Accident analysis with aggregated data: The random parameters negative binomial panel count data model
Highlights
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Roadway constructions including railway in Turkey are becoming increasingly important and attractive for both local and foreign investors.
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Each year more than three thousand people die and 200 thousand people get serious injuries in traffic accidents.
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Recently, count data provide more accurate tools for planners and decision makers to conduct proactive road safety planning.
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Year dummy and interaction variables used here provide more direct insightful policy implications.
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The correlated random parameters model used here will likely shed light on the frequencies of crashes across cities in Turkey.
Abstract
We analyzed factors affecting the frequency of accident counts in 81 cities over a three-year period (2008–2010) with monthly data using random-parameters negative binomial panel count data models. Among models considered, the random parameters model with the correlated coefficients outperformed the other models and was found to fit the data best with almost a perfect prediction of the conditional mean level. Most of variables used to control for the variation in the frequency of accident counts play crucial role with higher significance levels. Also, marginal effects and elasticity estimates were derived to get more insight into the effects of percent and unit changes in the dependent variable in response to changes in the exogenous variables.
Keywords
Accidents; Aggregated data; Negative binomial regression; Panel data; Random-parameters; Turkey