Accident Count Model Based on Multiyear Cross-Sectional Roadway Data with Serial Correlation

Abstract

The use of the negative multinomial model to form a predictive model of median crossover accident frequencies with a multiyear panel of crosssectional roadway data with a roadway section-specific serial correlation across time was explored. The negative multinomial model specification is compared with previous research, which used the same database but which also used negative binomial and random-effects negative binomial count models. If there is no section-specific correlation in the panel, the negative multinomial model becomes equivalent to the negative binomial. The differences in the estimation results between those models show that such a correlation exists in the data. The results show that the negative multinomial significantly outperforms the negative binomial and the random-effects negative binomial in terms of fit, with a statistically significantly higher likelihood at convergence. The signs of the coefficients were similar in all models; when the signs differed, the negative multinomial model results were more intuitive. Overall, the analysis supports the use of the negative multinomial count model to estimate median crossover accident frequency models that are based on panel data.