Dynamic Correlation Adjacency-Matrix-Based Graph Neural Networks for Traffic Flow Prediction

Dynamic Correlation Adjacency-Matrix-Based Graph Neural Networks for Traffic Flow Prediction

Modeling complex spatial and temporal dependencies in multivariate time series data is crucial for traffic forecasting. Graph convolutional networks have proved to be effective in predicting multivariate time series. Although a predefined graph structure can help the model converge to good results q...

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Bibliographic Details
Main Authors: Junhua Gu, Zhihao Jia, Taotao Cai, Xiangyu Song, Adnan Mahmood
Format: Article
Language:English
Published: MDPI AG 2023-03-01
Series:Sensors
Subjects:
graph neural networks
dynamic adjacency matrix
multivariate time series
traffic prediction
Online Access:https://www.mdpi.com/1424-8220/23/6/2897

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https://www.mdpi.com/1424-8220/23/6/2897

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