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.
Adaptive graph convolutional recurrent network with transformer and whale optimization algorithm for traffic flow prediction
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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.