jomarie04/smart-city-traffic-predictor

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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.

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Model Details

• Model Name: smart-city-traffic-predictor
• Model Type: Regression
• Framework: scikit-learn / XGBoost
• Input: Tabular CSV data
• Output: Traffic congestion score (numeric)

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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

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Intended Use

✅ Traffic forecasting demos
✅ Regression model tutorials
❌ Real-world traffic control without validation

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Evaluation Results

Metric	Score
RMSE	5.2
MAE	4.1
R²	0.88

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Limitations

• Synthetic data only
• Limited geographic and road type coverage
• Simplified traffic patterns

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Ethical Considerations

No real sensor or personal data is used.
Using this model for real-world traffic decisions without proper validation could be unsafe.

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License

This model is released under the Apache License 2.0.