Urban Traffic Congestion Prediction Dataset (Multi-City)
Catalog Summary
"Simulated traffic data from 30 major cities worldwide (2015-2024). Includes patterns, congestion metrics, time-of-day effects, and weather correlations for traffic management systems."
Catalog Notes
Dataset Scope
Comprehensive multi-city traffic dataset representing diverse urban environments globally. Useful for developing generalizable traffic prediction and congestion management models.
Geographical Coverage
- Cities: 30 major metropolitan areas worldwide
- Regions: Asia, Europe, North America, South America
- Time Period: 2015-2024 (10 years)
- Total Records: 500,000+ traffic observations
Traffic Metrics
- Congestion Level: Free flow, moderate, heavy, gridlock
- Average Speed: km/h (route-specific)
- Travel Time: Minutes (reference vs. actual)
- Vehicle Count: Hourly traffic volume
Feature Set
- Time features (hour, day of week, month, holidays)
- Weather conditions (temperature, precipitation, wind)
- Special events (sports, concerts, protests)
- Road incidents (accidents, construction)
- Public transport availability
Applications
Traffic flow prediction, congestion forecasting, route optimization, intelligent transportation systems (ITS), urban mobility planning, and real-time traffic management.
Data Preview
| Timestamp | City | Route_ID | Average_Speed_kmh | Travel_Time_min | Congestion_Level | Vehicle_Count |
|---|---|---|---|---|---|---|
| 2024-01-15 08:00 | Singapore | Route_A1 | 35.2 | 18 | Heavy | 1245 |
| 2024-01-15 08:00 | Bangkok | Route_B2 | 22.8 | 32 | Gridlock | 2150 |
| 2024-01-15 09:00 | Singapore | Route_A1 | 42.5 | 14 | Moderate | 890 |
Showing first few rows for preview
Provided Citation
Gagan Karnati (2024). Urban Traffic Data. Kaggle. Retrieved from https://www.kaggle.com/datasets/gagankarnati/urban-traffic-data
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Source metadata: Kaggle (2026)
Indexed by IoTDataset.com on Jan 13, 2026
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