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Open AccessArticle

A Copula-Based Approach for Accommodating the Underreporting Effect in Wildlife‒Vehicle Crash Analysis

1
Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China
2
School of Traffic and Transportation Engineering, Key Laboratory of Smart Transport in Hunan Province, Central South University, Changsha 410075, China
3
Texas A&M Transportation Institute, Texas A&M University System, 3135 TAMU College Station, TX 77843-3135, USA
4
Department of Civil and Environmental Engineering, University of Washington, More Hall 133B, Seattle, WA 98195, USA
*
Authors to whom correspondence should be addressed.
Sustainability 2019, 11(2), 418; https://doi.org/10.3390/su11020418
Received: 29 December 2018 / Revised: 8 January 2019 / Accepted: 9 January 2019 / Published: 15 January 2019
(This article belongs to the Section Sustainable Transportation)
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. View Full-Text
Keywords: wildlife‒vehicle collisions; transportation; statistical methods; maximum likelihood estimation; mathematical and statistical techniques wildlife‒vehicle collisions; transportation; statistical methods; maximum likelihood estimation; mathematical and statistical techniques
MDPI and ACS Style

Zou, Y.; Zhong, X.; Tang, J.; Ye, X.; Wu, L.; Ijaz, M.; Wang, Y. A Copula-Based Approach for Accommodating the Underreporting Effect in Wildlife‒Vehicle Crash Analysis. Sustainability 2019, 11, 418.

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