  The continuous expansion of the urban construction scale has recently
contributed to the demand for the dynamics of traffic intersections that are
managed, making adaptive modellings become a hot topic. Existing deep learning
methods are powerful to fit complex heterogeneous graphs. However, they still
have drawbacks, which can be roughly classified into two categories, 1)
spatiotemporal async-modelling approaches separately consider temporal and
spatial dependencies, resulting in weak generalization and large instability
while aggregating; 2) spatiotemporal sync-modelling is hard to capture
long-term temporal dependencies because of the local receptive field. In order
to overcome above challenges, a \textbf{C}ombined \textbf{D}ynamic
\textbf{V}irtual spatiotemporal \textbf{G}raph \textbf{M}apping
\textbf{(CDVGM)} is proposed in this work. The contributions are the following:
1) a dynamic virtual graph Laplacian ($DVGL$) is designed, which considers both
the spatial signal passing and the temporal features simultaneously; 2) the
Long-term Temporal Strengthen model ($LT^2S$) for improving the stability of
time series forecasting; Extensive experiments demonstrate that CDVGM has
excellent performances of fast convergence speed and low resource consumption
and achieves the current SOTA effect in terms of both accuracy and
generalization. The code is available at
\hyperlink{https://github.com/Dandelionym/CDVGM.}{https://github.com/Dandelionym/CDVGM.}
