LSTAN-GERPE uses spatio-temporal attention, graph embedding, and grid-searched rotational position encoding to achieve advanced accuracy on PeMS04 and PeMS08 traffic forecasting datasets without heavy feature engineering.
Llgformer: Learnable long-range graph transformer for traffic flow prediction,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.AI 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Lightweight Spatio-Temporal Attention Network with Graph Embedding and Rotational Position Encoding for Traffic Forecasting
LSTAN-GERPE uses spatio-temporal attention, graph embedding, and grid-searched rotational position encoding to achieve advanced accuracy on PeMS04 and PeMS08 traffic forecasting datasets without heavy feature engineering.