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• W2023131190 startingPage "829" @default.
• W2023131190 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 over time between two unobserved states of roadway safety as a means of accounting for potential unobserved heterogeneity. 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, two-state Markov switching multinomial logit models are estimated for severity outcomes of accidents occurring on Indiana roads over a four-year time period. 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 for a number of roadway classes and accident types. It is found that the more frequent state of roadway safety is correlated with better weather conditions and that the less frequent state is correlated with adverse weather conditions." @default.
• W2023131190 created "2016-06-24" @default.
• W2023131190 creator A5070617875 @default.
• W2023131190 creator A5072872891 @default.
• W2023131190 date "2009-07-01" @default.
• W2023131190 modified "2023-10-02" @default.
• W2023131190 title "Markov switching multinomial logit model: An application to accident-injury severities" @default.
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• W2023131190 doi "https://doi.org/10.1016/j.aap.2009.04.006" @default.
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