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Bayesian methodology to estimate and update safety performance functions under limited data conditions: A sensitivity analysis

Bayesian methodology to estimate and update safety performance functions under limited data conditions: A sensitivity analysis
Bayesian methodology to estimate and update safety performance functions under limited data conditions: A sensitivity analysis

In road safety studies, decision makers must often cope with limited data conditions. In such circumstances, the maximum likelihood estimation (MLE), which relies on asymptotic theory, is unreliable and prone to bias. Moreover, it has been reported in the literature that (a) Bayesian estimates might be significantly biased when using non-informative prior distributions under limited data conditions, and that (b) the calibration of limited data is plausible when existing evidence in the form of proper priors is introduced into analyses. Although the Highway Safety Manual (2010) (HSM) and other research studies provide calibration and updating procedures, the data requirements can be very taxing. This paper presents a practical and sound Bayesian method to estimate and/or update safety performance function (SPF) parameters combining the information available from limited data with the SPF parameters reported in the HSM. The proposed Bayesian updating approach has the advantage of requiring fewer observations to get reliable estimates. This paper documents this procedure. The adopted technique is validated by conducting a sensitivity analysis through an extensive simulation study with 15 different models, which include various prior combinations. This sensitivity analysis contributes to our understanding of the comparative aspects of a large number of prior distributions. Furthermore, the proposed method contributes to unification of the Bayesian updating process for SPFs. The results demonstrate the accuracy of the developed methodology. Therefore, the suggested approach offers considerable promise as a methodological tool to estimate and/or update baseline SPFs and to evaluate the efficacy of road safety countermeasures under limited data conditions.

Full Bayes road safety analysis, Index of treatment effectiveness, Prior distribution, SPF parameter estimation and update
0001-4575
41-51
Heydari, Shahram
0d12a583-a4e8-4888-9e51-a50d312be1e9
Miranda-Moreno, Luis F.
b61c4a8f-b48e-4c04-b051-3184945da9e4
Lord, Dominique
968f94d4-a988-4bb2-a3f2-613c487a3f3a
Fu, Liping
239058dc-3019-46af-9488-0bde99e6904a
Heydari, Shahram
0d12a583-a4e8-4888-9e51-a50d312be1e9
Miranda-Moreno, Luis F.
b61c4a8f-b48e-4c04-b051-3184945da9e4
Lord, Dominique
968f94d4-a988-4bb2-a3f2-613c487a3f3a
Fu, Liping
239058dc-3019-46af-9488-0bde99e6904a

Heydari, Shahram, Miranda-Moreno, Luis F., Lord, Dominique and Fu, Liping (2014) Bayesian methodology to estimate and update safety performance functions under limited data conditions: A sensitivity analysis. Accident Analysis and Prevention, 64, 41-51. (doi:10.1016/j.aap.2013.11.001).

Record type: Article

Abstract

In road safety studies, decision makers must often cope with limited data conditions. In such circumstances, the maximum likelihood estimation (MLE), which relies on asymptotic theory, is unreliable and prone to bias. Moreover, it has been reported in the literature that (a) Bayesian estimates might be significantly biased when using non-informative prior distributions under limited data conditions, and that (b) the calibration of limited data is plausible when existing evidence in the form of proper priors is introduced into analyses. Although the Highway Safety Manual (2010) (HSM) and other research studies provide calibration and updating procedures, the data requirements can be very taxing. This paper presents a practical and sound Bayesian method to estimate and/or update safety performance function (SPF) parameters combining the information available from limited data with the SPF parameters reported in the HSM. The proposed Bayesian updating approach has the advantage of requiring fewer observations to get reliable estimates. This paper documents this procedure. The adopted technique is validated by conducting a sensitivity analysis through an extensive simulation study with 15 different models, which include various prior combinations. This sensitivity analysis contributes to our understanding of the comparative aspects of a large number of prior distributions. Furthermore, the proposed method contributes to unification of the Bayesian updating process for SPFs. The results demonstrate the accuracy of the developed methodology. Therefore, the suggested approach offers considerable promise as a methodological tool to estimate and/or update baseline SPFs and to evaluate the efficacy of road safety countermeasures under limited data conditions.

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More information

Accepted/In Press date: 1 November 2013
e-pub ahead of print date: 13 November 2013
Published date: March 2014
Keywords: Full Bayes road safety analysis, Index of treatment effectiveness, Prior distribution, SPF parameter estimation and update

Identifiers

Local EPrints ID: 424166
URI: http://eprints.soton.ac.uk/id/eprint/424166
ISSN: 0001-4575
PURE UUID: c3bf51cd-689d-41eb-9a1c-3b5791a2f8b5

Catalogue record

Date deposited: 05 Oct 2018 11:31
Last modified: 07 Oct 2020 00:43

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