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

16 Citations (Scopus)

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

Fingerprint

collision
empirical method
modeling
regression
analysis
effect
wildlife
vehicle
Parameter estimation
road
method
removal
Statistical Models

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

Cite this

A copula-based approach for accommodating the underreporting effect in wildlife-vehicle crash analysis. / Zou, Yajie; Zhong, Xinzhi; Tang, Jinjun; Ye, Xin; Wu, Lingtao; Ijaz, Muhammad; Wang, Yinhai.

In: Sustainability (Switzerland), Vol. 11, No. 2, 418, 15.01.2019.

Research output: Contribution to journalArticle

Zou, Yajie ; Zhong, Xinzhi ; Tang, Jinjun ; Ye, Xin ; Wu, Lingtao ; Ijaz, Muhammad ; Wang, Yinhai. / A copula-based approach for accommodating the underreporting effect in wildlife-vehicle crash analysis. In: Sustainability (Switzerland). 2019 ; Vol. 11, No. 2.
@article{dcdba0a6fa534487b4ca8dbe8a997695,
title = "A copula-based approach for accommodating the underreporting effect in wildlife-vehicle crash analysis",
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.",
keywords = "Mathematical and statistical techniques, Maximum likelihood estimation, Statistical methods, Transportation, Wildlife-vehicle collisions",
author = "Yajie Zou and Xinzhi Zhong and Jinjun Tang and Xin Ye and Lingtao Wu and Muhammad Ijaz and Yinhai Wang",
year = "2019",
month = "1",
day = "15",
doi = "10.3390/su11020418",
language = "English (US)",
volume = "11",
journal = "Sustainability",
issn = "2071-1050",
publisher = "Mary Ann Liebert Inc.",
number = "2",

}

TY - JOUR

T1 - A copula-based approach for accommodating the underreporting effect in wildlife-vehicle crash analysis

AU - Zou, Yajie

AU - Zhong, Xinzhi

AU - Tang, Jinjun

AU - Ye, Xin

AU - Wu, Lingtao

AU - Ijaz, Muhammad

AU - Wang, Yinhai

PY - 2019/1/15

Y1 - 2019/1/15

N2 - 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.

AB - 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.

KW - Mathematical and statistical techniques

KW - Maximum likelihood estimation

KW - Statistical methods

KW - Transportation

KW - Wildlife-vehicle collisions

UR - http://www.scopus.com/inward/record.url?scp=85059986669&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85059986669&partnerID=8YFLogxK

U2 - 10.3390/su11020418

DO - 10.3390/su11020418

M3 - Article

AN - SCOPUS:85059986669

VL - 11

JO - Sustainability

JF - Sustainability

SN - 2071-1050

IS - 2

M1 - 418

ER -