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A flexible discrete density random parameters model for count data: embracing unobserved heterogeneity in highway safety analysis

A flexible discrete density random parameters model for count data: embracing unobserved heterogeneity in highway safety analysis
A flexible discrete density random parameters model for count data: embracing unobserved heterogeneity in highway safety analysis
In traffic safety studies, there are almost inevitable concerns about unobserved heterogeneity. As a feasible alternative to current methods, this article proposes a novel crash count model that can address asymmetry and multimodality in the data. Specifically, a Bayesian random parameters model with flexible discrete densities for the regression coefficients is developed, employing a Dirichlet process prior. The approach is illustrated on the Ontario Highway 401, which is one of the busiest North American highways. The results indicate that the proposed model better captures the underlying structure of the data compared to conventional models, improving predictive power examined based on pseudo Bayes factors. Interestingly, the model can identify sites (highway segments, intersections, etc.) with similar risk factor profiles, those that manifest similarity in the heterogeneous effects of their site characteristics (e.g., traffic flow) on traffic safety, providing useful insight towards designing effective countermeasures.
2213-6657
68-80
Heydari, Shahram
0d12a583-a4e8-4888-9e51-a50d312be1e9
Heydari, Shahram
0d12a583-a4e8-4888-9e51-a50d312be1e9

Heydari, Shahram (2018) A flexible discrete density random parameters model for count data: embracing unobserved heterogeneity in highway safety analysis. Analytic Methods in Accident Research, 20, 68-80. (doi:10.1016/j.amar.2018.10.001).

Record type: Article

Abstract

In traffic safety studies, there are almost inevitable concerns about unobserved heterogeneity. As a feasible alternative to current methods, this article proposes a novel crash count model that can address asymmetry and multimodality in the data. Specifically, a Bayesian random parameters model with flexible discrete densities for the regression coefficients is developed, employing a Dirichlet process prior. The approach is illustrated on the Ontario Highway 401, which is one of the busiest North American highways. The results indicate that the proposed model better captures the underlying structure of the data compared to conventional models, improving predictive power examined based on pseudo Bayes factors. Interestingly, the model can identify sites (highway segments, intersections, etc.) with similar risk factor profiles, those that manifest similarity in the heterogeneous effects of their site characteristics (e.g., traffic flow) on traffic safety, providing useful insight towards designing effective countermeasures.

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Flexible RPM Shahram to Melanie (002) - Accepted Manuscript
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More information

Accepted/In Press date: 3 October 2018
e-pub ahead of print date: 3 November 2018
Published date: December 2018

Identifiers

Local EPrints ID: 425039
URI: http://eprints.soton.ac.uk/id/eprint/425039
ISSN: 2213-6657
PURE UUID: e9f97ece-8513-4620-88a4-a0d6bf40467d

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Date deposited: 09 Oct 2018 16:30
Last modified: 07 Oct 2020 05:04

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