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

Zero-state Markov switching count-data models: an empirical assessment