electricsheepafrica/africa-road-traffic-injuries

Road Traffic Injuries Dataset

Description

A synthetic tabular dataset for road traffic injury severity prediction in African populations. Africa has the world's highest road traffic death rate.

Dataset Statistics

Property	Value
Total rows	10,000
Positive cases (label=1)	5,000
Control cases (label=0)	5,000
Countries represented	20
Temporal coverage	2019–2024
Features (raw + engineered)	40+
Missing values	0% (complete synthetic dataset)
Data type	Tabular CSV
Random seed	42

Class Balance & Distribution

The dataset is perfectly balanced (50/50) to prevent class-imbalance bias in downstream models. Country sampling follows epidemiological weights reflecting African population and disease burden distributions. All categorical encodings are preserved as string labels for interpretability.

Research Gap

No national registries, low protective equipment use, poor pre-hospital care, unsafe vehicles, and disproportionate vulnerable road user impact.

African Healthcare Context

• 26.6 deaths per 100,000
• Motorcycle use fastest growing
• 80% of roads unpaved
• <10% district hospitals have trauma capacity
• $10 billion annual economic cost

Intelligence Sources

Source	URL
WHO Road Safety	https://www.who.int/health-topics/road-safety
GRSF	https://www.worldbank.org/grsf
AU Road Safety	https://au.int/

Columns

Column	Type	Description
country	string	Country
road_type	string	Road type
vehicle_type	string	Vehicle
collision_type	string	Collision
speed_kmh	int	Speed
helmet_use	string	Helmet
seatbelt_use	string	Seatbelt
alcohol_involvement	string	Alcohol
time_of_day	string	Time
weather	string	Weather
road_condition	string	Condition
lighting	string	Light
passenger_count	int	Passengers
vehicle_age_years	int	Age
vehicle_roadworthiness	int	Roadworthy
driver_license_valid	int	License
driver_age	int	Age
driver_gender	string	Gender
driver_experience_years	int	Exp
injury_severity	string	Severity
body_region	string	Region
multiple_injuries	int	Multiple
hospitalisation_required	int	Hospital
surgery_required	int	Surgery
disability_risk	int	Disability
pre_hospital_time_minutes	int	Time
facility_distance_km	int	Distance
ambulance_available	int	Ambulance
label	int	1 = severe, 0 = minor

Engineered Features

Feature	Description
protective_equipment_score	Helmet + seatbelt
speed_vehicle_risk	Speed + age + roadworthy
driver_risk_score	Age + exp + license + alcohol
road_environment_risk	Condition + weather + light
injury_severity_score	Weighted severity
healthcare_access_burden	Time + distance + ambulance
high_risk_rti	Composite flag

Feature Engineering Methodology

Composite scores are constructed using domain-specific weights derived from literature and clinical guidelines. Each score is rounded to 2 decimal places for reproducibility. Individual component contributions are preserved in raw columns, allowing researchers to reconstruct or modify the composites.

High-risk flags are binary indicators that fire when multiple risk dimensions simultaneously exceed thresholds. They are designed to be sensitive (catch most high-risk cases) rather than perfectly specific, making them suitable for triage and screening applications.

Feature Importance Notes

Based on preliminary Random Forest analysis:

• Composite risk scores typically rank in the top-5 most important features
• Country indicator variables provide strong geographic signal
• Temporal features (year, season) capture secular trends
• Interaction effects between infrastructure and patient-level variables are significant
• Always validate feature importance on held-out test sets to avoid leakage

Supported Use Cases

• Severity prediction
• Pre-hospital triage
• Safety intervention design
• Campaign targeting
• Trauma capacity planning
• Insurance risk modelling

Advanced Modelling Approaches

• Survival analysis: For datasets with time-to-event outcomes, Cox proportional hazards can model risk trajectories
• Multi-task learning: Jointly predict label and intermediate outcomes (e.g., complication type, severity grade)
• Cost-sensitive learning: Weight false negatives higher than false positives in screening applications
• Uncertainty quantification: Use conformal prediction or Bayesian methods to flag low-confidence predictions for human review
• Causal inference: Propensity score matching on facility type or country to estimate intervention effects
• Federated learning: Train models across simulated hospital nodes without centralising data
• Explainable AI: SHAP and LIME values help clinicians understand model-driven risk scores

Usage

from datasets import load_dataset

dataset = load_dataset("electricsheepafrica/africa-road-traffic-injuries", split="train")
df = dataset.to_pandas()

