Comparative Analysis of the Reported Animal-Vehicle Collisions Data and Carcass Removal Data for Hotspot Identification

Xiaoxue Yang,Muhammad Ijaz,Yinhai Wang,Yichuan Peng,Yajie Zou,Xinzhi Zhong,Lingtao Wu

doi:10.1155/2019/3521793

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.

Highlights

Animal-vehicle collisions (AVCs) have always been one of research frontiers and hot topics
The reported AVC and the carcass removal are compared by the Empirical Bayesian (EB) method based on the Negative Binomial (NB) model and generalized Negative Binomial (GNB) model
The prediction is usually based on safety performance functions (SPFs), which commonly where μ∧푖=predicted number of crashes per year for site i estimated by EB method; μ∧푖= predicted number of crashes per year for site i expected by the SPF; w푖 = 1/(1 + αμ∧푖)= weight factor defined as a function of μ∧푖 and dispersion parameter α; and y푖=observed number of crashes per year at site i

Summary

Introduction

Animal-vehicle collisions (AVCs) have always been one of research frontiers and hot topics. It was estimated that the number of AVCs per year exceeded 1 million in the 1990s [2]. There are about 155-211 deaths, 13,713-29,000 injuries, and 1 billion dollars property loss per year caused by AVCs [2,3,4,5]. The fact that the average number of fatal AVCs was increasing year by year was inferred from the record from the NHTSA Fatality Analysis Reporting System (FARS) [4]. Previous studies found that the number of wild animals decreased significantly due to AVCs [6,7,8], and billions of wild animals died annually in the collision with vehicle and other types of transportation mode [9, 10]

Objectives

Methods

Results

Conclusion