JSM 2005 - Toronto

Abstract #304801

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 250
Type: Invited
Date/Time: Tuesday, August 9, 2005 : 10:30 AM to 12:20 PM
Sponsor: Section on Risk Analysis
Abstract - #304801
Title: Bayesian Multivariate Spatial Models for Roadway Traffic Crash Mapping
Author(s): Joon Jin Song*+ and Malay Ghosh and Shaw-Pin Miaou and Bani Mallick
Companies: University of Massachusetts and University of Florida and Texas Transportation Institute and Texas A&M University
Address: Department of Mathematics and Statistics, Amherst, MA, 01003-9305, United States
Keywords: Hierarchical Models ; Markov chain Monte Carlo ; Multivariate CAR ; Posterior Propriety
Abstract:

Transportation-related injuries and deaths cause major health problems in the United States. Most of the studies on traffic crash risk in the highway safety community ignore the spatial dependence among the crash data. A very recent exception is Miaou et al. (2003), who studied the geographical pattern of accidents in Texas. We generalize the univariate spatial conditional autoregressive (CAR) model to several multivariate cases and analyze traffic crash data with multivariate measurements, which are several types of crashes using the proposed multivariate spatial models. These multivariate spatial models are necessary to analyze more than one type of crash simultaneously, as a number of crashes may share the same set of risk factors. A full-Bayesian approach is taken and a general theorem for each case is provided to ensure the posterior density is proper under a noninformative prior. The performance of different multivariate models is compared using deviance information criterion (DIC). Markov chain Monte Carlo (MCMC) techniques are used for computation.

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