pedestrian and bicycle accidents with motor vehicles gudmundur f. ulfarsson, ph.d. assistant...
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Pedestrian and Bicycle Accidents with Motor
VehiclesGudmundur F. Ulfarsson, Ph.D.Assistant ProfessorDirector, Transportation Systems Engineering ProgramDepartment of Civil Engineering
Acknowledgement
University of North Carolina Highway Safety Research Center provided the data in this study
Mr. Joon-Ki Kim (doctoral candidate, Washington University)
Dr. Sungyop Kim (University of Missouri-Kansas City)
Dr. Venky N. Shankar (Pennsylvania State University)
Background and motivation Objectives Factors affecting bicycle and
pedestrian safety Data description Methodology Results Conclusions
Background and Motivation
Bicyclists and pedestrian Two particularly vulnerable groups 2% and 11% of all fatalities by traffic
accidents, respectively 46,000 bicyclists were injured in 2003 68,000 pedestrians were injured in
accidents in 2004
Background and Motivation
Previous studies Accident rates Accident frequency associated with a
certain type of injury Aggregate data
There is a need for studying the vulnerable groups’ safety based on disaggregate individual accident data
Research Objective
To build probability models of injury severity for bicyclists and pedestrians By employing econometric analysis and
behavioral models as a statistical method
For examining the factors affecting injury severity in bicycle-vehicle accidents and pedestrian-vehicle accidents
Research Objective
The primary goals Develop discrete probability models of
injury severity by using disaggregate accident data
Consider heteroscedasticity (non-identical variance of error terms) across individuals
Examine the factors affecting bicyclist injury severity in bicycle-motor vehicle accidents
Pedal-Cyclist Fatalities in U.S.
The number of pedal-cyclist fatalities 833 fatalities in 1995 629 fatalities in 2003 784 fatalities in 2005
Pedal-cyclist Fatalities
500
600
700
800
900
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Year
Factors Affecting Bicycle Safety
Alcohol Correlated with casualties Increases the risk of injury
Higher Speed Limits Modify driver’s scanning pattern
Pay attention to the most relevant direction Ignore the less relevant direction
The most frequent type of bicycle-motor vehicle accident
A driver turning right and a bicycle coming from the driver’s right
Factors Affecting Bicycle Safety
Age Children and the elderly are the primary groups
that suffer from injuries
Gender Males are overrepresented compared to females
at almost all ages
Obeying the law Bicyclists are more likely than drivers to violate
traffic laws
Lighting, weather, annual daily traffic, road design, and so on.
Factors Affecting Pedestrian Safety
Age The older pedestrians suffer from more serious
injuries than the other age groups in pedestrian-vehicle accidents
Vehicle speed Increases the risk of pedestrian-vehicle accidents Increases the pedestrian's injury severity
Alcohol Increases the risk of pedestrian-vehicle accidents Increases the pedestrian's injury severity
Factors Affecting Pedestrian Safety
Visibility The main causes of pedestrian-vehicle accidents
Crosswalk Most pedestrian accidents occur when
pedestrians cross roadways Only 13.5 % of all pedestrians look left and right
when they enter the crosswalk No traffic control device increases the risk of
collision involving an older pedestrian Traffic volume, educational achievement,
gross national income per capita, and so on
Bicycle Data Description
Police-reported accident data from North Carolina for the years 1997 through 2000 (The University of North Carolina Highway Safety Research Center)
Analyze only accidents that involve a single motorist and a bicyclist
Bicyclist injury severity categories: Fatal injury, incapacitating injury, non-
incapacitating injury, possible or no injury Sample size = 2,934
Bicycle Data Description
Bicyclist injury severity distributionN %
Fatal 104 4
Incapacitating 363 12
Non-incapacitating
1,323 45
Possible or no injury
1,144 39
Total 2,934 100%
Pedestrian Data Description
Police-reported accident data from North Carolina for the years 1997 through 2000
