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Repealed helmet laws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