Pedestrian and Bicycle Accidents with Motor Vehicles Gudmundur F. Ulfarsson, Ph.D. Assistant Professor Director, Transportation Systems Engineering Program Department 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 ,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 fatalities in fatalities in 2005

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 distribution N% Fatal1044 Incapacitating36312 Non-incapacitating1,32345 Possible or no injury1,14439 Total2,934100%

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 distribution N% Fatal59110 Incapacitating1,09419 Non-incapacitating1,97634 Possible or no injury2,14737 Total5,808100%

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

Heteroscedastic Extreme Value (HEV) Model Heteroscedasticity across individuals Propensity function The probability of being in the injury severity Individual-specific scaling parameter

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 where is the total number of individual is the total number of injury severities is 1 if individual suffers from injury severity, 0 otherwise

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) SpeedFatal injuryIncapacitating Injury km/h93% km/h303%100% km/h1,159%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 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

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 traffic and 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

Contact Information Gudmundur F. Ulfarsson tel: +1 (314)