  In this study, two-state Markov switching multinomial logit models are
proposed for statistical modeling of accident injury severities. These models
assume Markov switching in time between two unobserved states of roadway
safety. The states are distinct, in the sense that in different states accident
severity outcomes are generated by separate multinomial logit processes. To
demonstrate the applicability of the approach presented herein, two-state
Markov switching multinomial logit models are estimated for severity outcomes
of accidents occurring on Indiana roads over a four-year time interval.
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) multinomial
logit models. It is found that the more frequent state of roadway safety is
correlated with better weather conditions. The less frequent state is found to
be correlated with adverse weather conditions.
