Traffic Congestion Visualization Using IoT Choke Point Detection
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Solution Overview
Problem
Current traffic reporting systems rely on user inputs, which can be difficult and unsafe for drivers to provide while driving, and do not provide real-time, automated traffic congestion visualization and route recommendations.
Innovation Solution
A system that analyzes traffic images from IoT devices to identify choke points of traffic congestion and generates a map of traffic congestion with real-time updates, including a visualization of the traffic congestion at a number of distances relative to the choke point, and sends this information to navigation computing devices for real-time route guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If user inputs are used for traffic reporting, then traffic information can be collected, but it is difficult and unsafe for drivers to provide inputs while driving
Solution Approach 1:
The system enables self-service traffic reporting by automatically capturing traffic images using IoT devices and processing them through AI algorithms without requiring driver intervention. The processor set autonomously identifies traffic congestion, choke points, and generates visualizations, eliminating the need for manual driver input while maintaining data reliability.
Solution Approach 2:
The patent replaces the mechanical system of manual driver input with an automated optical and computational system. IoT devices capture traffic images, and a processor set with AI algorithms automatically analyzes these images to identify traffic conditions, substituting the need for manual driver reporting with an automated vision-based system.
2Measurement precision
If real-time traffic analysis is implemented, then route guidance accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the traffic analysis function into modular components: IoT devices for image capture, a processor set for AI-based image analysis, and a navigation system for route guidance. This segmentation allows each component to specialize in specific tasks, improving overall detection accuracy while managing system complexity through distributed architecture.
Solution Approach 2:
The patent introduces an intermediary AI processing layer between raw traffic images and navigation decisions. The processor set acts as a mediator that transforms unstructured image data into structured traffic congestion information, enabling accurate real-time analysis without requiring direct complex interactions between sensing and navigation subsystems.
3Ease of operation
If automated traffic reporting is implemented, then driver safety is improved, but loss of time in automated processing occurs
Solution Approach 1:
The system performs preliminary actions by continuously capturing and pre-processing traffic images in real-time as they occur. The processor set maintains a ready state with pre-loaded AI models, enabling immediate analysis of incoming images without delay, thus keeping processing time minimal while maintaining driver safety through automation.
Solution Approach 2:
The patent implements continuous useful action by maintaining constant traffic monitoring through uninterrupted image capture and processing. The system operates continuously with the processor set always ready to analyze incoming images, eliminating idle time and ensuring that traffic information is available in real-time without periodic interruptions or delays.
Data Source
AI summary
A computer implemented method analyzes traffic. A processor set receives traffic images from Internet of Things devices in real time. The processor set identifies a choke point of traffic congestion using the traffic images. The processor set generates a map of the traffic congestion with real time updates, wherein the map includes a visualization of the traffic congestion at a number of distances relative to the choke point of the traffic congestion. The processor set sends the map of the traffic congestion with the real time updates including at least one of a stage of the traffic jam or resolution information for the traffic jam to a navigation computing device.