Investigating factors influencing pedestrian injury severity at intersections

Zhuanglin Ma, Xi Lu, Steven I.Jy Chien, Dawei Hu

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


Objective: Vehicle crashes that involve pedestrians at intersections have been reported occasionally. Pedestrian injury severity in these crashes is significantly related to driver and pedestrian attributes, vehicle characteristics, and the geometry of intersections. Identifying factors associated with pedestrian injury severity (PIS) is critical for reducing crashes and improving safety. For developing the proposed probit models, drivers involved in crashes are classified into 3 groups: young drivers (16 ≤ age ≤ 24), middle-aged drivers (25 ≤ age ≤ 64), and older drivers (age ≥ 65). This study determines that PIS is significantly but differently affected by these grouped drivers with different sets of explanatory variables. Methods: A total of 2,614 crash records (2011–2012) at intersections in Cook County, Illinois, were collected. An ordered probit modeling approach was employed to develop the proposed model and examine factors influencing PIS. The likelihood ratio test was used to assess model performance. Elasticity analysis was conducted to interpret the marginal effect of contributing factors on PIS associated with different driver groups by age. Results: The results show that 4 independent variables, including pedestrian age, vehicle type, point of first contact, and weather condition, significantly affect PIS at intersections for all drivers. Two additional independent variables (i.e., number of vehicles and traffic type) affect PIS for young and middle-aged drivers, and 2 other variables (i.e., divided type and hit-and-run related) are significant to PIS for both young and older drivers. Conclusions: The independent variables significant to PIS at intersections for young, middle-aged, and older driver groups were identified and the marginal effect of each variable to the likelihood of PIS were assessed.

Original languageEnglish (US)
Pages (from-to)159-164
Number of pages6
JournalTraffic Injury Prevention
Issue number2
StatePublished - Feb 17 2018

All Science Journal Classification (ASJC) codes

  • Safety Research
  • Public Health, Environmental and Occupational Health


  • Pedestrian injury severity
  • age
  • driver
  • elasticity analysis
  • intersection
  • ordered probit model
  • safety


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