aceConsensus machine learning for interpretable road-user fatality risk analysis: Evidence from the Canadian National Collision Database
Objectives Fatal road traffic collisions account for a small proportion of reported crashes but impose a disproportionate societal and public health burden. This study evaluates interpretable machine-learning models and proposes a consensus machine learning (CML) framework to identify robust predictors associated with fatal collision risk using Canadian collision data. Methods Person-level records from the Canadian National Collision Database (NCDB) covering 2014-2021 were used to classify fatal and nonfatal col…
Aggregating feature rankings across three models gave a consistent, interpretable list of fatal-collision predictors that could support road-safety planning.
Predictors are described as associated with fatal collision risk, and several features varied in relative importance between models, so the results do not show causal effects or the impact of any intervention.
Evidence
- Peer-reviewedTraffic Injury Prevention2026-10-16
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Truvace Impact Record TRV-2026-1943, v1: “Consensus machine learning for interpretable road-user fatality risk analysis: Evidence from the Canadian National Collision Database.” Truvace, 2026-10-19. /record/TRV-2026-1943 (accessed at citation time). sha256 284d9f6cce223016…
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