A Note on Modeling Pedestrian-Injury Severity in Motor-Vehicle Crashes With the Mixed Logit Model

Accid Anal Prev. 2010 Nov;42(6):1751-8. doi: 10.1016/j.aap.2010.04.016. Epub 2010 Jun 1.


Pedestrian-injury severity has been traditionally modeled with approaches that have assumed that the effect of each variable is fixed across injury observations. This assumption ignores possible unobserved heterogeneity which is likely to be particularly important in pedestrian injuries because unobserved physical health, strength, and behavior may significantly affect the pedestrians' ability to absorb collision forces. To address such unobserved heterogeneity, this research applies a mixed logit model to analyze pedestrian-injury severity in pedestrian-vehicle crashes. Using police-reported collision data from 1997 through 2000 from North Carolina, several factors were found to more than double the average probability of fatal injury for pedestrians in motor-vehicle crashes including: darkness without streetlights (400% increase in fatality probability), vehicle is a truck (370% increase), freeway (330% increase), speeding involved (360% increase), and collisions involving a motorist who had been drinking (250% increase). It was also found that the effect of pedestrian age was normally distributed across observations, and that as pedestrians became older the probability of fatal injury increased substantially. Heterogeneity in the mean of the random parameters for the freeway and pedestrian-solely-at-fault collision indicators was related to pedestrian gender, and heterogeneity in the mean of the random parameters for the traffic-sign and motorist-back-up indicators was related to pedestrian age.

MeSH terms

  • Acceleration / adverse effects
  • Accidents, Traffic / mortality*
  • Accidents, Traffic / prevention & control*
  • Adolescent
  • Adult
  • Age Factors
  • Aged
  • Alcoholic Intoxication / mortality
  • Alcoholic Intoxication / prevention & control
  • Environment Design
  • Female
  • Humans
  • Injury Severity Score*
  • Logistic Models*
  • Male
  • Middle Aged
  • North Carolina
  • Probability
  • Risk Factors
  • Sex Factors
  • Walking / injuries*
  • Wounds and Injuries / mortality*
  • Wounds and Injuries / prevention & control*
  • Young Adult