Spatiotemporal interactive progressive dynamic graph convolutional networks for traffic flow prediction

L Chen et al.

Transportation Letters2026https://doi.org/10.1080/19427867.2026.2613143article
ABDC B
Weight
0.50

What the paper says

No abstract available.

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.1080/19427867.2026.2613143

Or copy a formatted citation

@article{l2026,
  title        = {{Spatiotemporal interactive progressive dynamic graph convolutional networks for traffic flow prediction}},
  author       = {L Chen et al.},
  journal      = {Transportation Letters},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1080/19427867.2026.2613143},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Spatiotemporal interactive progressive dynamic graph convolutional networks for traffic flow prediction

Flags are reviewed by the Arbiter methodology team within 5 business days.


Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.