Development of a partial proportional odds model for pedestrian injury severity at intersections

Xi Lu, Zhuanglin Ma, Steven I.Jy Chien, Ying Xiong

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Pedestrian injury in crashes at intersections often results from complex interaction among various factors. The factor identification is a critical task for understanding the causes and improving the pedestrian safety. A total of 2,614 crash records at signalized and non-signal-ized intersections were applied. A Partial Proportional Odds (PPO) model was developed to examine the factors influencing Pedestrian Injury Severity (PIS) because it can accommodate the ordered response nature of injury severity. An elasticity analysis was conducted to quan-tify the marginal effects of contributing factors on the likelihood of PIS. For signalized intersections, seven explanatory variables significantly affect the likelihood of PIS, in which five explanatory variables violate the Proportional Odds Assumption (POA). Local driver, truck, holiday, clear weather, and hit-and-run lead to higher likelihood of severer PIS. For non-signalized intersec-tions, six explanatory variables were found significant to the PIS, in which three explanatory variables violate the POA. Young and adult drivers, senior pedestrian, bus/ van, divided road, holiday, and darkness tend to increase the likelihood of severer PIS. The vehicles of large size and heavy weight (e.g. truck, bus/van) are significant factors to the PIS at both signalized and non-signalized in-tersections. The proposed PPO model has demonstrated its effectiveness in identifying the effects of contributing factors on the PIS.

Original languageEnglish (US)
Pages (from-to)559-571
Number of pages13
JournalPromet - Traffic and Transportation
Issue number4
StatePublished - Jul 2020

All Science Journal Classification (ASJC) codes

  • Ocean Engineering
  • Engineering (miscellaneous)
  • Civil and Structural Engineering


  • Crash injury severity
  • Intersection
  • Partial proportional odds model
  • Pedestrian
  • Safety


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