title: Two-vehicle injury severity models based on integration of pavement management and traffic engineering factors.
abstract: OBJECTIVE
  The severity of traffic-related injuries has been studied by many researchers in recent decades. However, the evaluation of many factors is still in dispute and, until this point, few studies have taken into account pavement management factors as points of interest. The objective of this article is to evaluate the combined influences of pavement management factors and traditional traffic engineering factors on the injury severity of 2-vehicle crashes.
  
  
  METHODS
  This study examines 2-vehicle rear-end, sideswipe, and angle collisions that occurred on Tennessee state routes from 2004 to 2008. Both the traditional ordered probit (OP) model and Bayesian ordered probit (BOP) model with weak informative prior were fitted for each collision type. The performances of these models were evaluated based on the parameter estimates and deviances.
  
  
  RESULTS
  The results indicated that pavement management factors played identical roles in all 3 collision types. Pavement serviceability produces significant positive effects on the severity of injuries. The pavement distress index (PDI), rutting depth (RD), and rutting depth difference between right and left wheels (RD_df) were not significant in any of these 3 collision types. The effects of traffic engineering factors varied across collision types, except that a few were consistently significant in all 3 collision types, such as annual average daily traffic (AADT), rural-urban location, speed limit, peaking hour, and light condition.
  
  
  CONCLUSIONS
  The findings of this study indicated that improved pavement quality does not necessarily lessen the severity of injuries when a 2-vehicle crash occurs. The effects of traffic engineering factors are not universal but vary by the type of crash. The study also found that the BOP model with a weak informative prior can be used as an alternative but was not superior to the traditional OP model in terms of overall performance.
authors.affiliation: Department of Civil and Environmental Engineering, The University of Tennessee, Knoxville, Tennessee, USA.
authors.forename: Ximiao
authors.initials: X
authors.lastname: Jiang
authors.forename: Baoshan
authors.initials: B
authors.lastname: Huang
authors.forename: Xuedong
authors.initials: X
authors.lastname: Yan
authors.forename: Russell L
authors.initials: RL
authors.lastname: Zaretzki
authors.forename: Stephen
authors.initials: S
authors.lastname: Richards
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D000293:Adolescent; D000328:Adult; D000368:Aged; D001499:Bayes Theorem; D004779:Environment Design / Q000706:statistics & numerical data; D006804:Ergonomics; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D012307:Risk Factors; D013714:Tennessee; D014186:Transportation; D015599:Trauma Severity Indices; D014947:Wounds and Injuries / Q000209:etiology*; D055815:Young Adult