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TRUVACE RECORD VERSION
record: TRV-2026-1943
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-19T22:03:16.156524Z
status: published
lens: g_space
sector: policy
headline: Consensus machine learning for interpretable road-user fatality risk analysis: Evidence from the Canadian National Collision Database
dek: 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…
gain_title: Aggregating feature rankings across three models gave a consistent, interpretable list of fatal-collision predictors that could support road-safety planning.
problem_title: (none)
trace_subject: (none)
gain_reading: Aggregating feature rankings across three models gave a consistent, interpretable list of fatal-collision predictors that could support road-safety planning.
gain_evidence: providing a robust and interpretable approach to understanding fatal collision risk
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers used person-level records from the Canadian National Collision Database for 2014-2021 to train balanced random forest, XGBoost and neural network models that separate fatal from nonfatal collision outcomes. They then combined permutation-based feature-importance rankings from the three models into a consensus framework.

The result matters because fatal crashes are a small share of reported collisions but carry a large public health burden, and a stable ranking of risk factors can inform where engineering, enforcement and education effort goes. The authors present the approach as a complementary decision-support tool, not a replacement for existing safety planning.
limitation: 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.
tag: Evidence-backed gain
key_points: XGBoost performed best with ROC-AUC 0.932 and PR-AUC 0.714, ahead of balanced random forest (0.898 and 0.585) and the neural network (0.886 and 0.587). | Collision configuration, traffic control, collision hour and number of vehicles involved ranked consistently among the most influential predictors across all three models. | The consensus framework named collision configuration, road-surface conditions, time of collision, number of vehicles, roadway configuration and weather as leading predictors. | Crash characteristics and traffic and environmental conditions were the two key feature categories for predicting fatalities.
rundown: The data came from person-level NCDB records covering 2014-2021, and all models used a common preprocessing framework. Performance was assessed with ROC-AUC and PR-AUC, the latter being informative when fatal outcomes are rare.

Roadway alignment, vehicle characteristics and safety-device use also contributed to prediction, but their relative importance varied between models. The consensus method was designed to highlight predictors that stayed important across algorithms and across reporting years.

The authors conclude that the findings support engineering, policy, enforcement and education initiatives aimed at reducing fatal collisions. This is a predictive and associational analysis of historical records, not a test of any intervention.
sources:
- peer_reviewed | Traffic Injury Prevention | https://doi.org/10.1080/15389588.2026.2738025 | 2026-10-16
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