A copula-based approach for accommodating the underreporting effect in wildlife-vehicle crash analysis

Yajie Zou, Xinzhi Zhong, Jinjun Tang, Xin Ye, Lingtao Wu, Muhammad Ijaz, Yinhai Wang

Research output: Contribution to journalArticle

24 Scopus citations

Abstract

Wildlife-vehicle collision (WVC) data usually contain two types: the reportedWVCdata 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.

Original languageEnglish (US)
Article number418
JournalSustainability (Switzerland)
Volume11
Issue number2
DOIs
StatePublished - Jan 15 2019
Externally publishedYes

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Keywords

  • Mathematical and statistical techniques
  • Maximum likelihood estimation
  • Statistical methods
  • Transportation
  • Wildlife-vehicle collisions

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Renewable Energy, Sustainability and the Environment
  • Management, Monitoring, Policy and Law

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