A spatio-temporal grammar graph attention network with adaptive edge information for traffic flow prediction
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-023-05020-6.pdf
Reference38 articles.
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3. Hou Q, Leng J, Ma G, Liu W, Cheng Y (2019) An adaptive hybrid model for short-term urban traffic flow prediction. Phys A 527:121065–121074
4. Wang W, Zhang H, Li T, Guo J, Huang W, Wei Y, Cao J (2020) An interpretable model for short term traffic flow prediction. Math Comput Simul 171:264–278
5. Emami A, Sarvi M, Bagloee SA (2020) Short-term traffic flow prediction based on faded memory Kalman filter fusing data from connected vehicles and Bluetooth sensors. Simul Model Pract Theory 102:102025–102041
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1. FeSTGCN: A frequency-enhanced spatio-temporal graph convolutional network for traffic flow prediction under adaptive signal timing;Applied Intelligence;2024-03
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