Modeling crash outcome probabilities at rural intersections: application of hierarchical binomial logistic models

Kim, Do-Gyeong, Lee, Yuhwa, Washington, Simon and Choi, Keechoo (2007) Modeling crash outcome probabilities at rural intersections: application of hierarchical binomial logistic models. Accident Analysis and Prevention, 39 1: 125-134. doi:10.1016/j.aap.2006.06.011


Author Kim, Do-Gyeong
Lee, Yuhwa
Washington, Simon
Choi, Keechoo
Title Modeling crash outcome probabilities at rural intersections: application of hierarchical binomial logistic models
Journal name Accident Analysis and Prevention   Check publisher's open access policy
ISSN 0001-4575
1879-2057
Publication date 2007-01-01
Sub-type Article (original research)
DOI 10.1016/j.aap.2006.06.011
Open Access Status Not yet assessed
Volume 39
Issue 1
Start page 125
End page 134
Total pages 10
Place of publication Langford Lane, Oxford, United Kingdom
Publisher Elsevier
Language eng
Abstract It is important to examine the nature of the relationships between roadway, environmental, and traffic factors and motor vehicle crashes, with the aim to improve the collective understanding of causal mechanisms involved in crashes and to better predict their occurrence. Statistical models of motor vehicle crashes are one path of inquiry often used to gain these initial insights. Recent efforts have focused on the estimation of negative binomial and Poisson regression models (and related deviants) due to their relatively good fit to crash data. Of course analysts constantly seek methods that offer greater consistency with the data generating mechanism (motor vehicle crashes in this case), provide better statistical fit, and provide insight into data structure that was previously unavailable. One such opportunity exists with some types of crash data, in particular crash-level data that are collected across roadway segments, intersections, etc. It is argued in this paper that some crash data possess hierarchical structure that has not routinely been exploited. This paper describes the application of binomial multilevel models of crash types using 548 motor vehicle crashes collected from 91 two-lane rural intersections in the state of Georgia. Crash prediction models are estimated for angle, rear-end, and sideswipe (both same direction and opposite direction) crashes. The contributions of the paper are the realization of hierarchical data structure and the application of a theoretically appealing and suitable analysis approach for multilevel data, yielding insights into intersection-related crashes by crash type.
Keyword Hierarchical data
Motor vehicle crashes
Multilevel models
Rural intersections
Transportation safety
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Civil Engineering Publications
 
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