MLPST : MLP is All You Need for Spatio-Temporal Prediction

Zijian ZHANG, Ze HUANG, Zhiwei HU, Xiangyu ZHAO*, Wanyu WANG, Zitao LIU, Junbo ZHANG, S. Joe QIN, Hongwei ZHAO

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Research

1 Citation (Scopus)


Traffic prediction is a typical spatio-temporal data mining task and has great significance to the public transportation system. Considering the demand for its grand application, we recognize key factors for an ideal spatio-temporal prediction method: efficient, lightweight, and effective. However, the current deep model-based spatio-temporal prediction solutions generally own intricate architectures with cumbersome optimization, which can hardly meet these expectations. To accomplish the above goals, we propose an intuitive and novel framework, MLPST, a pure multi-layer perceptron architecture for traffic prediction. Specifically, we first capture spatial relationships from both local and global receptive fields. Then, temporal dependencies in different intervals are comprehensively considered. Through compact and swift MLP processing, MLPST can well capture the spatial and temporal dependencies while requiring only linear computational complexity, as well as model parameters that are more than an order of magnitude lower than baselines. Extensive experiments validated the superior effectiveness and efficiency of MLPST against advanced baselines, and among models with optimal accuracy, MLPST achieves the best time and space efficiency.
Original languageEnglish
Title of host publicationCIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery (ACM)
Number of pages10
ISBN (Electronic)9798400701245
ISBN (Print)9798400701245
Publication statusPublished - Oct 2023
EventThe 32nd ACM International Conference on Information and Knowledge Management - Birmingham, United Kingdom
Duration: 21 Oct 202325 Oct 2023


ConferenceThe 32nd ACM International Conference on Information and Knowledge Management
Abbreviated titleCIKM ’23
Country/TerritoryUnited Kingdom

Bibliographical note

Publisher Copyright:
© 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM. ACM ISBN 979-8-4007-0124-5/23/10...$15.00.


  • Spatio-Temporal Data Mining
  • Traffic Prediction
  • MLP-Mixer


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