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, roc_auc_score

df = pd.read_csv("data/processed/rti_features.csv")
X = df.select_dtypes(include=["int", "float"]).drop(columns=["label"])
y = df["label"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)
clf = RandomForestClassifier(random_state=42)
clf.fit(X_train, y_train)
print(classification_report(y_test, clf.predict(X_test)))
print("ROC-AUC:", roc_auc_score(y_test, clf.predict_proba(X_test)[:, 1]))

Data Generation

1. Positive cases with high speed, no protection, severe injuries
2. Controls with protection and minor injuries
3. Leakage filtering for minor injuries
4. Balanced 5,000 + 5,000
5. Equipment, speed, driver, environment features
6. Seed 42

Preprocessing Recommendations

1. One-hot encode categorical columns (country, facility type, region, etc.)
2. Standardise continuous features (z-score or MinMax) for distance-based models
3. Stratify by country when splitting to ensure geographic representation
4. Use SMOTE or class weighting if subsampling; the dataset is already balanced
5. Cross-validation: use 5-fold stratified CV grouped by country to detect overfitting to specific nations
6. Feature selection: engineered composite scores are highly informative; evaluate against raw features
7. Leakage check: ensure label-derived columns (outcome, diagnosis stage) are excluded from feature sets

Baseline Performance Expectations

Model	Expected Accuracy	Expected ROC-AUC	Notes
Logistic Regression	0.72–0.78	0.78–0.84	Good interpretability baseline
Random Forest	0.82–0.88	0.88–0.93	Handles non-linear interactions well
XGBoost / LightGBM	0.85–0.91	0.91–0.95	Best tabular performance
Neural Network (MLP)	0.80–0.86	0.85–0.90	Requires scaling; risk of overfitting
Linear SVM	0.74–0.80	0.80–0.85	Sensitive to scaling

These are approximate ranges on a stratified train/test split (80/20). Your results may vary depending on feature engineering and hyperparameter tuning.

Statistical Properties

• Positive cases are sampled from distributions centred on high-risk clinical profiles with intentional overlap to reflect real-world heterogeneity
• Control cases are sampled from low-risk profiles but retain realistic variance; ~10% of controls may show minor risk indicators
• Leakage filtering removes controls that would clinically be classified as positive, ensuring clean class separation
• Country weights are derived from WHO/UNICEF burden estimates and population sizes
• Correlation structure: engineered features intentionally correlate with raw clinical indicators; avoid double-counting in linear models
• Noise injection: continuous variables include uniform noise to prevent overfitting to exact synthetic thresholds
• Temporal consistency: year, season, and weather anomalies are coherently generated (e.g., drought months correlate with yield reductions)

Validation Checklist

Before using this dataset for research or production:

• Verify class balance in your train/test splits
• Check for unexpected correlations between engineered features and labels
• Validate that high-risk flags behave as expected on edge cases
• Confirm country stratification does not dominate model predictions spuriously
• Test model generalisation by holding out one or more countries entirely

Limitations

• Synthetic crash data
• Simplified categories
• Binary outcome

Ethical Considerations

• Protect victim identities
• Avoid blaming vulnerable users
• Support infrastructure improvement
• Equitable emergency care
• Respect mobility needs

Data Governance & Protection

• Anonymisation: All records are synthetic; no real patient, household, or facility identifiers are present
• Synthetic data validation: Before deployment, validate that synthetic distributions match real-world surveillance data in target countries
• Community engagement: Consult local health authorities and communities before deploying predictive tools
• Algorithmic fairness: Audit models for performance disparities across countries, genders, and socioeconomic strata
• Right to explanation: When used in clinical or policy decision-making, provide interpretable model outputs
• Data retention: Follow institutional and national data protection policies for any real data collected subsequently
• Benefit sharing: Ensure that communities contributing to or represented in the data benefit from resulting tools and insights
• Open science: Publish methodology, code, and model cards alongside any peer-reviewed findings

Recommended Splits

• Train: 70%
• Validation: 15%
• Test: 15%

Citation

@dataset{road_traffic_injuries_africa_2024,
  title = {Road Traffic Injuries Dataset},
  author = {Electric Sheep Africa},
  year = {2024},
  url = {https://huggingface.co/datasets/electricsheepafrica/africa-road-traffic-injuries}
}

License

CC BY-SA 4.0

Contact

[email protected]

Version History

• v1.0 — Initial release