Highway Traffic Congestion Recognition Dataset
#object detection #image recognition #traffic monitoring #intelligent transportation #urban management
- Records
- 5000 records
- Size
- 1.2G
- Format
- JPG/PNG/JSON
- Provider
MOBIUSI INC- Updated
- v1.02026-11-05
AI Analysis & Value Prop
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.
Overview
Dataset Insights
Preview
Sample Examples
c8053dfb**.jpg|1920*1080|151.16 KB
143594de**.jpg|1080*1440|152.37 KB
79262177**.jpg|1920*2560|443.00 KB
bc1dac27**.jpg|2560*1920|567.41 KB
8943fa54**.jpg|3000*4000|852.71 KB
Schema
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. |
Trust
Compliance Statement
Authorization Type
Proprietary - Commercial AI Training License (No Redistribution)
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
FAQ
Frequently Asked Questions
- What information does the highway traffic congestion recognition dataset contain?
- The dataset primarily contains images from highways, which are used for automatic recognition and monitoring of traffic congestion.
- How to use this dataset for traffic congestion detection?
- You can use this dataset to train an object detection model to detect and recognize traffic congestion on highways.
- Why is the highway traffic congestion dataset helpful for the transportation industry?
- This dataset aids in automated traffic management, providing real-time monitoring and prediction tools, thereby improving traffic efficiency and safety.
- Is specific software required to analyze the highway traffic congestion recognition dataset?
- Analysis of this dataset typically requires machine learning frameworks like TensorFlow or PyTorch, which support training and inference of object detection models.
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Cite this Work
@dataset{Mobiusi2025,
title={Highway Traffic Congestion Recognition Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/8c24c4ed189a3d4581bb119a372b73ba?dataset_scene_id=1},
urldate={2025-09-15},
keywords={traffic congestion dataset, object detection dataset, highway monitoring, intelligent transportation},
version={1.0}
}Using this in research? Please cite us.
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