  In this research, two-state Markov switching models are proposed to study
accident frequencies and severities. These models assume that there are two
unobserved states of roadway safety, and that roadway entities (e.g., roadway
segments) can switch between these states over time. The states are distinct,
in the sense that in the different states accident frequencies or severities
are generated by separate processes (e.g., Poisson, negative binomial,
multinomial logit). Bayesian inference methods and Markov Chain Monte Carlo
(MCMC) simulations are used for estimation of Markov switching models. To
demonstrate the applicability of the approach, we conduct the following three
studies. In the first study, two-state Markov switching count data models are
considered as an alternative to zero-inflated models for annual accident
frequencies, in order to account for preponderance of zeros typically observed
in accident frequency data. In the second study, two-state Markov switching
Poisson model and two-state Markov switching negative binomial model are
estimated using weekly accident frequencies on selected Indiana interstate
highway segments over a five-year time period. In the third study, two-state
Markov switching multinomial logit models are estimated for severity outcomes
of accidents occurring on Indiana roads over a four-year time period. One of
the most important results found in each of the three studies, is that in each
case the estimated Markov switching models are strongly favored by roadway
safety data and result in a superior statistical fit, as compared to the
corresponding standard (non-switching) models.
