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Abstract Details
Activity Number:
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658
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Type:
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Contributed
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Date/Time:
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Thursday, August 4, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #302528 |
Title:
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A Poisson/Gamma Markov Random Field Model to Introduce Spatial Dependence to Crash Frequency Modeling
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Author(s):
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Kristian Schmidt*+ and Alicia Carriquiry
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Companies:
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Iowa State University and Iowa State University
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Address:
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Department of Statistics, Ames, IA, 50011,
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Keywords:
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Gaussian Markov Random Fields ;
Poisson Processes ;
Traffic Statistics
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Abstract:
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Markov Random Field models are used in a variety of applications. In their 1997 paper, Wolpert and Ickstadt describe some fundamental work in the area of Poisson/Gamma Random Fields, using hybrid Gibbs/Metropolis schemes to implement MCMC methods.
Enhancing their ideas by incorporating covariate information into the conditional mean structure of the random field, we propose a Poisson/Gamma Markov Random Field approach to model crash frequencies that are not only spatially dependent, but are also highly correlated with additional explanatory variables.
A further challenge will be the definition of a sensible neighborhood structure. In traffic modeling, an isotropic view of spatial dependence is usually not justifiable, so we propose a structure that accounts for whether or not two intersections share the same road and also models dependence as a decreasing function of the distance between two intersections.
This work is based on a crash frequencies for the city of Ames, Iowa and is in cooperation with the Iowa Department of Transportation.
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