  In this study, a two-state Markov switching count-data model is proposed as
an alternative to zero-inflated models to account for the preponderance of
zeros sometimes observed in transportation count data, such as the number of
accidents occurring on a roadway segment over some period of time. For this
accident-frequency case, zero-inflated models assume the existence of two
states: one of the states is a zero-accident count state, in which accident
probabilities are so low that they cannot be statistically distinguished from
zero, and the other state is a normal count state, in which counts can be
non-negative integers that are generated by some counting process, for example,
a Poisson or negative binomial. In contrast to zero-inflated models, Markov
switching models allow specific roadway segments to switch between the two
states over time. An important advantage of this Markov switching approach is
that it allows for the direct statistical estimation of the specific
roadway-segment state (i.e., zero or count state) whereas traditional
zero-inflated models do not. To demonstrate the applicability of this approach,
a two-state Markov switching negative binomial model (estimated with Bayesian
inference) and standard zero-inflated negative binomial models are estimated
using five-year accident frequencies on Indiana interstate highway segments. It
is shown that the Markov switching model is a viable alternative and results in
a superior statistical fit relative to the zero-inflated models.
