Citation

Yang X, Zou Y, Wu L, Zhong X, Wang Y, Ijaz M, Peng Y. J. Adv. Transp. 2019; 2019: e3521793.

Copyright

(Copyright © 2019, Institute for Transportation, Publisher John Wiley and Sons)

DOI

10.1155/2019/3521793

PMID

unavailable

Abstract

Two common types of animal-vehicle collision data (reported animal-vehicle collision (AVC) data and carcass removal data) are usually recorded by transportation management agencies. Previous studies have found that these two datasets often demonstrate different characteristics. To accurately identify the higher-risk animal-vehicle collision sites, this study compared the differences in hotspot identification and the effect of explanation variables between carcass removal and reported AVCs. To complete the objective, both the Negative Binomial (NB) model and the generalized Negative Binomial (GNB) are applied in calculating the Empirical Bayesian (EB) estimates using the animal collision data collected on ten highways in Washington State. The important findings can be summarized as follows.

( 1 )

The explanatory variables have different effects on the occurrence of carcass removal data and reported AVC data.

(2)

The ranking results from EB estimates when using carcass removal data and reported AVC data differ significantly.

(3)

The results of hotspot identification are different between carcass removal data and reported AVC data. However, the ranking results of GNB models are better than those of NB models in terms of consistency. Thus, transportation management agencies should be cautious when using either carcass removal data or reported AVC data to identify hotspots.

Language: en