Estimating Uncoded Crash Factors with Tabular Foundation and System One Models: Kumo Tabular and Jev
Read the original on arXiv Machine Learning →The paper presents a system that combines coded police crash records with officer narratives to estimate uncoded crash factors in Texas from 2017‑2025. It uses an in‑context tabular foundation model (Kumo Tabular) to process coded data, a calibrated System One model (Jev) to read sampled narratives, and human judgments to recalibrate probabilities, producing validated population estimates for factors such as hydroplaning, medical episodes, fatigue, animals, and phone use. The approach demonstrates that narrative analysis uncovers more injury crashes than coded fields and that a single additional round of human review can significantly tighten estimate precision. "whyItMatters":"The system provides safety offices with more accurate counts, a discordance map, a validated re‑read list, and a reading budget, enabling better resource allocation for road‑safety interventions."
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