crashrisk-qld-severity

CrashRisk-QLD — Severity classifier (4 classes)

LightGBM classifier predicting the severity of a Queensland road crash given pre-/at-crash conditions. Classes: fatal, hospitalisation, medical treatment, minor injury.

© 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, json

REPO = "Mattysmittttt/crashrisk-qld-severity"
booster = lgb.Booster(model_file=hf_hub_download(REPO, "model.txt"))
pre = joblib.load(hf_hub_download(REPO, "preprocessor.joblib"))
classes = json.loads(open(hf_hub_download(REPO, "label_classes.json")).read())
# `pre` is a fitted sklearn ColumnTransformer; pass a DataFrame whose
# columns match the dataset's ✅ feature columns. See dataset card for
# the schema, and feature_importance.csv for the names this model uses.

Intended uses

• Road-safety research and descriptive analytics.
• Education on tabular ML, class imbalance, and leakage prevention.
• As a baseline severity model in road-safety pipelines.

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 for full provenance, cleaning, schema, and split definition.

• Train (2011–2022): 152,842 rows
• Validation (2023): 13,622 rows (used for early stopping and Optuna HPO)
• Test (2024): 14,358 rows

Training details

• Model: LightGBM multiclass objective, multi_logloss metric
• Class imbalance: balanced sample weights (sklearn.utils.class_weight.compute_sample_weight('balanced', y))
• Hyperparameter tuning: Optuna TPE, 20 trials, minimising val log-loss with early stopping
• Best iteration on val: 1686
• Best HPO config:

{
  "num_leaves": 227,
  "learning_rate": 0.010205401359666888,
  "feature_fraction": 0.7235023898845259,
  "bagging_fraction": 0.6068744601075835,
  "bagging_freq": 6,
  "min_data_in_leaf": 76,
  "lambda_l1": 1.4239280268649237e-08,
  "lambda_l2": 1.2540363726263013e-08
}

• Features after preprocessing: 173 (low-card → OHE, high-card → TargetEncoder, numerics passthrough)

Evaluation (held-out test = 2024)

• Accuracy: 0.4404 (sanity check: must NOT exceed 0.70 — observed: ok)
• Macro-F1: 0.3259 [95% bootstrap CI 0.3164, 0.3351]
• Log-loss: 1.1820 [1.1680, 1.1965]

Per-class metrics

Confusion matrix

Rows = actual, columns = predicted.

Top 20 feature importances (LightGBM gain)

1. loc_suburb_0 (gain = 1574725)
2. loc_suburb_3 (gain = 574071)
3. crash_hour (gain = 562661)
4. loc_abs_statistical_area_2_0 (gain = 468933)
5. count_unit_car (gain = 466195)
6. crash_year (gain = 464291)
7. loc_suburb_2 (gain = 403835)
8. loc_suburb_1 (gain = 391710)
9. crash_longitude (gain = 367704)
10. crash_latitude (gain = 332986)
11. loc_abs_statistical_area_2_3 (gain = 300528)
12. loc_post_code_0 (gain = 260922)
13. crash_speed_limit (gain = 254960)
14. loc_post_code_3 (gain = 242106)
15. loc_abs_statistical_area_2_2 (gain = 240559)
16. loc_abs_statistical_area_2_1 (gain = 235112)
17. loc_post_code_2 (gain = 199437)
18. loc_post_code_1 (gain = 194552)
19. loc_state_electorate_3 (gain = 183491)
20. loc_state_electorate_0 (gain = 183178)

SHAP explainability

Global and local SHAP plots for this model are in the reports/shap_severity/ folder of the source repository:

• summary_bar.png — mean |SHAP| across the test sample
• summary_beeswarm.png — per-feature SHAP distribution
• dependence_*.png — top-6 features
• force_plot_examples.png — TP / TN / FP local explanations for the fatal class
• values.parquet — raw SHAP values, reproducible

Geographic surface

Per-LGA mean predicted P(fatal) under a fixed condition grid (wet/dry × day/night × intersection/no-feature × 60 km/h vs 110 km/h) is published in reports/maps/ of the source repository as both interactive HTML and static PNG.

Limitations & biases

• Trained on reported crashes only — under-reporting is real.
• Possible feedback loops if used to allocate enforcement or infrastructure spend (geographic risk concentrates because past incidents concentrated, not necessarily because future ones will).
• Geographic coverage variance across QLD: urban LGAs are very well-represented; remote LGAs much less so.
• Temporal drift: 2011–2024 includes vehicle-safety improvements, regulatory changes, and recording-system migrations.

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_severity_2026,
  title  = {CrashRisk-QLD severity classifier},
  author = {Mattysmittttt},
  year   = {2026},
  url    = {https://huggingface.co/Mattysmittttt/crashrisk-qld-severity},
  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. Attribution must be preserved.