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Bayesian nonparametric modeling in transportation safety studies: Applications in univariate and multivariate settings

Bayesian nonparametric modeling in transportation safety studies: Applications in univariate and multivariate settings
Bayesian nonparametric modeling in transportation safety studies: Applications in univariate and multivariate settings

In transportation safety studies, it is often necessary to account for unobserved heterogeneity and multimodality in data. The commonly used standard or over-dispersed generalized linear models (e.g., negative binomial models) do not fully address unobserved heterogeneity, assuming that crash frequencies follow unimodal exponential families of distributions. This paper employs Bayesian nonparametric Dirichlet process mixture models demonstrating some of their major advantages in transportation safety studies. We examine the performance of the proposed approach using both simulated and real data. We compare the proposed model with other models commonly used in road safety literature including the Poisson-Gamma, random effects, and conventional latent class models. We use pseudo Bayes factors as the goodness-of-fit measure, and also examine the performance of the proposed model in terms of replicating datasets with high proportions of zero crashes. In a multivariate setting, we extend the standard multivariate Poisson-lognormal model to a more flexible Dirichlet process mixture multivariate model. We allow for interdependence between outcomes through a nonparametric random effects density. Finally, we demonstrate how the robustness to parametric distributional assumptions (usually the multivariate normal density) can be examined using a mixture of points model when different (multivariate) outcomes are modeled jointly.

Dirichlet process mixture models, Generalized linear models, Latent class models, Multivariate models, Unobserved heterogeneity
2213-6657
18-34
Heydari, Shahram
0d12a583-a4e8-4888-9e51-a50d312be1e9
Fu, Liping
239058dc-3019-46af-9488-0bde99e6904a
Jopseph, Lawrence
795e0dd1-e7a6-4c29-92a0-560196f5359c
Miranda-Moreno, Luis F.
b61c4a8f-b48e-4c04-b051-3184945da9e4
Heydari, Shahram
0d12a583-a4e8-4888-9e51-a50d312be1e9
Fu, Liping
239058dc-3019-46af-9488-0bde99e6904a
Jopseph, Lawrence
795e0dd1-e7a6-4c29-92a0-560196f5359c
Miranda-Moreno, Luis F.
b61c4a8f-b48e-4c04-b051-3184945da9e4

Heydari, Shahram, Fu, Liping, Jopseph, Lawrence and Miranda-Moreno, Luis F. (2016) Bayesian nonparametric modeling in transportation safety studies: Applications in univariate and multivariate settings. Analytic Methods in Accident Research, 12, 18-34. (doi:10.1016/j.amar.2016.09.001).

Record type: Article

Abstract

In transportation safety studies, it is often necessary to account for unobserved heterogeneity and multimodality in data. The commonly used standard or over-dispersed generalized linear models (e.g., negative binomial models) do not fully address unobserved heterogeneity, assuming that crash frequencies follow unimodal exponential families of distributions. This paper employs Bayesian nonparametric Dirichlet process mixture models demonstrating some of their major advantages in transportation safety studies. We examine the performance of the proposed approach using both simulated and real data. We compare the proposed model with other models commonly used in road safety literature including the Poisson-Gamma, random effects, and conventional latent class models. We use pseudo Bayes factors as the goodness-of-fit measure, and also examine the performance of the proposed model in terms of replicating datasets with high proportions of zero crashes. In a multivariate setting, we extend the standard multivariate Poisson-lognormal model to a more flexible Dirichlet process mixture multivariate model. We allow for interdependence between outcomes through a nonparametric random effects density. Finally, we demonstrate how the robustness to parametric distributional assumptions (usually the multivariate normal density) can be examined using a mixture of points model when different (multivariate) outcomes are modeled jointly.

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More information

Accepted/In Press date: 8 September 2006
e-pub ahead of print date: 8 October 2016
Published date: 1 December 2016
Keywords: Dirichlet process mixture models, Generalized linear models, Latent class models, Multivariate models, Unobserved heterogeneity

Identifiers

Local EPrints ID: 424170
URI: http://eprints.soton.ac.uk/id/eprint/424170
ISSN: 2213-6657
PURE UUID: 33b58e1c-66d2-45fd-8743-91fdc8523605

Catalogue record

Date deposited: 05 Oct 2018 11:31
Last modified: 25 Nov 2021 18:54

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Contributors

Author: Shahram Heydari
Author: Liping Fu
Author: Lawrence Jopseph
Author: Luis F. Miranda-Moreno

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