• DOI:10.1609/aaai.v33i01.33015668
Corpus ID: 53217065

Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction

@inproceedings{Yao2019RevisitingSS,
  title={Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction},
  author={Huaxiu Yao and Xianfeng Tang and H. Wei and Guanjie Zheng and Z. Li},
  booktitle={AAAI},
  year={2019}
}

Huaxiu Yao, Xianfeng Tang, +2 authors Z. Li

Published in AAAI 2019

Mathematics, Computer Science

Traffic prediction has drawn increasing attention in AI research field due to the increasing availability of large-scale traffic data and its importance in the real world. [...] Key Method To address these two issues, we propose a novel Spatial-Temporal Dynamic Network (STDN), in which a flow gating mechanism is introduced to learn the dynamic similarity between locations, and a periodically shifted attention mechanism is designed to handle long-term periodic temporal shifting. To the best of our knowledge…Expand Abstract

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