Edge Nodes for Lane-Level Traffic State Determination
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Solution Overview
Problem
Existing centralized traffic management systems face challenges such as low reusability of intersection devices, independence of inter-system data, and excessive reliance on central computing capabilities, leading to inefficiencies in traffic state determination and management.
Innovation Solution
An edge computing-based method that preprocesses lane-level multi-source data from intersection nodes, uses fuzzy logic models to determine traffic states, and computes average delays and travel times to provide accurate, real-time traffic information for proactive anomaly identification and improved traffic management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized application systems are used for traffic management, then system coordination is achieved, but device reusability decreases and data independence is lost
Solution Approach 1:
The patent divides the centralized traffic management system into distributed edge computing nodes at intersections. Each edge node independently processes local traffic data and performs computations, while still coordinating with other nodes through standardized protocols. This segmentation enables each device to be reused across different intersections without requiring system redesign, resolving the contradiction between system coordination and device reusability.
2Reliability
If centralized computing is relied upon excessively, then overall system control is achieved, but processing efficiency and real-time response deteriorate
Solution Approach 1:
The patent extracts computing capabilities from the centralized system and implements them at edge nodes at intersections. Each edge node performs local computations such as calculating average delays, determining traffic states, and generating control signals independently. This extraction maintains overall system control through standardized protocols while dramatically improving processing efficiency and real-time response by eliminating the need for all computations to go through the center.
3Measurement precision
If multi-source data aggregation is performed at intersections, then traffic state determination accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent implements local quality by having each edge node process and aggregate multi-source data (vehicle counts, speeds, delays) specific to its local intersection environment. The node performs computations such as calculating average delay per vehicle and determining traffic state based on locally relevant parameters. This localized processing maintains high measurement precision for traffic state determination while managing data processing complexity by processing data at the appropriate granular level rather than attempting to process all data centrally.
Data Source
AI summary
An edge computing-based method for fine determination of an urban traffic state includes the following steps: preprocessing lane-level data collected by edge nodes; dividing a complete road segment; computing an average delay per vehicle of a lane by using the edge nodes; inputting the preprocessed and computed data into a fuzzy logic model to determine a lane-level traffic state of an approach region; and based on the characteristic that edge nodes at the intersections can be interconnected, linking upstream and downstream intersection information to compute an average travel speed of a remaining road segment, and determining a traffic state of the remaining road segment.