Model crash frequency at highway–railroad grade crossings using negative binomial regression

Abstract

Despite the fact that traffic collisions at highway–railroad grade crossings (HRGXs) are rare events, the impact of HRGX crashes is nevertheless more severe than highway crashes. Empirical studies show that traffic collisions at HRGXs are mainly attributed to railway-related and/or highway-related characteristics, particularly drivers’ abnormal behavior, driving around, or through an HRGX. These factors have different effects on crash likelihood (i.e., the number of traffic collisions or crash frequency) at an HRGX. To explore the causal relationship between crash frequency and the factors related to railroad and highway systems, we used a negative binomial regression model to identify the factors that are statistically significantly associated with traffic collisions at HRGXs, and conducted relevant sensitivity analyses to investigate the marginal effect of daily highway traffic on changes in crash frequency. The empirical study shows that the number of daily trains, the number of tracks, highway separation, annual averaged daily traffic (AADT), and crossing length had statistically significant effects on the mean number of traffic collisions (all p-values ≤ 0.0487). Further, the marginal effect of the AADT on the change of crash frequency indicates that crash likelihood monotonically increases with the increase of AADT.

Keywords:

• crash frequency
• highway–railroad grade crossing
• negative binomial regression
• marginal effect

Acknowledgments

The authors are grateful for the insightful comments given by two anonymous reviewers, which significantly improve the clarity of the article. This research was partially supported by grants NSC 95-2415-H-006-014-MY3 (S.R. Hu and C.K. Lee) from the National Science Council, Taiwan, and by grant no. UL1 RR024146 from the National Center for Research Resources, a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research (C.S. Li).