Campus Units

Civil, Construction and Environmental Engineering, Statistics

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

3-2018

Journal or Book Title

Analytic Methods in Accident Research

Volume

17

First Page

14

Last Page

31

DOI

10.1016/j.amar.2018.02.001

Abstract

Unobserved heterogeneity across space, time, and crash type is often non-negligible in crash frequency modeling. When multiple crash types with spatial and temporal features are analyzed, multivariate spatio-temporal models should be considered. For this study, we analyzed the yearly county-level fatal, major injury, and minor injury crashes in Iowa from 2006 to 2015 using a multivariate spatio-temporal Bayesian model. The model adopted a multivariate spatial structure, a multivariate temporal structure, and a multivariate spatio-temporal interaction structure to account for possible correlations across injury severities over space, time, and spatio-temporal interaction, respectively. Income and weather indicators were found to have no significant effects on crash frequencies in the presence of vehicle miles traveled and unemployment rate. Both spatial and temporal effects were found to be important, and they played nearly the same roles for all three crash types in the studied dataset. Counties located in north and southwest Iowa were found to tend to have fewer crashes than the remaining counties. All three crash types generally showed descending trends from 2006 to 2015. They also had significantly positive correlations between each other in space but not in time. The crude crash rates and predicted crash rates were generally consistent for major injury and minor injury crashes but not for low-count fatal crashes. High-risk counties were identified using the posterior expected rank by the predicted crash cost rate, which was more able to truly represent the underlying traffic safety status than the rank by the crude crash cost rate.

Research Focus Area

Transportation Engineering

Comments

This is a manuscript of an article published as Liu, Chenhui, and Anuj Sharma. "Using the multivariate spatio-temporal Bayesian model to analyze traffic crashes by severity." Analytic Methods in Accident Research 17 (2018): 14-31. DOI: 10.1016/j.amar.2018.02.001. 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 Friday, March 01, 2019

Published Version

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