Subjects: Computer Science >> Computer Application Technology submitted time 2023-02-15 Cooperative journals: 《桂林电子科技大学学报》
Abstract： Traffic forecasting is of great significance in urban management and traffic planning. However, in the task of traffic
prediction, the modeling of complex dynamic spatio-temporal dependence is still a great challenge. For the problem that
the neural network can't capture the long-term traffic information in the spatial dimension, the new neural network structure
proposed in the past can't capture the complex traffic data in the spatial dimension. Through adaptive graph convolutional
network, the specific state of nodes is automatically captured and the interdependence between different nodes is automatically
inferred to extract the complete spatial features of traffic data. Then, the time characteristics of traffic data are captured
by the time memory module in the spatio-temporal short-term memory network, and the short, medium and long-term time
dependence is simulated.
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