A Copula-Based Approach for Accommodating the Underreporting Effect in Wildlife‒Vehicle Crash Analysis
| Autor: | Yajie Zou, Lingtao Wu, Muhammad Ijaz, Jinjun Tang, Xin Ye, Xinzhi Zhong, Yinhai Wang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Maximum likelihood Geography Planning and Development Copula (linguistics) Negative binomial distribution TJ807-830 maximum likelihood estimation 010501 environmental sciences Management Monitoring Policy and Law wildlife‒vehicle collisions TD194-195 01 natural sciences Renewable energy sources Copula (probability theory) Bayes' theorem 0502 economics and business Statistics GE1-350 0105 earth and related environmental sciences transportation 050210 logistics & transportation Environmental effects of industries and plants Renewable Energy Sustainability and the Environment Estimation theory 05 social sciences Regression analysis Collision Environmental sciences statistical methods mathematical and statistical techniques Motor vehicle crash |
| Zdroj: | Sustainability, Vol 11, Iss 2, p 418 (2019) Sustainability Volume 11 Issue 2 |
| ISSN: | 2071-1050 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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