Citation

Abdel-Aty MA, Pemmanaboina R, Hsia L. Transp. Res. Rec. 2006; 1953: 1-9.

Copyright

(Copyright © 2006, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

unavailable

PMID

unavailable

Abstract

Most existing freeway crash frequency models analyze overall frequency of crashes. Furthermore, researchers have traditionally used average annual daily traffic (AADT) to represent traffic volume in their models. These two cases are examples of macroscopic crash frequency modeling. Segregating crashes on the basis of type of crash, peak or off-peak traffic conditions, lighting conditions, severity, and pavement condition could provide insight into the specific factors that affect each category. In this study multiple binary categorizations of the crashes were created to identify the factors associated with their frequencies and used geometric characteristics of the freeway and microscopic traffic variables that were based on loop detector data. These categorizations included multiple- and single-vehicle crashes, peak period and off peak period crashes, dry and wet pavement crashes, daytime and dark-hour crashes, and property-damage-only and injury crashes. Models for frequency of each of the two groups of crashes were estimated separately for all five categorizations. To account for correlation between the disturbance terms arising from omitted variables between any two models in a category, seemingly unrelated negative binomial (SUNB) regression was used for simultaneous estimation. SUNB estimation proved to be advantageous for multiple- and single-vehicle crashes and for daytime and dark-hour crashes. Road curvature and presence of on- or off-ramps were found to be the significant factors related to every crash category. Median type and pavement surface type were among other important factors affecting crashes. AADT was significant in most models and the 15-min coefficient of variation of speed was significant for frequency of daytime and peak period crashes. SUNB estimation proved to increase the efficiency of the crash frequency models by accounting for the disturbance correlation, reducing the standard errors, and providing better model fit.