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Computational Bayesian statistics in transportation modeling: from road safety analysis to discrete choice

Computational Bayesian statistics in transportation modeling: from road safety analysis to discrete choice
Computational Bayesian statistics in transportation modeling: from road safety analysis to discrete choice

In this paper, we review both the fundamentals and the expansion of computational Bayesian econometrics and statistics applied to transportation modeling problems in road safety analysis and travel behavior. Whereas for analyzing accident risk in transportation networks there has been a significant increase in the application of hierarchical Bayes methods, in transportation choice modeling, the use of Bayes estimators is rather scarce. We thus provide a general discussion of the benefits of using Bayesian Markov chain Monte Carlo methods to simulate answers to the problems of point and interval estimation and forecasting, including the use of the simulated posterior for building predictive distributions and constructing credible intervals for measures such as the value of time. Although there is the general idea that going Bayesian is just another way of finding an equivalent to frequentist results, in practice Bayes estimators have the potential of outperforming frequentist estimators and, at the same time, may offer more information. Additionally, Bayesian inference is particularly interesting for small samples and weakly identified models.

Bayesian statistics, discrete choice, MCMC, road safety
0144-1647
570-592
Daziano, Ricardo A.
8b8d0202-9f63-40f3-8d41-5774ca06da3d
Miranda-Moreno, Luis
b61c4a8f-b48e-4c04-b051-3184945da9e4
Heydari, Shahram
0d12a583-a4e8-4888-9e51-a50d312be1e9
Daziano, Ricardo A.
8b8d0202-9f63-40f3-8d41-5774ca06da3d
Miranda-Moreno, Luis
b61c4a8f-b48e-4c04-b051-3184945da9e4
Heydari, Shahram
0d12a583-a4e8-4888-9e51-a50d312be1e9

Daziano, Ricardo A., Miranda-Moreno, Luis and Heydari, Shahram (2013) Computational Bayesian statistics in transportation modeling: from road safety analysis to discrete choice. Transport Reviews, 33 (5), 570-592. (doi:10.1080/01441647.2013.829890).

Record type: Article

Abstract

In this paper, we review both the fundamentals and the expansion of computational Bayesian econometrics and statistics applied to transportation modeling problems in road safety analysis and travel behavior. Whereas for analyzing accident risk in transportation networks there has been a significant increase in the application of hierarchical Bayes methods, in transportation choice modeling, the use of Bayes estimators is rather scarce. We thus provide a general discussion of the benefits of using Bayesian Markov chain Monte Carlo methods to simulate answers to the problems of point and interval estimation and forecasting, including the use of the simulated posterior for building predictive distributions and constructing credible intervals for measures such as the value of time. Although there is the general idea that going Bayesian is just another way of finding an equivalent to frequentist results, in practice Bayes estimators have the potential of outperforming frequentist estimators and, at the same time, may offer more information. Additionally, Bayesian inference is particularly interesting for small samples and weakly identified models.

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More information

Accepted/In Press date: 23 July 2013
e-pub ahead of print date: 5 September 2013
Published date: September 2013
Keywords: Bayesian statistics, discrete choice, MCMC, road safety

Identifiers

Local EPrints ID: 424163
URI: http://eprints.soton.ac.uk/id/eprint/424163
ISSN: 0144-1647
PURE UUID: 2990698e-518a-42bb-a55e-7c3a6c95aaec

Catalogue record

Date deposited: 05 Oct 2018 11:31
Last modified: 15 Oct 2018 16:30

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