Markov switching multinomial logit model: an application to accident injury severities

TL;DR

This paper introduces two-state Markov switching multinomial logit models to better understand accident injury severities by capturing unobserved state changes in roadway safety, demonstrating improved fit over traditional models.

Contribution

The paper develops and applies a novel Markov switching multinomial logit model for accident severity analysis, incorporating unobserved state transitions in roadway safety.

Findings

Better statistical fit than standard models

Safety states correlate with weather conditions

Model captures unobserved safety dynamics

Abstract

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.