Datasets:
file_name stringclasses 5 values | quality stringclasses 5 values | vehicle_count stringclasses 5 values | congestion_level stringclasses 3 values | weather_conditions stringclasses 3 values | road_condition stringclasses 2 values | time_of_day stringclasses 2 values | traffic_signal_status stringclasses 5 values | lane_count stringclasses 3 values | incident_presence stringclasses 5 values |
|---|---|---|---|---|---|---|---|---|---|
143594de27b658c015be8fac2e82dfbe.jpg | 1080*1440 | 8 | Severe | Clear | Dry | Daytime | Unclear | 3 | Traffic incident present |
79262177896f06027f74c44cbcd6c473.jpg | 1920*2560 | 5 | Moderate | Clear | Dry | Daytime | None | 2 | Traffic accident present |
8943fa5423ce7d64ec04d39567f186d1.jpg | 3000*4000 | at least 3 vehicles | severe | clear | dry | daytime | not displayed | at least 2 lanes | traffic incident present |
bc1dac27064a27dfeb871a02754f0c6a.jpg | 2560*1920 | 20 | Severe | Sunny | Dry | Daytime | Red light | 3 | None |
c8053dfb9d88f3e9a6b3cee2dc552149.jpg | 1920*1080 | 6 | Moderate | Sunny | Dry | Daytime | No signal light | 2 | Yes |
Highway Traffic Congestion Recognition Dataset
The current transportation industry faces serious traffic congestion issues, leading to time wastage and environmental pollution. Existing traffic monitoring systems often rely on traditional manual inspections, which are inefficient and prone to errors. This dataset aims to provide high-quality traffic congestion image data to support deep learning-based object detection technologies, improving the automation and accuracy of traffic condition recognition. The dataset includes traffic images from various highways, with collection devices including HD cameras and the collection environment being actual highways. To ensure data quality, multi-round annotation and expert review are used to ensure consistency and accuracy of each image and label. The data is stored in JPEG format, organized with each image corresponding to an ID, file path, and annotation information.
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| vehicle_count | int | The total number of vehicles appearing in the image. |
| congestion_level | string | The level of traffic congestion depicted in the image, such as severe, moderate, or mild. |
| weather_conditions | string | The weather conditions at the time the image was captured, such as sunny, cloudy, or rainy. |
| road_condition | string | The road conditions depicted in the image, such as dry, slippery, or snowy. |
| time_of_day | string | The time period when the image was captured, such as day, night, or dusk. |
| traffic_signal_status | string | The status of the traffic signal at the time of capture, such as red, green, or yellow. |
| lane_count | int | The number of visible lanes in the image. |
| incident_presence | boolean | Indicates whether a traffic incident is present in the image. |
Compliance Statement
| Authorization Type | CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Source & Contact
If you need more dataset details, please visit Mobiusi. or contact us via contact@mobiusi.com
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