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.
Enhancing spatial-temporal awareness via graph convolutional networks and transformers for traffic flow forecasting,
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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.