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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Bibliographic Details
Published in:	IEEE transactions on knowledge and data engineering Vol. 34; no. 11; pp. 5415 - 5428
Main Authors:	Guo, Shengnan, Lin, Youfang, Wan, Huaiyu, Li, Xiucheng, Cong, Gao
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.11.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
Online Access:	Get full text
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