Road Link Congestion Queue Detection Using Vehicle Probe Clusters
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Solution Overview
Problem
Current traffic management systems lack comprehensive methodologies to accurately detect, analyze, and mitigate traffic congestion in real-time, leading to inefficiencies in road infrastructure optimization and increased adverse effects on travelers.
Innovation Solution
A system utilizing machine learning models to analyze probe data from vehicles, generating clusters based on motion components, and determining traffic congestion status, including enqueuing, dequeuing, or stagnant conditions, to provide real-time congestion information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional methods (manual observations or fixed sensors) are used to monitor traffic, then real-time data collection is achieved, but comprehensive congestion status information (enqueuing, stagnant, dequeuing) cannot be provided
Solution Approach 1:
The patent introduces mobile devices (smartphones, tablets) as intermediary carriers to collect traffic congestion data from vehicles. These devices serve as mobile sensors that can detect congestion status (enqueuing, stagnant, dequeuing) and transmit the data to the server, enabling comprehensive congestion information collection without requiring complex fixed sensor infrastructure throughout the entire road network.
Solution Approach 2:
The patent replaces traditional mechanical sensor systems with a software-based processing approach. The server uses algorithms to process probe data from multiple vehicles and determine congestion status, substituting physical sensor infrastructure with computational analysis of vehicle movement patterns, speeds, and positions to identify congestion conditions.
2Measurement precision
If machine learning models are applied to analyze probe data and generate clusters, then congestion detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent segments the road network into multiple road links and divides the congestion detection task by processing probe data for each road link independently. The system collects probe data from vehicles on specific road links, processes this localized data through machine learning models to generate clusters, and determines congestion status for each segment separately, improving overall processing efficiency while maintaining accuracy.
Solution Approach 2:
The patent applies machine learning models selectively to process probe data only for road links where congestion is detected or suspected, rather than processing all road links uniformly. This partial application of computational resources focuses processing power on areas needing attention, reducing overall processing time while maintaining high detection accuracy for congested segments.
Data Source
AI summary
A system for characterizing congestion queue status on road links is disclosed. The system obtains the first probe data associated with a first plurality of probe points from a first vehicle of a set of vehicles associated with a road segment. The system further generates a plurality of motion components for each of the first plurality of probe points based on the obtained first data. The system further applies a machine learning (ML) model on the generated plurality of motion components for the first plurality of probe points. The system further generates a set of clusters based on the application of the ML model on the generated plurality of motion components. The system further outputs the generated set of clusters.