Analyze only accidents that involve a single motorist and a pedestrian
Sample size = 5,808
Pedestrian Data Description
Pedestrian injury severity distributionN %
Fatal 591 10
Incapacitating 1,094 19
Non-incapacitating
1,976 34
Possible or no injury
2,147 37
Total 5,808 100%
Methodology
Multinomial logit (MNL) model Heteroscedastic generalized extreme
value (HET GEV) model
Methodology: Multinomial Logit
Discrete Outcome Multinomial Logit (MNL) model: 1) Fatal Injury, 2) Incapacitating Injury, 3) Non-Incapacitating Injury, and 4) Possible or No Injury
Estimate propensity towards an injury severity Categories of explanatory variables:
Bicyclist or Pedestrian, Driver, Vehicle, Accident, Control, Geometry, Land development, and Environmental/Temporal
Tt
S
S
nttn
nt
e
eP
Heteroscedastic Extreme Value (HEV) Model
Heteroscedasticity across individuals Propensity function
The probability of being in the injury severity
ni i ni niU β x ni n i ni n ni nU β x
Individual-specific scaling parameter
1
Pr{ }
Pr{ }
e, where 0
e
i ni n
j nj n
ni ni nj
ni n nj n
nI
j
P U U j i
U U j i
β x
β x
2
2and Var( )
6nin
Heteroscedastic Extreme Value (HEV) Model
Individual-specific scaling parameter
Estimation
where is a vector of observed individual-specific variables is a vector of estimable coefficients
nenγznzγ
where is the total number of individual is the total number of injury severities is 1 if individual suffers from injury severity , 0 otherwise
N nI iniy n i
N
n
I
inini PyL
1 1
,ln
Explanatory Variables
Bicyclist Characteristics Bicyclist age and gender, Intoxication, Helmet Use
Pedestrian Characteristics Pedestrian age and gender, Intoxication
Driver Characteristics: Driver age and gender, Intoxication
Vehicle Characteristics: Estimated vehicle speed Vehicle Type
Land Characteristics Urban Area Land development type (e.g., commercial area)
Explanatory Variables (Cont.)
Accident Characteristics Bicycle direction (facing traffic or with traffic) Type of accident Party at fault Speeding involved Road defects involved Accident location
Control Characteristics Signal, sign, other control, or no control
present
Explanatory Variables (Cont.)
Geometry Characteristics Intersection, Asphalt road, Posted speed limit Road class type, Road geometry (e.g., curve) Road type (e.g., two-way divided) Number of traffic lanes
Temporal Characteristics Weekend, Time
Environmental Characteristics Weather, Light conditions, Road surface
(e.g., dry)
Model Selection
Bicyclist injury severity turned out better with the multinomial logit model
Pedestrian injury severity turned out better with the heteroscedastic extreme value model The pedestrian age forms the
heteroscedasticity Gender was explored and not found
significant
Bicyclist Model Findings
Bicyclist Characteristics Bicyclist age 55 and over increases the
probability of fatal injury (109%) Bicyclist intoxication increases the
probability of fatal injury (174%) Helmets decrease the probabilities of fatal
injury (-24%) and possible or no injury (-24%)
Driver Characteristics Driver intoxication increases the
probabilities of bicyclist fatal injury (265%) and incapacitating injury (87.7%)
Bicyclist Findings (Cont.)
Vehicle Characteristics Estimated vehicle speed beyond 32.2 km/h
(20 mph) increases the probabilities of serious injuries
Pick-up trucks and heavy trucks increase the probability of fatal injury (10% and 381%, respectively)
Speed Fatal injury Incapacitating Injury
32.2-48.3 km/h 93% 93%
48.3-64.4 km/h 303% 100%
64.4-80.5 km/h 1,159% 83%
80.5 km/h + 1,504% 134%
Bicyclist Findings (Cont.)
Accident Characteristics Head-on collisions increase the probability
of fatal injury (101%) Speeding involved increases the probability
of fatal injury (300%)
Bicyclist Findings (Cont.)
Geometry Characteristics A curved road increases the probabilities of
fatal injury (68%) and incapacitating injury (68%)
Two-way divided roadways decrease the probabilities of fatal injury (-11%) and incapacitating injury (-11%)
Temporal Characteristics Weekend increases the probability of fatal
injury (13%) Time (06:00-09:59) increases the probability
of fatal injury (85.4%)
Bicyclist Findings (Cont.)
