Dynamic traveling time forecasting based on spatial-temporal graph convolutional networks

Graphical abstract

References

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (NSFC) (Grant No. 62002216), the Shanghai Sailing Program (No. 20YF1414400), the Collaborative Innovation Platform of Electronic Information Master of Shanghai Polytechnic University (SSPU) (No. A10GY21F015), the Research Projects of Shanghai Polytechnic University (Nos. EGD22QD03, EGD23DS05), the Key Disciplines of Computer Science and Technology of SSPU and the Construction of Electronic Information Master Degree of SSPU.

Competing interests

The authors declare that they have no competing interests or financial conflicts to disclose.

Supporting information

The supporting information is available online at journal.hep.com.cn and link.springer.com.

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