Accounting for under-reporting in wildlife–vehicle collision hotspot identification using copulas and Bayesian model averaging
Moeinaddini, Amin ; Zhang, Tianren ; D’Agostino, Carmelo LU
; Xie, Yuanchang
and Zou, Yajie
(2026)
In Accident Analysis and Prevention
233.
- Abstract
Wildlife-vehicle collisions (WVCs) pose significant risks to highway safety and wildlife populations, leading to considerable economic losses. However, efforts to systematically characterize and mitigate WVCs are often constrained by substantial under-reporting in official crash record databases. This study integrates police-reported crash data with carcass removal records from 7041 road segments across Washington State, USA, to provide a more accurate picture of true WVC occurrence and risk factors. Building on prior copula-based approaches, we propose a novel hybrid copula-based framework to jointly model reported WVC frequencies and under-reporting probabilities, incorporating Bayesian Model Averaging (BMA). The hybrid models link a... (More)
Wildlife-vehicle collisions (WVCs) pose significant risks to highway safety and wildlife populations, leading to considerable economic losses. However, efforts to systematically characterize and mitigate WVCs are often constrained by substantial under-reporting in official crash record databases. This study integrates police-reported crash data with carcass removal records from 7041 road segments across Washington State, USA, to provide a more accurate picture of true WVC occurrence and risk factors. Building on prior copula-based approaches, we propose a novel hybrid copula-based framework to jointly model reported WVC frequencies and under-reporting probabilities, incorporating Bayesian Model Averaging (BMA). The hybrid models link a binary logit under-reporting with the count submodel using Gaussian and Student-t copulas to capture dependence. Key findings reveal that under-reporting is less likely near wolverine habitat, on state routes, in low ecological value areas, and on multi-lane roads, but more likely near white-tailed deer habitat and in segments of highest ecological value. Reported WVC frequency increases with Annual Average Daily Traffic (AADT), wider shoulders, proximity to large mammal habitat, and higher speed limits, while it decreases with higher truck percentages and more lanes. Empirical Bayes (EB) hotspot identification (HSID) shows copula-based models outperform independent specifications, capturing 2.8–4.4% more true collisions at selective thresholds (2–5%), with further gains from BMA. Residual diagnostics further confirm the framework’s robustness. Hybrid BMA-copula specifications substantially reduce heteroscedasticity and alleviate remaining endogeneity bias compared to Gaussian, no-copula. These results provide transportation agencies with clearer guidance for prioritizing mitigation measures, such as wildlife crossings, fencing, and speed management, on high-risk segments, particularly two-lane roads in ecologically sensitive areas near large mammal habitats, while accounting for systematic under-reporting biases linked to specific species and roadway types.
(Less)
- author
- Moeinaddini, Amin
; Zhang, Tianren
; D’Agostino, Carmelo
LU
; Xie, Yuanchang
and Zou, Yajie
- organization
- publishing date
- 2026-08
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- And hotspot identification, Bayesian model averaging, Hybrid copula-based model, Under-reporting collisions, Wildlife-vehicle collisions
- in
- Accident Analysis and Prevention
- volume
- 233
- article number
- 108583
- publisher
- Elsevier
- external identifiers
-
- scopus:105039081869
- pmid:42114245
- ISSN
- 0001-4575
- DOI
- 10.1016/j.aap.2026.108583
- language
- English
- LU publication?
- yes
- id
- 0846378a-268a-4ae2-a6a8-2dcf8878834b
- date added to LUP
- 2026-08-17 09:36:36
- date last changed
- 2026-09-14 12:38:37
@article{0846378a-268a-4ae2-a6a8-2dcf8878834b,
abstract = {{<p>Wildlife-vehicle collisions (WVCs) pose significant risks to highway safety and wildlife populations, leading to considerable economic losses. However, efforts to systematically characterize and mitigate WVCs are often constrained by substantial under-reporting in official crash record databases. This study integrates police-reported crash data with carcass removal records from 7041 road segments across Washington State, USA, to provide a more accurate picture of true WVC occurrence and risk factors. Building on prior copula-based approaches, we propose a novel hybrid copula-based framework to jointly model reported WVC frequencies and under-reporting probabilities, incorporating Bayesian Model Averaging (BMA). The hybrid models link a binary logit under-reporting with the count submodel using Gaussian and Student-t copulas to capture dependence. Key findings reveal that under-reporting is less likely near wolverine habitat, on state routes, in low ecological value areas, and on multi-lane roads, but more likely near white-tailed deer habitat and in segments of highest ecological value. Reported WVC frequency increases with Annual Average Daily Traffic (AADT), wider shoulders, proximity to large mammal habitat, and higher speed limits, while it decreases with higher truck percentages and more lanes. Empirical Bayes (EB) hotspot identification (HSID) shows copula-based models outperform independent specifications, capturing 2.8–4.4% more true collisions at selective thresholds (2–5%), with further gains from BMA. Residual diagnostics further confirm the framework’s robustness. Hybrid BMA-copula specifications substantially reduce heteroscedasticity and alleviate remaining endogeneity bias compared to Gaussian, no-copula. These results provide transportation agencies with clearer guidance for prioritizing mitigation measures, such as wildlife crossings, fencing, and speed management, on high-risk segments, particularly two-lane roads in ecologically sensitive areas near large mammal habitats, while accounting for systematic under-reporting biases linked to specific species and roadway types.</p>}},
author = {{Moeinaddini, Amin and Zhang, Tianren and D’Agostino, Carmelo and Xie, Yuanchang and Zou, Yajie}},
issn = {{0001-4575}},
keywords = {{And hotspot identification; Bayesian model averaging; Hybrid copula-based model; Under-reporting collisions; Wildlife-vehicle collisions}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Accident Analysis and Prevention}},
title = {{Accounting for under-reporting in wildlife–vehicle collision hotspot identification using copulas and Bayesian model averaging}},
url = {{http://dx.doi.org/10.1016/j.aap.2026.108583}},
doi = {{10.1016/j.aap.2026.108583}},
volume = {{233}},
year = {{2026}},
}