Campus Units

Civil, Construction and Environmental Engineering, Statistics

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

12-2017

Journal or Book Title

Analytic Methods in Accident Research

Volume

16

First Page

104

Last Page

116

DOI

10.1016/j.amar.2017.09.002

Abstract

Unobserved heterogeneity produced by spatial and temporal correlations of crashes often needs to be captured in crash frequency modeling. Although many studies have included either spatial or temporal effects in crash frequency modeling, only a limited number of studies have considered both. This study addresses the limitations of existing studies by exploring multiple models that best fit the spatial and temporal correlations. In this study, we used Bayesian spatio-temporal models to investigate regional crash frequency trends, and explored the effects of omitting spatial or temporal trends in spatio-temporal correlated data. The fast Bayesian inference approach, integrated nested Laplace approximation, was used to estimate parameters. It was found that fatal crashes showed decreasing trends in all Iowa counties from 2006 to 2015, but the decreasing rates varied by counties. Among all the covariates investigated, only vehicle miles traveled (VMT) was significant. None of the socio-economic or weather indicators were found to be significant in the presence of VMT. Both spatial and temporal effects were found to be important, and they were responsible for both over dispersion and zero inflation in the crash data. In addition, spatial effects played a more important role than did temporal effects in the studied dataset, but temporal component selection was still important in spatio-temporal modeling.

Research Focus Area

Transportation Engineering

Comments

This is a manuscript of an article published as Liu, Chenhui, and Anuj Sharma. "Exploring spatio-temporal effects in traffic crash trend analysis." Analytic Methods in Accident Research 16 (2017): 104-116. DOI: 10.1016/j.amar.2017.09.002. Posted with permission.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Copyright Owner

Elsevier Ltd.

Language

en

File Format

application/pdf

Available for download on Saturday, December 01, 2018

Published Version

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