Recovering Hidden Wildlife--Vehicle Collisions from Existing Crash Data: A Cross-State Framework for Detection, Prevalence Estimation, and Under-Reporting Audits
Wildlife--vehicle collisions (WVCs) impose a large toll on U.S. highways, yet state crash databases often fail to record animal involvement, leaving agencies uncertain of the true missing count. This study evaluates whether existing crash records can identify individual WVCs and estimate overall unrecorded rates. We analyzed 4.9 million crash records from Colorado, Utah, Iowa, and Illinois (2007-2026). Each state's schema underwent a label-leakage screen to exclude explicit animal fields before harmonization into a shared vocabulary. Calibrated gradient-boosted classifiers were trained per state, interpreted using Shapley additive explanations, and evaluated for prevalence estimation via Adjusted Classify-and-Count and Maximum-Likelihood techniques across in-domain, cross-state, and synthetic reporting-gap scenarios. Detection performance depends heavily on record completeness rather than collision rarity. At ~4% prevalence, feature-rich Utah achieved an average precision of 0.88 versus 0.64 for feature-poor Colorado, and outperformed Iowa despite Iowa's 3x higher WVC rate. Within-state richness curves confirm feature depth drives accuracy. While classifiers ranked records accurately across states, probability calibration did not transfer directly. However, in-domain prevalence estimation recovered true collision rates within 0.001. This work shifts WVC analysis from spatial clustering to record-level detection and missing-data estimation using legacy datasets. Transportation agencies can retrospectively flag unrecorded WVCs, quantify total collision burden to support mitigation funding, and identify high-value reporting fields prior to revising crash-report manuals.