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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.
Yajie Zou; Xinzhi Zhong; Jinjun Tang; Xin Ye; Lingtao Wu; Muhammad Ijaz; Yinhai Wang. A Copula-Based Approach for Accommodating the Underreporting Effect in Wildlife‒Vehicle Crash Analysis. Sustainability 2019, 11, 418 .
AMA StyleYajie Zou, Xinzhi Zhong, Jinjun Tang, Xin Ye, Lingtao Wu, Muhammad Ijaz, Yinhai Wang. A Copula-Based Approach for Accommodating the Underreporting Effect in Wildlife‒Vehicle Crash Analysis. Sustainability. 2019; 11 (2):418.
Chicago/Turabian StyleYajie 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 11, no. 2: 418.
Hotspot identification (HSID) is a critical part of network-wide safety evaluations. Typical methods for ranking sites are often rooted in using the Empirical Bayes (EB) method to estimate safety from both observed crash records and predicted crash frequency based on similar sites. The performance of the EB method is highly related to the selection of a reference group of sites (i.e., roadway segments or intersections) similar to the target site from which safety performance functions (SPF) used to predict crash frequency will be developed. As crash data often contain underlying heterogeneity that, in essence, can make them appear to be generated from distinct subpopulations, methods are needed to select similar sites in a principled manner. To overcome this possible heterogeneity problem, EB-based HSID methods that use common clustering methodologies (e.g., mixture models, K-means, and hierarchical clustering) to select “similar” sites for building SPFs are developed. Performance of the clustering-based EB methods is then compared using real crash data. Here, HSID results, when computed on Texas undivided rural highway cash data, suggest that all three clustering-based EB analysis methods are preferred over the conventional statistical methods. Thus, properly classifying the road segments for heterogeneous crash data can further improve HSID accuracy.
Yajie Zou; Xinzhi Zhong; John Ash; Ziqiang Zeng; Yinhai Wang; Yanxi Hao; Yichuan Peng. Developing a Clustering-Based Empirical Bayes Analysis Method for Hotspot Identification. Journal of Advanced Transportation 2017, 2017, 1 -9.
AMA StyleYajie Zou, Xinzhi Zhong, John Ash, Ziqiang Zeng, Yinhai Wang, Yanxi Hao, Yichuan Peng. Developing a Clustering-Based Empirical Bayes Analysis Method for Hotspot Identification. Journal of Advanced Transportation. 2017; 2017 ():1-9.
Chicago/Turabian StyleYajie Zou; Xinzhi Zhong; John Ash; Ziqiang Zeng; Yinhai Wang; Yanxi Hao; Yichuan Peng. 2017. "Developing a Clustering-Based Empirical Bayes Analysis Method for Hotspot Identification." Journal of Advanced Transportation 2017, no. : 1-9.