Dynamic Correlation Adjacency Matrix Based Graph Neural Network for Traffic Flow Prediction

Article


Gu, Junhua, Jia, Zhihao, Cai, Taotao, Song, Xiangyu and Mahmood, Adnan. 2023. "Dynamic Correlation Adjacency Matrix Based Graph Neural Network for Traffic Flow Prediction." Sensors. 23 (6). https://doi.org/10.3390/s23062897
Article Title

Dynamic Correlation Adjacency Matrix Based Graph Neural Network for Traffic Flow Prediction

ERA Journal ID34304
Article CategoryArticle
AuthorsGu, Junhua, Jia, Zhihao, Cai, Taotao, Song, Xiangyu and Mahmood, Adnan
Journal TitleSensors
Journal Citation23 (6)
Article Number2897
Number of Pages17
YearMar 2023
PublisherMDPI AG
Place of PublicationSwitzerland
ISSN1424-8220
1424-8239
Digital Object Identifier (DOI)https://doi.org/10.3390/s23062897
Web Address (URL)https://www.mdpi.com/1424-8220/23/6/2897
Abstract

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 quickly, it also limits the further improvement of the model due to its stationary state. In addition, current methods may not converge on some datasets due to the graph structure of these datasets being difficult to learn. Motivated by this, we propose a novel model named Dynamic Correlation Graph Convolutional Network (DCGCN) in this paper. The model can construct adjacency matrices from input data using a correlation coefficient; thus, dynamic correlation graph convolution is used for capturing spatial dependencies. Meanwhile, gated temporal convolution is used for modeling temporal dependencies. Finally, we performed extensive experiments to evaluate the performance of our proposed method against ten existing well-recognized baseline methods using two original and four public datasets.

Keywordsgraph neural networks; dynamic adjacency matrix; multivariate time series; traffic prediction
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204605. Data management and data science
Byline AffiliationsHebei University of Technology, China
School of Mathematics, Physics and Computing
Swinburne University of Technology
Macquarie University
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