  In this paper, two-state Markov switching models are proposed to study
accident frequencies. These models assume that there are two unobserved states
of roadway safety, and that roadway entities (roadway segments) can switch
between these states over time. The states are distinct, in the sense that in
the different states accident frequencies are generated by separate counting
processes (by separate Poisson or negative binomial processes). To demonstrate
the applicability of the approach presented herein, two-state Markov switching
negative binomial models are estimated using five-year accident frequencies on
Indiana interstate highway segments. Bayesian inference methods and Markov
Chain Monte Carlo (MCMC) simulations are used for model estimation. The
estimated Markov switching models result in a superior statistical fit relative
to the standard (single-state) negative binomial model. It is found that the
more frequent state is safer and it is correlated with better weather
conditions. The less frequent state is found to be less safe and to be
correlated with adverse weather conditions.
