Generalized spatial–temporal regression graph convolutional transformer for traffic forecasting
Lang Xiong,
Liyun Su,
Shiyi Zeng
et al.
Abstract:Spatial–temporal data is widely available in intelligent transportation systems, and accurately solving non-stationary of spatial–temporal regression is critical. In most traffic flow prediction research, the non-stationary solution of deep spatial–temporal regression tasks is typically formulated as a spatial–temporal graph modeling problem. However, there are several issues: (1) the coupled spatial–temporal regression approach renders it unfeasible to accurately learn the dependencies of diverse modalities; … Show more
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