A Deep Learning Approach for Long-Term Traffic Flow Prediction With Multifactor Fusion Using Spatiotemporal Graph Convolutional Network

As a vital research subject in the field of intelligent transportation systems (ITSs), traffic flow prediction using deep learning methods has attracted much attention in recent years. However, numerous existing studies mainly focus on short-term traffic flow predictions and fail to consider the inf...

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Veröffentlicht in:	IEEE transactions on intelligent transportation systems Jg. 24; H. 8; S. 1 - 14
Hauptverfasser:	Qi, Xiaoyu, Mei, Gang, Tu, Jingzhi, Xi, Ning, Piccialli, Francesco
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Artificial neural networks Atmospheric models Convolution Data models Deep learning external factor Flow stability Forecasting Intelligent transportation systems Intelligent transportation systems (ITS) Logic gates long-term traffic flow prediction Predictive models spatiotemporal graph convolutional network Spatiotemporal phenomena Traffic flow Transportation networks Weather
ISSN:	1524-9050, 1558-0016
Online-Zugang:	Volltext
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