Journal of System Simulation
Abstract
Abstract: Accurate short-term vehicle speed prediction helps to resolve city traffic congestion problems. Focusing on the defect that CNN cannot process non-Euclidean geometric data, GCN and BiLSTM are combined to fully process the spatiotemporal characteristics of road network information, in which the advantages of GCN integrating global features and the ability of BiLSTM to extract temporal features are considered. In order to reduce the interference of noise to the data, variational modal decomposition (VMD) is introduced and short-term vehicle speed prediction model based on VMD-GCN-BiLSTM (VGBLSTM) is proposed . Simulation results show that the prediction accuracy of VGBLSTM model is significantly improved, especially the fitting effects at peak and trough moments. The new proposed approach can be a reference for traffic planning.
Recommended Citation
Zhang, Kai; Lu, Haipeng; Han, Ying; Zhang, Lingyun; and Ding, Yujie
(2023)
"Short-term Vehicle Speed Prediction With Spatiotemporal Convolution Fused With Variational Modal Decomposition,"
Journal of System Simulation: Vol. 35:
Iss.
8, Article 2.
DOI: 10.16182/j.issn1004731x.joss.22-0394
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol35/iss8/2
First Page
1651
Last Page
1660
CLC
TP391.9
Recommended Citation
Zhang Kai, Lu Haipeng, Han Ying, et al. Short-term Vehicle Speed Prediction With Spatiotemporal Convolution Fused With Variational Modal Decomposition[J]. Journal of System Simulation, 2023, 35(8): 1651-1660.
DOI
10.16182/j.issn1004731x.joss.22-0394
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