Exploring the Need to Model Two- and Multiple-Vehicle Crashes Separately

Document Type

Article

Publication Date

1-1-2022

Abstract

Single-vehicle crashes have been shown to differ from two-plus vehicle crashes. Several studies have discussed the issues with modeling single-vehicle and two-plus vehicle crashes together. However, none of the empirical studies have attempted to study two-vehicle (2V) and multiple-vehicle (MV), that is, three-plus crash groups, to understand their correlation and influencing factors. This study first investigated 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 were evaluated. Three regression models—a correlated bivariate negative binomial regression (BNR) model, an uncorrelated bivariate negative binomial regression (NR) model, and a univariate negative binomial regression (UNR) model, were developed and compared. The analysis was based on the 2011–2015 crashes that occurred on I-4 in Florida. Findings indicated that the BNR model significantly outperformed the NR and the UNR models. The model results suggest that disaggregating 2V and MV 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 for 2V crashes included the presence of interchange influence area, and for MV crashes, the presence of a vertical curve and the presence of a horizontal curve. Study findings could assist transportation officials in implementing specific safety countermeasures for road segments identified as hotspots for 2V and MV crashes.

Publication Title

Transportation Research Record

Volume

2676

Issue

1

First Page

622

Last Page

636

Digital Object Identifier (DOI)

10.1177/03611981211037882

ISSN

03611981

E-ISSN

21694052

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