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Bayesian road safety analysis: Incorporation of past evidence and effect of hyper-prior choice

Bayesian road safety analysis: Incorporation of past evidence and effect of hyper-prior choice
Bayesian road safety analysis: Incorporation of past evidence and effect of hyper-prior choice

Problem This paper aims to address two related issues when applying hierarchical Bayesian models for road safety analysis, namely: (a) how to incorporate available information from previous studies or past experiences in the (hyper) prior distributions for model parameters and (b) what are the potential benefits of incorporating past evidence on the results of a road safety analysis when working with scarce accident data (i.e., when calibrating models with crash datasets characterized by a very low average number of accidents and a small number of sites). Method A simulation framework was developed to evaluate the performance of alternative hyper-priors including informative and non-informative Gamma, Pareto, as well as Uniform distributions. Based on this simulation framework, different data scenarios (i.e., number of observations and years of data) were defined and tested using crash data collected at 3-legged rural intersections in California and crash data collected for rural 4-lane highway segments in Texas. Results This study shows how the accuracy of model parameter estimates (inverse dispersion parameter) is considerably improved when incorporating past evidence, in particular when working with the small number of observations and crash data with low mean. The results also illustrates that when the sample size (more than 100 sites) and the number of years of crash data is relatively large, neither the incorporation of past experience nor the choice of the hyper-prior distribution may affect the final results of a traffic safety analysis. Conclusions As a potential solution to the problem of low sample mean and small sample size, this paper suggests some practical guidance on how to incorporate past evidence into informative hyper-priors. By combining evidence from past studies and data available, the model parameter estimates can significantly be improved. The effect of prior choice seems to be less important on the hotspot identification. Impact on Industry The results show the benefits of incorporating prior information when working with limited crash data in road safety studies.

Bayesian approach, hyper-prior assumptions, past evidence, road safety
0022-4375
31-40
Miranda-Moreno, Luis F.
b61c4a8f-b48e-4c04-b051-3184945da9e4
Heydari, Shahram
0d12a583-a4e8-4888-9e51-a50d312be1e9
Lord, Dominique
968f94d4-a988-4bb2-a3f2-613c487a3f3a
Fu, Liping
239058dc-3019-46af-9488-0bde99e6904a
Miranda-Moreno, Luis F.
b61c4a8f-b48e-4c04-b051-3184945da9e4
Heydari, Shahram
0d12a583-a4e8-4888-9e51-a50d312be1e9
Lord, Dominique
968f94d4-a988-4bb2-a3f2-613c487a3f3a
Fu, Liping
239058dc-3019-46af-9488-0bde99e6904a

Miranda-Moreno, Luis F., Heydari, Shahram, Lord, Dominique and Fu, Liping (2013) Bayesian road safety analysis: Incorporation of past evidence and effect of hyper-prior choice. Journal of Safety Research, 46, 31-40. (doi:10.1016/j.jsr.2013.03.003).

Record type: Article

Abstract

Problem This paper aims to address two related issues when applying hierarchical Bayesian models for road safety analysis, namely: (a) how to incorporate available information from previous studies or past experiences in the (hyper) prior distributions for model parameters and (b) what are the potential benefits of incorporating past evidence on the results of a road safety analysis when working with scarce accident data (i.e., when calibrating models with crash datasets characterized by a very low average number of accidents and a small number of sites). Method A simulation framework was developed to evaluate the performance of alternative hyper-priors including informative and non-informative Gamma, Pareto, as well as Uniform distributions. Based on this simulation framework, different data scenarios (i.e., number of observations and years of data) were defined and tested using crash data collected at 3-legged rural intersections in California and crash data collected for rural 4-lane highway segments in Texas. Results This study shows how the accuracy of model parameter estimates (inverse dispersion parameter) is considerably improved when incorporating past evidence, in particular when working with the small number of observations and crash data with low mean. The results also illustrates that when the sample size (more than 100 sites) and the number of years of crash data is relatively large, neither the incorporation of past experience nor the choice of the hyper-prior distribution may affect the final results of a traffic safety analysis. Conclusions As a potential solution to the problem of low sample mean and small sample size, this paper suggests some practical guidance on how to incorporate past evidence into informative hyper-priors. By combining evidence from past studies and data available, the model parameter estimates can significantly be improved. The effect of prior choice seems to be less important on the hotspot identification. Impact on Industry The results show the benefits of incorporating prior information when working with limited crash data in road safety studies.

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More information

Accepted/In Press date: 11 March 2013
e-pub ahead of print date: 24 March 2013
Published date: September 2013
Keywords: Bayesian approach, hyper-prior assumptions, past evidence, road safety

Identifiers

Local EPrints ID: 424164
URI: http://eprints.soton.ac.uk/id/eprint/424164
ISSN: 0022-4375
PURE UUID: d7ef5963-1886-45d6-a5d3-41fe3c363b94

Catalogue record

Date deposited: 05 Oct 2018 11:31
Last modified: 07 Oct 2020 00:50

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