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Probabilistic Models of Motorcyclists' Injury Severities in Single- and Multi-vehicle Crashes Peter T. Savolainen, Ph.D. Wayne State University Fred Mannering,

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Presentation on theme: "Probabilistic Models of Motorcyclists' Injury Severities in Single- and Multi-vehicle Crashes Peter T. Savolainen, Ph.D. Wayne State University Fred Mannering,"— Presentation transcript:

1 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

2 Overview Background Research Objectives Methodology Multi-Vehicle Crash Severity Model Single-Vehicle Crash Severity Model Conclusions

3 Background

4

5 Ridership increasing Riding population changing Training Age Gender Bike design Speed Power Safety Repealed helmet laws

6 Research Objective To develop probabilistic models of motorcycle crash-injury severity using Indiana crash data from 2003 to 2005 Single-vehicle Multi-vehicle

7 Single- vs. Multi-Vehicle

8 Methodology – Multi-Vehicle Multinomial logit (MNL) model with, S in = β i X in + ε in S in is the function that determines the probability of severity i for crash n, X in is a vector of measurable characteristics (motorcyclist and roadway characteristics) that determine the severity level for crash n, β i is a vector of estimable coefficients, and ε in is an error term accounting for unobserved effects influencing the injury severity of crash n

9 Methodology – Multi-Vehicle if ε in are assumed to be extreme value distributed (see McFadden, 1981), then a standard multinomial logit model results, where P n (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).

10 Methodology – Multi-Vehicle Elasticity Pseudo-elasticity

11 Multi-Vehicle Crash Severity Model Injury Severity No Evident Injury (PDO or Possible) Non- Incapacitating Injury Incapacitating Injury Fatal Injury

12 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

13 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%)

14 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%)

15 Methodology – Single Vehicle If ε in are correlated (crash severity levels share unobserved effects): where P n (j  i) 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, LS in is the inclusive value (logsum), and  i is an estimable parameter.

16 Single-Vehicle Crash Severity Model Injury Severity PDO or Minor Injury No Evident Injury (PDO or Possible) Non- Incapacitating Injury Incapacitating Injury Fatal Injury

17 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)

18 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%)

19 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

20 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

21 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

22 Overview Background Research Objectives Methodology Crash Propensity Model Top Travel Speed Model Helmet Usage Model Conclusions

23 Background Rider education and training critical to motorcycle safety agenda Limited research on education/training programs Contradictory results Methodological shortcomings

24 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

25 Research Objectives To provide additional evidence on effectiveness of motorcycle training courses Motorcyclist survey Using 2005 sample of Indiana motorcyclists

26 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

27 Motorcyclist Survey Why ABATE? Why combine samples? Not statistically different. Proof: likelihood ratio test LL(β R ) = log-likelihood of restricted model e.g., BMV only sample LL(β U ) = log-likelihood of unrestricted model e.g., BMV and ABATE sample Combining allows more precise parameter estimates

28 Summary Statistics Average age % male, 16% female Completed BRC 60% Multiple times 6% Completed ERC 12% ABATE members 46% Annual exposure < % % Over %

29 Summary Statistics Type of Motorcycle Sportbike: 15% Cruiser: 46% Touring: 27% Other: 12%

30 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%

31 Summary Statistics Helmet usage frequency Always/Usually: 56% Sometimes: 21% Rarely/Never: 23%

32 Methodology Multinomial logit models developed with, R in = β i X in + ε in R in is the function that determines the probability of response i being chosen by motorcyclist n, X in is a vector of measurable characteristics (socioeconomics and rider perceptions) that determine the response of motorcyclist n, β i is a vector of estimable coefficients, and ε in is an error term accounting for unobserved effects influencing the response of motorcyclist n

33 Methodology if ε in are assumed to be extreme value distributed (see McFadden, 1981), then a standard multinomial logit model results, where P n (i) is the probability that motorcyclist n will choose response i and I is the set of possible survey responses.

34 Crash Propensity Model C ni is a function that determines crash propensity X ni is a vector of rider characteristics Crash Involvement 0 crashes1+ crashes

35 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%)

36 Crash Propensity Model Crash propensity decreases with: Citing no need for BRC (51%) Riding experience Highest during 1 st year Decreases years 2-4 (58%) Increases slightly year 5+ Riding mi/yr (64%)

37 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

38 Maximum Speed Model Binary logit model for maximum speed MS ni is a function that determines maximum travel speed X ni is a vector of rider characteristics Maximum Travel Speed Less than 90 mph 90 mph or faster

39 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%)

40 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%)

41 Helmet Usage Model Multinomial Logit for helmet usage X ni rider characteristics H ni is equal to: 1 : Always/Usually 2: Sometimes 3: Rarely/Never Helmet Usage Always/UsuallySometimesRarely/Never

42 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

43 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

44 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

45 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


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