crashrisk-qld-fatal

CrashRisk-QLD — Fatal binary classifier

LightGBM binary classifier estimating P(fatal) given pre-/at-crash conditions in a Queensland road crash. Severe class imbalance (1.90% positive on the held-out test year); we evaluate primarily on PR-AUC and report a full threshold sweep.

© State of Queensland (Department of Transport and Main Roads) 2026. Licensed CC-BY 4.0. Source: data.qld.gov.au, dataset 'Crash data from Queensland roads', version rqC45037 (2025-06), retrieved 2026-04-30.

Disclaimer. This is a population-level statistical model trained on publicly reported crash data. It is NOT suitable for individual driver risk assessment, insurance underwriting, pre-incident law enforcement targeting, or any decision with legal or financial consequence to an individual. Use it for research, road-safety analysis, and education.

Quick start

from huggingface_hub import hf_hub_download
import joblib, lightgbm as lgb

REPO = "Mattysmittttt/crashrisk-qld-fatal"
booster = lgb.Booster(model_file=hf_hub_download(REPO, "model.txt"))
pre = joblib.load(hf_hub_download(REPO, "preprocessor.joblib"))
# pre.transform(X)  →  booster.predict(...)  →  P(fatal)

Intended uses

Same as the multi-class severity head: research, education, descriptive analysis. Pair the two heads — the severity head gives a full distribution, the fatal head gives a sharper imbalanced-binary signal.

Out of scope

• Insurance underwriting — using population-level statistical patterns to set individual premiums creates fairness concerns and is outside this model's intended scope.
• Individual driver risk assessment — these features describe road conditions and aggregate vehicle context, not driver behaviour or identity.
• Pre-incident law enforcement targeting — geographic patterns may reflect reporting biases as much as actual risk; using them to pre-target locations creates feedback loops.
• Any decision with legal or financial consequence to a single individual — full stop.

Training data

See the dataset card.

• Train: 152,842
• Test: 14,358 (positives: 273, rate: 1.901%)

Training details

• Objective: binary log-loss (no scale_pos_weight — at ~50× it caused early stopping at iteration 1; imbalance is handled at threshold-selection time via the threshold sweep instead).
• Optuna: 20 trials, TPE sampler, early stopping on val log-loss
• Best iteration: 23
• Best params:

{
  "num_leaves": 83,
  "learning_rate": 0.05082341959721458,
  "feature_fraction": 0.6563696899899051,
  "bagging_fraction": 0.9208787923016158,
  "bagging_freq": 0,
  "min_data_in_leaf": 480,
  "lambda_l1": 0.08916674715636552,
  "lambda_l2": 6.143857495033091e-07
}

• Features after preprocessing: 155

Evaluation (held-out test = 2024)

• PR-AUC (primary): 0.0737 [95% CI 0.0579, 0.0958]
• ROC-AUC: 0.7858 [0.7623, 0.8093] (sanity check: must NOT exceed 0.95 — observed: ok)
• Best-F1 operating point: precision = 0.092, recall = 0.282, F1 = 0.139 (threshold = 0.0620; 95% F1 CI 0.112, 0.165)
• Recall @ Precision = 0.20: 0.026 (threshold = 0.1744)
• Recall @ Precision = 0.50: 0.007 (threshold = 0.2448)

Threshold sweep (compact)

Full table on the artifact: threshold_table.csv.

Pick a threshold for your use case rather than relying on the default 0.5 — the default is rarely optimal under heavy class imbalance.

Top 20 features (LightGBM gain)

1. loc_suburb (gain = 28660)
2. count_unit_car (gain = 5619)
3. crash_speed_limit (gain = 5061)
4. loc_abs_statistical_area_2 (gain = 4840)
5. loc_post_code (gain = 4436)
6. count_unit_pedestrian (gain = 2189)
7. count_unit_motorcycle_moped (gain = 1875)
8. crash_hour (gain = 1678)
9. count_unit_truck (gain = 1453)
10. crash_year (gain = 1014)
11. loc_state_electorate (gain = 975)
12. crash_lighting_condition_daylight (gain = 878)
13. crash_latitude (gain = 748)
14. loc_abs_statistical_area_3 (gain = 619)
15. crash_longitude (gain = 608)
16. crash_road_horiz_align_straight (gain = 601)
17. crash_roadway_feature_no roadway feature (gain = 554)
18. loc_local_government_area (gain = 513)
19. crash_road_horiz_align_curved - view open (gain = 471)
20. crash_lighting_condition_darkness - not lighted (gain = 400)

SHAP explainability

See reports/shap_fatal/ in the source repository for global and local SHAP plots. The same set of physical drivers (speed_limit, lighting, surface, roadway_feature) consistently dominates, which is the expected sanity-check signal.

Geographic surface

See reports/maps/model_lga_risk_surface.png (and the interactive HTML in the same folder) for a per-LGA map of mean P(fatal) under a fixed conditions grid.

Limitations & biases

Same set as the severity card. The binary head is more sensitive to under-reporting bias than the multi-class one because the positive class is small enough that a few mis-coded rows shift metrics noticeably.

Ethical considerations

• Population-level, not causal: The model encodes correlations between pre-crash conditions and recorded outcomes. It does not assign fault and cannot be read as a statement about individual responsibility.
• Geographic predictions can stigmatise: We publish per-LGA aggregates only, never per-address. Even at LGA level, higher predicted risk reflects historical reporting and demographics as much as it does inherent road danger.
• Demographic features deliberately excluded: We do not include gender, age, or any demographic field, even though some are present in the casualties aggregates. This is to avoid encoding protected-class proxies. Vehicle-type counts are kept because they describe the crash configuration, not the people involved.
• Reporting bias: This is a model of reported crashes, not true crashes. Under-reporting is differential by severity (PDO under-reported, fatal generally fully reported) and by region.

Citation

@software{crashrisk_qld_fatal_2026,
  title  = {CrashRisk-QLD fatal binary classifier},
  author = {Mattysmittttt},
  year   = {2026},
  url    = {https://huggingface.co/Mattysmittttt/crashrisk-qld-fatal},
  note   = {Trained on Mattysmittttt/qld-traffic-crashes-clean; source data CC-BY 4.0 © State of Queensland (Department of Transport and Main Roads).}
}

License

Released under CC-BY 4.0.