This paper introduces a new multivariate approach for jointly modeling crash counts by severity data based on Multivariate Poisson-Lognormal models. Although the crash frequency by severity data are multivariate in nature, they have often been analyzed by modeling each severity level separately without taking into account correlations that exist among different severity levels. The new Multivariate Poisson-Lognormal regression approach can cope with both overdispersion and a fully general correlation structure in the data as opposed to the recently suggested Multivariate Poisson regression approach that allows for neither over-dispersion nor a general correlation structure in the data. The new method is applied to the multivariate crash counts obtained from the intersections in California for 10 years. The results show promise towards the goal of obtaining more accurate estimates by accounting for correlations in the multivariate crash counts and over-dispersion.

No abstract

and Key WordsMultivariate receptor modeling aims to estimate pollution source profiles and the amounts of pollution based on a series of ambient concentrations of multiple chemical species over time. Air pollution data often show temporal dependence due to meteorology and/or background sources. Previous approaches to receptor modeling do not incorporate this dependence. We model dependence in the data using a time series approach so that we can incorporate extra sources of variability in parameter estimation and uncertainty estimation. We estimate parameters using the Markov chain Monte Carlo method, which makes simultaneous estimation of parameters and uncertainties possible.The methods are applied to simulated data and 1990 Atlanta air pollution data. The results show promise towards the goal of accounting for the dependence in the data.

No abstract