Transportation corridors fragment habitats and hinder animal movement, yet wildlife crossing placement often relies on subjective resistance-based models that overstate precision in predicting movement paths. We developed a replicable, data-driven GIS framework that integrates focal-species ecology and existing infrastructure to prioritize: (1) modifying existing structures for wildlife use, and (2) identifying “gaps” where new crossings are needed. The framework combines multiple datasets (i.e., species occurrences, habitat suitability, existing culverts and bridges) with results from literature reviews, including movement capabilities and expected structural tolerances. It can use two movement buffer scales—within-home-range and long-distance dispersal—to address routine versus infrequent connectivity needs, making it adaptable to varying conservation goals. Structures are filtered by species-specific tolerance criteria (e.g., dimensions, substrate, visibility), while highway segments with proximal species evidence but no usable structures are deemed connectivity gaps. Results are combined for multi-species prioritization. This is the first method to distinguish existing high-potential wildlife structures from true connectivity gaps. Applied to 5 highways (US-101, I-680, I-580, SR-84, I-5) and 24 focal species thus far, the approach is reproducible across ecoregions, scales, and goals, producing actionable recommendations (e.g., maintenance, retrofits, new crossings). It offers agencies a defensible, system-wide framework for prioritizing connectivity investments under uncertainty.