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smart-city-traffic-predictor
jomarie04/smart-city-traffic-predictor
smart-city-traffic-predictor is a machine learning model from jomarie04. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
--- modelname: smart-city-traffic-predictor license: apache-2.0 language: en tags: - tabular - regression - smart-city - traffic-forecasting - synthetic-data metrics: - rmse - mae - r2 ---
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Updated Jan 27, 2026
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At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Jan 27, 2026
- Updated
- Jan 27, 2026
- SHA
- 5a00babf
Model card
From the Hugging Face model README
model_name: smart-city-traffic-predictor license: apache-2.0 language: en tags:
- tabular
- regression
- smart-city
- traffic-forecasting
- synthetic-data metrics:
- rmse
- mae
- r2
Smart City Traffic Predictor
Model Overview
Smart City Traffic Predictor is a regression model trained on synthetic traffic sensor data to predict traffic congestion levels in urban areas.
Intended for research, ML demos, and educational purposes.
Model Details
- Model Name: smart-city-traffic-predictor
- Model Type: Regression
- Framework: scikit-learn / XGBoost
- Input: Tabular CSV data
- Output: Traffic congestion score (numeric)
Training Data
Synthetic dataset with the following features:
| Feature | Type | Description |
|---|---|---|
| sensor_id | String | Traffic sensor ID |
| city | Categorical | City name |
| road_type | Categorical | Highway, Urban, Suburban |
| timestamp | DateTime | Timestamp of measurement |
| vehicle_count | Integer | Number of vehicles detected |
| avg_speed_kph | Float | Average speed of vehicles |
| weather_condition | Categorical | Sunny, Rainy, Cloudy, etc. |
| incident_reported | Binary | Whether an incident occurred |
| traffic_density | Numeric | Target congestion score |
Intended Use
✅ Traffic forecasting demos
✅ Regression model tutorials
❌ Real-world traffic control without validation
Evaluation Results
| Metric | Score |
|---|---|
| RMSE | 5.2 |
| MAE | 4.1 |
| R² | 0.88 |
Limitations
- Synthetic data only
- Limited geographic and road type coverage
- Simplified traffic patterns
Ethical Considerations
No real sensor or personal data is used.
Using this model for real-world traffic decisions without proper validation could be unsafe.
License
This model is released under the Apache License 2.0.
- License
- apache-2.0
- Created
- Jan 27, 2026
- Updated
- Jan 27, 2026