• Corpus ID: 245650514

A Lightweight and Accurate Spatial-Temporal Transformer for Traffic Forecasting

@article{Li2022ALA,
  title={A Lightweight and Accurate Spatial-Temporal Transformer for Traffic Forecasting},
  author={Guanyao Li and Shuhan Zhong and Letian Xiang and Shueng-Han Gary Chan and Ruiyuan Li and Chih-Chieh Hung and Wen-Chih Peng},
  journal={ArXiv},
  year={2022},
  volume={abs/2201.00008}
}
—We study the forecasting problem for traffic with dynamic, possibly periodical, and joint spatial-temporal dependency between regions. Given the aggregated inflow and outflow traffic of regions in a city from time slots 0 to t − 1 , we predict the traffic at time t at any region. Prior arts in the area often consider the spatial and temporal dependencies in a decoupled manner, or are rather computationally intensive in training with a large number of hyper-parameters to tune. We propose ST-TIS, a… 

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