logsum), and i is an estimable parameter.
| || /n j i n j i JnJ
P j i EXP X EXP X
/n i in i in i in i inI
P i EXP X L EXP X LS
|in J i JnJ
LS LN EXP X
Single-Vehicle Crash Severity Model
Injury Severity
PDO orMinor Injury
FatalInjury
IncapacitatingInjury
No Evident Injury(PDO or Possible)
Non-IncapacitatingInjury
Some Single-Vehicle Crash Severity Model Findings
Severity level; Minor or No injury:
Factors increasing minor/no-injury likelihood: Motorcycle less than 5 years old (20%) Helmet used (50%)
Factors decreasing minor/no-injury likelihood: Motorcyclist age (1.15% per 1% increase in age) Alcohol use (10%) Speeding (14%) Collisions with trees, poles, curbs, culverts, guardrails)
Some Single-Vehicle Crash Severity Model Findings
Severity level; Fatality:
Factors increasing fatality likelihood:
Over 2 years since took BRC (171%) Speeding (212%) Run-off-road (137%) Collision with tree (525%) Collision with pole (344%)
Conclusions
Critical areas Poor visibility
horizontal curvature, vertical curvature, darkness Unsafe speed Risk-taking behavior
alcohol use, not wearing a helmet Collision type
Right-angle, head-on, and collisions with fixed objects
Age
Conclusions
Critical areas (continued) Rider training (BRC results)
Degradation in skills, self-selectivity, risk compensation?
Encouragingly, crashes were found to be less severe: Under wet pavement conditions Near intersections When passengers were on the motorcycle
Additional Evidence on the Effectiveness of Motorcycle Training and Motorcyclists’ Risk-taking Behavior
Peter T. Savolainen, Ph.D.
Wayne State University
Fred Mannering, Ph.D.
Purdue University
Overview
Background Research Objectives Methodology Crash Propensity Model Top Travel Speed Model Helmet Usage Model Conclusions
Background
Rider education and training critical to motorcycle safety agenda
Limited research on education/training programs
Contradictory results Methodological shortcomings
Background
Methodological shortcomings: Lack of consideration of variables beyond
violation and crash statistics
Lack of control for exposure
Not fully considering dissimilarity between trained/untrained riders
Not considering possible risk compensation
Research Objectives
To provide additional evidence on effectiveness of motorcycle training courses Motorcyclist survey
Using 2005 sample of Indiana motorcyclists
Motorcyclist Survey Survey developed to collect data on:
Demographics Training history Riding behavior Crash involvement
2 groups of riders Trained: ABATE of Indiana – MSF Basic Rider Course
(BRC) Untrained: Indiana BMV and ABATE newsletter
Surveys mailed to 4,000 riders from each group Over 1,300 responses obtained
Motorcyclist Survey
Why ABATE? Why combine samples? Not statistically different. Proof: likelihood ratio test
LL(βR) = log-likelihood of restricted model e.g., BMV only sample
LL(βU) = log-likelihood of unrestricted model e.g., BMV and ABATE sample
Combining allows more precise parameter estimates
UR LLLLX 22
Summary Statistics
Average age 47.8 84% male, 16% female Completed BRC 60%
Multiple times 6% Completed ERC 12% ABATE members 46% Annual exposure
<1000 23% 1000-5000 51% Over 5000 26%
Summary Statistics
Type of Motorcycle
Sportbike: 15% Cruiser: 46% Touring: 27% Other:
12%
Summary Statistics
Reasons for not taking BRC
No need to take course: 47% Could not find time: 34% Unaware of course: 15% Could not afford program cost: 4%
Summary Statistics
Helmet usage frequency
Always/Usually: 56% Sometimes:
21% Rarely/Never: 23%
Methodology Multinomial logit models developed with,
Rin = βi Xin + εin
Rin is the function that determines the probability of response i being chosen by motorcyclist n,
Xin is a vector of measurable characteristics (socioeconomics and rider perceptions) that determine the response of motorcyclist n,
βi is a vector of estimable coefficients, and εin is an error term accounting for unobserved effects
influencing the response of motorcyclist n
Methodology
if εin are assumed to be extreme value distributed (see McFadden, 1981), then a standard multinomial logit model results,
where Pn(i) is the probability that motorcyclist n will choose response i and I is the set of possible survey responses.
i inn
I In I
EXPP i
EXP
β X
β X
Crash Propensity Model
Cni is a function that determines crash propensity
Xni is a vector of rider characteristics
niniini XC
ni
ni
C
C
ni e
eP
1
Crash Involvement
0 crashes 1+ crashes
Crash Propensity Model
Crash propensity increases with: Not wearing helmet (63%) Ride over 100 mph in past 12 mo. (161%) Sportbike (54%) Ride over 10,000 mi/yr (102%) Age under 35 (59%) Completed BRC once (44%) Completed BRC more than once (180%)
Crash Propensity Model
Crash propensity decreases with: Citing no need for BRC (51%) Riding experience
Highest during 1st year Decreases years 2-4 (58%) Increases slightly year 5+
Riding 500-1000mi/yr (64%)
Crash Propensity Model
Note on BRC findings: Completed BRC once (increases crash 44%) Completed BRC more than once
(increases crash 180%) Cited no need for BRC (decreases crash 51%)
Evidence that BRC riders may be a self-selected group of inherently less-skillful riders
Maximum Speed Model
Binary logit model for maximum speed
MSni is a function that determines maximum travel speed
Xni is a vector of rider characteristics
niniini XMS
ni
ni
MS
MS
ni e
eP
1
MaximumTravel Speed
Less than 90 mph 90 mph or faster
Maximum Speed Model
Increasing probability of riding over 90 mph: Motorcycle primary mode of travel (42%) Usually wear a helmet (39%) Sportbike riders (128%) Drank alcohol within 2 hrs of riding (66%) Licensed at 40+ years old (38%) Ride 5-10K miles per year (106%) Ride over 10K miles per year (189%) Involved in crash/near-miss (30%)
Maximum Speed Model
Decreasing probability of riding over 90 mph:
Rider age (1.82% per 1% increase in age) Female riders (61%) Smaller engines Usually wear protective clothing/equipment
(30%)
Helmet Usage Model
Multinomial Logit for helmet usage Xni rider characteristics Hni is equal to:
1 : Always/Usually 2: Sometimes 3: Rarely/Never
Helmet Usage
Always/Usually Rarely/NeverSometimes
niniini XH
j
H
H
ni nj
ni
e
eP
Helmet Usage Model
Always wear helmet: Typically wear other protective equipment or
reflective clothing/equipment Typical travel speed over 70 mph Typical travel speed less than 60 mph Older riders Number of bikes owned
Helmet Usage Model
Never wear helmet: Motorcycle primary mode of travel Never wear other protective equipment Larger engine displacement Rode over 100 mph in past year Involved in near-miss in past year Drank alcohol within 2 hrs of riding Females ABATE members
Except for those completing BRC Self-rated as excellent rider
Conclusions
Individuals taking BRC are more likely to be crash-involved Inherently less capable riders? Overcompensation of risks with learned material?
Skill-measurement methods must be developed and research undertaken to understand how skills can be improved considering: Risk compensation Self selection of less skilled rider to training
courses
Future Research Directions Improvement of crash records system
Further research into rider training, self-selectivity into training courses, and risk compensation induced by course-taught material
Improvements to Rider Training Program Baseline evaluation
Further application of survey methodology Regional/national level Focus on other issues