Predicting Traffic Flow Using Dynamic Spatial-Temporal Graph Convolution Networks

Traffic flow prediction plays a key role in the construction of intelligent transportation system. However, due to its complex spatio-temporal dependence and its uncertainty, the research becomes very challenging. Most of the existing studies are based on graph neural networks that model traffic flo...

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Bibliographic Details
Published in:Computers, materials & continua Vol. 78; no. 3; pp. 4343 - 4361
Main Authors: Liu, Yunchang, Wan, Fei, Liang, Chengwu
Format: Journal Article
Language:English
Published: Henderson Tech Science Press 2024
Subjects:
Algorithms
Artificial neural networks
Convolution
Flow graphs
Graph neural networks
Intelligent transportation systems
Nodes
Predictions
Spatiotemporal data
Traffic flow
Traffic models
Transportation networks
ISSN:1546-2226, 1546-2218, 1546-2226
Online Access:Get full text
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Summary:Traffic flow prediction plays a key role in the construction of intelligent transportation system. However, due to its complex spatio-temporal dependence and its uncertainty, the research becomes very challenging. Most of the existing studies are based on graph neural networks that model traffic flow graphs and try to use fixed graph structure to deal with the relationship between nodes. However, due to the time-varying spatial correlation of the traffic network, there is no fixed node relationship, and these methods cannot effectively integrate the temporal and spatial features. This paper proposes a novel temporal-spatial dynamic graph convolutional network (TSADGCN). The dynamic time warping algorithm (DTW) is introduced to calculate the similarity of traffic flow sequence among network nodes in the time dimension, and the spatiotemporal graph of traffic flow is constructed to capture the spatiotemporal characteristics and dependencies of traffic flow. By combining graph attention network and time attention network, a spatiotemporal convolution block is constructed to capture spatiotemporal characteristics of traffic data. Experiments on open data sets PEMSD4 and PEMSD8 show that TSADGCN has higher prediction accuracy than well-known traffic flow prediction algorithms.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1546-2226
1546-2218
1546-2226
DOI:10.32604/cmc.2024.047211