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.

Keywords:

• traffic injury severity
• pavement management factor
• Bayesian ordered probit (BOP) model
• pavement distress index (PDI)
• pavement serviceability index (PSI)
• rutting depth (RD)

Acknowledgments

The authors thank Southeastern Transportation Center for funding this research. The authors also thank the TDOT for providing crash data. A special thanks to the Chinese National Natural Science Foundation (71210001) for supporting this research. The authors are grateful to Tom Every from the TDOT for extracting the data set from TRIMS. Thanks are also due to Steve Allen, James Maxwell, and Jean Stevens from the TDOT, who helped the authors access the TDOT TRIMS and the Pavement Management System.