Xinzhi Zhong
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Xinzhi Zhong has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Jinjun Tang
Smart Transport Key Laboratory of Hunan Province, School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
1 shared publication - Lingtao Wu
Texas A&M Transportation Institute, Texas A&M University System, College Station, TX
1 shared publication - Muhammad Ijaz
School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China
1 shared publication - Xin Ye
Tongji University
1 shared publication - Yajie Zou
Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University
1 shared publication - Yinhai Wang
University of Washington
1 shared publication
Latest Publications
- Open Access
- 15 January 2019
- Published by MDPI AG in Sustainability
- Vol. 11 (2) , 418
A Copula-Based Approach for Accommodating the Underreporting Effect in Wildlife‒Vehicle Crash Analysis
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.
- 15 January 2019
- Published by MDPI AG in Sustainability
- Vol. 11 (2) , 418
Open AccessA Copula-Based Approach for Accommodating the Underreporting Effect in Wildlife‒Vehicle Crash Analysis
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.