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Pedestrian and Bicycle Accidents with Motor Vehicles

Pedestrian and Bicycle Accidents with Motor Vehicles. Gudmundur F. Ulfarsson, Ph.D. Assistant Professor Director, Transportation Systems Engineering Program Department of Civil Engineering. Acknowledgement.

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Pedestrian and Bicycle Accidents with Motor Vehicles

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  1. Pedestrian and Bicycle Accidents with Motor Vehicles Gudmundur F. Ulfarsson, Ph.D. Assistant Professor Director, Transportation Systems Engineering Program Department of Civil Engineering

  2. 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)

  3. Background and motivation • Objectives • Factors affecting bicycle and pedestrian safety • Data description • Methodology • Results • Conclusions

  4. 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

  5. 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

  6. 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

  7. 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

  8. Pedal-Cyclist Fatalities in U.S. • The number of pedal-cyclist fatalities • 833 fatalities in 1995 • 629 fatalities in 2003 • 784 fatalities in 2005

  9. 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

  10. 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 Bicycle Safety

  11. 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

  12. 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

  13. 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

  14. Bicycle Data Description • Bicyclist injury severity distribution

  15. 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

  16. Pedestrian Data Description • Pedestrian injury severity distribution

  17. Methodology • Multinomial logit (MNL) model • Heteroscedastic generalized extreme value (HET GEV) model

  18. 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

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

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

  21. 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)

  22. 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

  23. 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)

  24. 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

  25. 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%)

  26. 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)

  27. Bicyclist Findings (Cont.) • Accident Characteristics • Head-on collisions increase the probability of fatal injury (101%) • Speeding involved increases the probability of fatal injury (300%)

  28. 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%)

  29. 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%)

  30. 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%)

  31. 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%)

  32. 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%)

  33. 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%)

  34. 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

  35. 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)

  36. 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

  37. 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

  38. 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)

  39. 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

  40. 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

  41. 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

  42. Contact Information • Gudmundur F. Ulfarsson • gfu@wustl.edu • tel: +1 (314) 935-9354

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