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

Author

Listed:
  • Yajie Zou

    (Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China)

  • Xinzhi Zhong

    (Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China)

  • Jinjun Tang

    (School of Traffic and Transportation Engineering, Key Laboratory of Smart Transport in Hunan Province, Central South University, Changsha 410075, China)

  • Xin Ye

    (Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China)

  • Lingtao Wu

    (Texas A&M Transportation Institute, Texas A&M University System, 3135 TAMU College Station, TX 77843-3135, USA)

  • Muhammad Ijaz

    (Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China)

  • Yinhai Wang

    (Department of Civil and Environmental Engineering, University of Washington, More Hall 133B, Seattle, WA 98195, USA)

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.

Suggested Citation

  • Yajie Zou & Xinzhi Zhong & Jinjun Tang & Xin Ye & Lingtao Wu & Muhammad Ijaz & Yinhai Wang, 2019. "A Copula-Based Approach for Accommodating the Underreporting Effect in Wildlife‒Vehicle Crash Analysis," Sustainability, MDPI, vol. 11(2), pages 1-13, January.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:2:p:418-:d:197798
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    References listed on IDEAS

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