Exploring A Need to Model Two- and Multiple-vehicle Crashes Separately

Single-vehicle crashes have been shown to differ from two-plus vehicle crashes. Several studies have discussed the issues with modeling single- and two-plus vehicle crashes together. However, none of the empirical studies have attempted to study two-vehicle (2V), and multiple-vehicle (MV), i.e., three-plus, crash groups to understand their correlation and influencing factors. This study first investigates whether there is a need to develop separate safety performance functions for 2V and MV crashes, in addition to single-vehicle crashes. Then, the correlation and influencing factors of 2V and MV are evaluated. Three regression models – a correlated bivariate negative binomial regression (BNR) model, an uncorrelated bivariate negative binomial regression (NR) models, and a univariate negative binomial regression (UNR) model, are fitted and compared. The analysis is based on the 2011-2015 crash data that occurred on I-4 in Florida. Findings indicate that the BNR model significantly outperformed the NR and the UNR models. The model results suggest that disaggregating these crashes while allowing correlation between the groups for the latent effects in the model best describes the data. Traffic volume, posted speed limit, and median type were found significant in contributing to the occurrence of both 2V and MV crashes. Additional contributing factors included the presence of interchange influence area for 2V crashes and the presence of a vertical curve and the presence of a horizontal curve for MV crashes. Study findings could assist transportation officials implement specific safety countermeasures for road segments that are identified as hotspots for 2V and MV crashes.


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Maps; References; Tables;
  • Pagination: 22p

Subject/Index Terms

Filing Info

  • Accession Number: 01763461
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-04403
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:01AM