Learning Dynamics and Heterogeneity of Spatial-Temporal Graph Data for Traffic Forecasting

Accurate traffic forecasting is critical in improving safety, stability, and efficiency of intelligent transportation systems. Despite years of studies, accurate traffic prediction still faces the following challenges, including modeling the dynamics of trafﬁc data along both temporal and spatial di...

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Vydané v:	IEEE transactions on knowledge and data engineering Ročník 34; číslo 11; s. 5415 - 5428
Hlavní autori:	Guo, Shengnan, Lin, Youfang, Wan, Huaiyu, Li, Xiucheng, Cong, Gao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.11.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Convolution Correlation Data models Detectors Forecasting graph convolution Graph neural networks Heterogeneity Intelligent transportation systems Mathematical models Modules Prediction models Predictive models Roads self-attention Spatial data spatial-temporal graph data Spatiotemporal data Traffic flow Traffic forecasting Traffic information
ISSN:	1041-4347, 1558-2191
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