GNN Traffic Prediction Model for Smart City Congestion Management
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Solution Overview
Problem
Current traffic management strategies in smart cities rely heavily on human experience, leading to unscientific judgments, poor accuracy, and delayed responses, which inefficiencies in managing traffic congestion.
Innovation Solution
A method utilizing a Graph Neural Network (GNN) model to predict traffic congestion areas and dynamically adjust traffic scheduling strategies, including the deployment of traffic police, traffic light durations, and temporary traffic controls, based on real-time and historical traffic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traffic scheduling strategies are formulated based on human experience, then the management process is simple to operate, but the accuracy and scientificity of traffic management deteriorates
Solution Approach 1:
The patent replaces the mechanical system of human experience-based judgment with an automated AI model system. The traffic state prediction model and traffic scheduling strategy prediction model automatically analyze traffic data and generate scheduling strategies, substituting human cognitive processes with computational algorithms to improve accuracy while maintaining operational simplicity through automated decision-making.
Solution Approach 2:
The traffic management system performs self-service by automatically predicting traffic states and generating scheduling strategies without requiring human intervention. The AI models continuously process traffic data and autonomously determine optimal scheduling strategies, enabling the system to serve itself in formulating traffic management policies.
2Speed
If traffic scheduling strategies are formulated based on human experience, then the system is easy to operate, but the response time to traffic congestion deteriorates
Solution Approach 1:
The patent implements preliminary action by predicting future traffic states before congestion occurs. The traffic state prediction model analyzes current traffic data to forecast future traffic conditions, allowing the system to proactively adjust scheduling strategies in advance, thereby improving response time by acting before problems fully manifest.
Solution Approach 2:
The system replaces manual analysis and decision-making with automated AI models that continuously process traffic data in real-time. This substitution enables rapid response to changing traffic conditions without the delays inherent in human judgment processes.
3Measurement precision
If automated prediction models are used to improve traffic management accuracy, then the scientificity and accuracy of traffic management is improved, but the device complexity increases
Solution Approach 1:
The patent employs Graph Neural Network models to substitute complex manual analysis processes with automated computational systems. The GNN architecture efficiently processes spatial relationships in traffic data, achieving high prediction accuracy while managing computational complexity through specialized neural network designs tailored for graph-structured traffic networks.
Data Source
AI summary
The present disclosure provides a method for managing traffic congestion in a smart city. The method includes predicting, based on a trained traffic state prediction model, one or more target areas where the traffic congestion is likely to occur from the preset area during a next time period by processing the traffic data information during the current time period, the traffic state prediction model being a Graph Neural Network (GNN) model and a predicted result being output by at least one node of a traffic state prediction model; determining whether a traffic scheduling strategy is needed to be switched based on traffic data information in the one or more target areas during the next time period; and in response to determining that the traffic scheduling strategy is needed to be switched, switching a first traffic scheduling strategy to a second traffic scheduling strategy.