Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting

Liu, Hangchen1; Dong, Zheng1; Jiang, Renhe2; Deng, Jiewen1; Deng, Jinliang3; Chen, Quanjun2; Song, Xuan1

10.1145/3583780.3615160

2023

32nd ACM International Conference on Information and Knowledge Management (CIKM)

PROCEEDINGS OF THE 32ND ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, CIKM 2023

OCT 21-25, 2023

null,Birmingham,ENGLAND

1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES

ASSOC COMPUTING MACHINERY

With the rapid development of the Intelligent Transportation System (ITS), accurate traffic forecasting has emerged as a critical challenge. The key bottleneck lies in capturing the intricate spatio-temporal traffic patterns. In recent years, numerous neural networks with complicated architectures have been proposed to address this issue. However, the advancements in network architectures have encountered diminishing performance gains. In this study, we present a novel component called spatio-temporal adaptive embedding that can yield outstanding results with vanilla transformers. Our proposed Spatio-Temporal Adaptive Embedding transformer (STAEformer) achieves state-of-the-art performance on five real-world traffic forecasting datasets. Further experiments demonstrate that spatio-temporal adaptive embedding plays a crucial role in traffic forecasting by effectively capturing intrinsic spatio-temporal relations and chronological information in traffic time series.

traffic forecasting spatio-temporal embedding transformer

第一 ; 通讯

英语

EI ; CPCI-S

National Key Research and Development Program of China[2021YFB1714400]

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Information Systems

WOS:001161549504033

Web of Science

1.Southern University of Science and Technology, China
2.The University of Tokyo, Japan
3.University of Technology, Sydney, Australia

Liu, Hangchen,Dong, Zheng,Jiang, Renhe,et al. Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting[C]. 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES:ASSOC COMPUTING MACHINERY,2023.