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

Nataliya V Malyshkina¹, Fred L Mannering

¹School of Civil Engineering, 550 Stadium Mall Drive, Purdue University, West Lafayette, IN 47907, USA. nmalyshk@purdue.edu

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A new Markov switching count-data model offers a better way to analyze transportation accident data than traditional zero-inflated models. This approach accurately identifies roadway segments in zero-accident or normal-count states.

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Area of Science:

Transportation safety and statistical modeling.
Development of advanced count-data models.

Background:

Zero-inflated models are common for analyzing transportation count data, like accident frequencies.
These models face criticism for their assumptions about zero-accident states.
Existing models struggle to dynamically account for state changes in accident occurrence.

Purpose of the Study:

To propose a two-state Markov switching count-data model as an alternative to zero-inflated models.
To address limitations of zero-inflated models in modeling transportation count data.
To improve the statistical fit and interpretability of accident frequency models.

Main Methods:

A two-state Markov switching negative binomial model was developed.
Bayesian inference was used for model estimation.
The proposed model was compared against standard zero-inflated negative binomial models.

Main Results:

The Markov switching model demonstrated a superior statistical fit to the data compared to zero-inflated models.
The model successfully estimated the specific state (zero-accident or normal-count) of roadway segments.
The approach proved viable for analyzing five-year accident frequencies on highway segments.

Conclusions:

The proposed Markov switching model is a statistically superior and viable alternative for analyzing transportation count data with excess zeros.
This model offers improved state estimation and overcomes criticisms associated with traditional zero-inflated models.
The findings have implications for more accurate transportation safety analysis and infrastructure management.