Abstract
Wildlife‒vehicle collision (WVC) data usually contain two types: the reported WVC data and carcass removal data. Previous studies often found a discrepancy between the number of reported WVC and carcass removal data, and the quality of both datasets is affected by underreporting. Underreporting means the number of WVCs is not fully recorded in the database; neglecting the underreporting in WVC data may result in biased parameter estimation results. In this study, a copula regression model linking wildlife‒vehicle collisions and the underreporting outcome was proposed to consider the underreporting in WVC data. The WVC data collected from 10 highways in Washington State were analyzed using the copula regression model and the Negative Binomial (NB) model. The main findings from this study are as follows: (1) the Gaussian copula model can provide different modeling results when compared with the conventional modeling approach; (2) the hotspot identification results indicate that the Gaussian copula-based Empirical Bayes (EB) method can more accurately identify hotspots than the NB-based EB method. Thus, the proposed copula model may be a better alternative to the conventional NB model for modeling underreported WVC data.
Highlights
Wildlife-vehicle collisions (WVC) are a prominent road safety issue as highway expansion projects in natural areas endanger the safe sharing of highways between vehicles and wildlife, which is a great potential threat to humans and wildlife [1,2]
Where μi is the expected number of wildlife-vehicle collisions at site i; Li represents the length of roadway segment in miles for site i; Fi is the average daily traffic over five years traveling on site i; x2i, . . . , xmi are the explanatory variables included in the functional form at site i; β = (β0, β1, β2, β3, . . . , βm) are the estimated coefficients; and m is the number of explanatory variables
In the following analysis of the parameter estimation results, we mainly focus on comparing the effects of different explanatory variables on underreporting outcome and reported wildlife-vehicle collisions using the independence copula and Gaussian copula
Summary
Wildlife-vehicle collisions (WVC) are a prominent road safety issue as highway expansion projects in natural areas endanger the safe sharing of highways between vehicles and wildlife, which is a great potential threat to humans and wildlife [1,2]. In order to mitigate the wildlife-vehicle collision risk and develop effective countermeasures, statistical regression models are frequently applied by transportation safety researchers to quantify the effect of explanatory factors on WVCs [10]. Gkritza et al [22] applied the Poisson regression model and the NB regression model to estimate the effect of identified factors on the frequency and severity of WVCs. Using reported WVC data, a stepwise logistic regression model was applied to identify the significant factors at a landscape scale and recognize the points of high collision risk [23,24,25]. Lao et al [28] applied a diagonal inflated bivariate Poisson regression to model reported WVCs and carcass data jointly and found a correlation between the two datasets. Murphy and Xia [30] found the positive effect of coverage degree of vegetation on WVCs by using a hierarchical Bayesian model
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