Environmental Characteristics Inclement weather (fog, rain, snow etc.)
increases the probability of fatal injury (129%)
Darkness without streetlights increases the probabilities of fatal injury (111%) and incapacitating injury (50%)
Pedestrian Model Findings
Pedestrian Characteristics Pedestrian age is continuous and has a elasticity
of 1.85 towards fatal injury, indicating a 1.85% increase in the probability of a fatality for each 1% increase in age
Driver Characteristics Driver age is continuous with elasticity of -0.126
towards fatal injury, indicating a small reduction in fatality probability with increasing driver age
Driver intoxication increases the probabilities of pedestrian fatal injury (168%)
Pedestrian Model Findings
Environmental Characteristics Dark-lighted conditions increase the
probability of fatal injury (148%) Dark-unlighted conditions increase the
probability of fatal injury (338%) Vehicle Type
Truck increases the probability of fatal injury (265%)
Pedestrian Model Findings
Road Types Freeway increases the probability of
fatal and incapacitating injury (143%) US Route increases the probability of
fatal injury (216%) State Route increases the probability of
fatal injury (146%)
Pedestrian Model Findings
Accident Characteristics Speeding-involved increases the
probability of fatal injury (309%) Both driver and pedestrian at fault
increases the probability of fatal injury (114%)
Pedestrian solely at fault increases the probability of fatal injury (386%)
Pedestrian Model Findings
Pedestrian age shows a significant effect on heteroscedasticity
Simulation of probability of fatal injury for all pedestrians by starting each pedestrian at age 18 and increasing by one to 100
Average for all pedestrians by age Shows MNL overestimates fatality
probabilities with age
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
18 24 30 36 42 48 54 60 66 72 78 84 90 96
Age
Ave
rag
e P
rob
abili
ty o
f F
atal
Inju
ry
HET
MNL
Bicyclist Conclusions Factors that significantly increase the probability of
fatal injury Vehicle speed >20 mph (fatality 2-16x more likely) Truck involved (fatality ~5x more likely) Speeding-involved (fatality ~4x more likely) Intoxicated driver/bicyclist (fatality ~3x more likely) Inclement weather (fatality ~2x more likely) Darkness w/o streetlights (fatality ~2x more likely) Bicyclist aged 55 and over (fatality ~2x more likely) Head-on collision (fatality ~2x more likely)
Bicyclist Conclusions
Policy perspective Lower speed limit in residential area (e.g. 20
mph)
Education perspective Discourage biking against traffic due to
increased injury severity in head-on crashes Discourage drunk biking Encourage helmet usage Develop education programs for older
bicyclists, in addition to education for youth
Bicyclist Conclusions
Engineering perspective Enable separated bicycling from high-speed
traffic (e.g., when speed limit of 30 mph or over)
Design to avoid conflicts with oncoming trafficand heavy trucks
Pedestrian Conclusions Factors that significantly increase the probability of
fatal injury Pedestrian age (fatality elasticity 1.85) Pedestrian at fault (fatality ~5x more likely) Darkness w/o streetlights (fatality ~4x more likely) Speeding-involved (fatality ~4x more likely) Truck involved (fatality ~3.6x more likely) US Route (fatality ~3x more likely) Intoxicated driver (fatality ~2.7x more likely) Darkness with streetlights (fatality ~2.5x more likely) State Route (fatality ~2.5x more likely) Freeway (fatality ~2.4x more likely) Both driver and ped. fault (fatality ~2x more likely)
Pedestrian Conclusions
Policy perspective Reduce truck traffic in pedestrian areas Restrict pedestrian access to major routes if
possible
Education perspective Drunk driving Encourage reflectors worn at night Develop education programs for older
pedestrians Walking along major routes
Pedestrian Conclusions
Engineering perspective Improved street lighting Design to avoid conflicts with traffic streams
with higher percentage of trucks Improve road crossings with older
pedestrians in mind
Future Studies
Further study needs to develop a more detailed picture of land use and the built environment around accident sites to facilitate specific design and policy interventions that may reduce bicyclist and pedestrian injury severities
