JSM 2011 Online Program

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Abstract Details

Activity Number: 658
Type: Contributed
Date/Time: Thursday, August 4, 2011 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #302528
Title: A Poisson/Gamma Markov Random Field Model to Introduce Spatial Dependence to Crash Frequency Modeling
Author(s): Kristian Schmidt*+ and Alicia Carriquiry
Companies: Iowa State University and Iowa State University
Address: Department of Statistics, Ames, IA, 50011,
Keywords: Gaussian Markov Random Fields ; Poisson Processes ; Traffic Statistics
Abstract:

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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