Suburban Area Traffic Congestion and Traffic Pattern Image Dataset
#Object Detection #Image Segmentation #Traffic Monitoring #Intelligent Traffic #Urban Planning #Traffic Flow Analysis
- Records
- 5000 records
- Size
- 1.2G
- Format
- JPG/PNG/JSON
- Provider
MOBIUSI INC- Updated
- v1.02026-11-05
AI Analysis & Value Prop
The current traffic industry faces the problem of urban traffic congestion, especially in suburban areas where traffic flow is high and management is challenging. Existing traffic monitoring methods mostly rely on manual inspection or traditional surveillance cameras, resulting in insufficient real-time data collection and accuracy. To enhance the intelligence level of traffic management, this dataset focuses on traffic congestion and traffic patterns in suburban areas through multi-angle image capture for object detection tasks, aiming to provide high-quality foundational data support for traffic flow analysis and intelligent traffic systems. In terms of data collection, we utilize high-resolution cameras to capture traffic scenes at different times, ensuring data diversity and representativeness. Additionally, multi-round annotation and expert review quality control measures are adopted to ensure annotation accuracy and consistency. The data is stored in JPG format, organized by image ID, facilitating subsequent data retrieval and analysis.
Overview
Dataset Insights
Preview
Sample Examples
b6156da5**.jpg|1439*2558|386.93 KB
227daf9c**.jpg|3264*1493|585.50 KB
9940debf**.jpg|3060*4080|866.47 KB
Schema
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| vehicle_count | int | The total number of vehicles detected in the image. |
| pedestrian_count | int | The total number of pedestrians detected in the image. |
| bike_count | int | The total number of bicycles detected in the image. |
| traffic_signal_state | string | The current state of the traffic signal in the image, such as red, green, or yellow. |
| road_condition | string | Information about the road condition shown in the image, such as wet, dry, or flooded. |
| number_of_lanes | int | The number of traversable lanes included in the image. |
| weather_condition | string | The weather condition visible in the image, such as sunny, cloudy, or rainy. |
| vehicle_speed | float | The estimated speed of vehicles in the image (unit: km/h). |
| congestion_level | string | The level of traffic congestion indicated in the image, such as clear, light congestion, or heavy congestion. |
| incident_occurrence | boolean | Indication of whether a traffic incident occurs 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 is the Suburban Area Traffic Congestion and Passage Feature Image Dataset?
- The Suburban Area Traffic Congestion and Passage Feature Image Dataset is a collection of images annotated with traffic congestion and vehicle passage features, primarily used for intelligent management and research in the transportation industry.
- What traffic management applications can the dataset be used for?
- The dataset can be used for analyzing traffic congestion, optimizing traffic route planning, enhancing traffic signal control systems, and various other traffic management applications.
- What is the value of this dataset in the transportation industry?
- By providing detailed images of traffic congestion and passage features, the dataset helps transportation management departments more effectively identify and address traffic issues, improving overall traffic flow efficiency.
- Why choose images as the data modality?
- Images as the data modality provide a visual representation of traffic conditions and features, facilitating object detection and data analysis.
- What is the role of an object detection dataset in traffic research?
- By annotating target objects in traffic flow, an object detection dataset can be used to train algorithms for recognizing and predicting traffic conditions, supporting the development of smart transportation systems.
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Cite this Work
@dataset{Mobiusi2025,
title={Suburban Area Traffic Congestion and Traffic Pattern Image Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/b648d5061773f1f7b7243d9873864180?dataset_scene_id=1},
urldate={2025-09-15},
keywords={traffic dataset, object detection, traffic congestion data, suburban traffic monitoring},
version={1.0}
}Using this in research? Please cite us.
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