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Mobiusi/Highway-Traffic-Congestion-Recognition-Dataset

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… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Highway-Traffic-Congestion-Recognition-Dataset.

sourceHugging Facecc-by-nc-sa-4.0updated 7mo agoView on Hugging Face

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Dataset Card

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.
timeofday	string	The time period when the image was captured, such as day, night, or dusk.
trafficsignalstatus	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

<table> <tr> <td>Authorization Type</td> <td>CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)</td> </tr> <tr> <td>Commercial Use</td> <td>Requires exclusive subscription or authorization contract (monthly or per-invocation charging)</td> </tr> <tr> <td>Privacy and Anonymization</td> <td>No PII, no real company names, simulated scenarios follow industry standards</td> </tr> <tr> <td>Compliance System</td> <td>Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs</td> </tr> </table>

Source & Contact

If you need more dataset details, please visit Mobiusi. or contact us via contact@mobiusi.com