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Title	Traffic Prediction Based On Gcn-Lstm Model
ID_Doc	58649
Authors	Wu Z.; Huang M.; Zhao A.; Lan Z.
Year	2021
Published	Journal of Physics: Conference Series, 1972, 1
DOI	http://dx.doi.org/10.1088/1742-6596/1972/1/012107
Abstract	Traffic flow prediction is an important part of intelligent traffic management system. Because there are many irregular data structures in road traffic, in order to improve the accuracy of traffic flow prediction, this paper proposes a combined traffic flow prediction model based on deep learning graph convolution neural network (GCN), long-term memory network (LSTM) and residual network (RESNET). GCN is used to extract the features of topology structure in traffic data, LSTM is used to extract the features of time structure, combined with ResNet to optimize the overall model, reduce the occurrence of gradient disappearance or explosion in network degradation, and finally achieve the prediction of traffic flow. According g to the experimental results, the combined traffic flow prediction model used in this paper is closer to the actual traffic flow occurrence than the traditional convolutional neural network model (CNN), and the accuracy is improved. © Published under licence by IOP Publishing Ltd.
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