On the significance of omitted variables in intersection crash modeling
Mitra, Sudeshna & Washington, Simon (2012) On the significance of omitted variables in intersection crash modeling. Accident Analysis and Prevention, 49(Nov), pp. 439-448.
Advances in safety research—trying to improve the collective understanding of motor vehicle crash causes and contributing factors—rest upon the pursuit of numerous lines of research inquiry. The research community has focused considerable attention on analytical methods development (negative binomial models, simultaneous equations, etc.), on better experimental designs (before-after studies, comparison sites, etc.), on improving exposure measures, and on model specification improvements (additive terms, non-linear relations, etc.).
One might logically seek to know which lines of inquiry might provide the most significant improvements in understanding crash causation and/or prediction. It is the contention of this paper that the exclusion of important variables (causal or surrogate measures of causal variables) cause omitted variable bias in model estimation and is an important and neglected line of inquiry in safety research. In particular, spatially related variables are often difficult to collect and omitted from crash models—but offer significant opportunities to better understand contributing factors and/or causes of crashes.
This study examines the role of important variables (other than Average Annual Daily Traffic (AADT)) that are generally omitted from intersection crash prediction models. In addition to the geometric and traffic regulatory information of intersection, the proposed model includes many spatial factors such as local influences of weather, sun glare, proximity to drinking establishments, and proximity to schools—representing a mix of potential environmental and human factors that are theoretically important, but rarely used. Results suggest that these variables in addition to AADT have significant explanatory power, and their exclusion leads to omitted variable bias. Provided is evidence that variable exclusion overstates the effect of minor road AADT by as much as 40% and major road AADT by 14%.
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|Item Type:||Journal Article|
|Keywords:||omitted variables, spatial variables, signalized intersection safety, motor vehicle crashes, crash modeling, negative binomial, traffic safety|
|Divisions:||Current > Research Centres > Centre for Accident Research & Road Safety - Qld (CARRS-Q)
Current > Schools > School of Civil Engineering & Built Environment
Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
|Copyright Owner:||Copyright 2012 Elsevier Ltd|
|Copyright Statement:||NOTICE: this is the author’s version of a work that was accepted for publication in Accident Analysis and Prevention. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Accident Analysis and Prevention, Volume 49 (November 2012). DOI: 10.1016/j.aap.2012.03.014|
|Deposited On:||04 Jun 2012 22:49|
|Last Modified:||04 Dec 2015 10:36|
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