research-article

Multi-Task Synchronous Graph Neural Networks for Traffic Spatial-Temporal Prediction

Authors:

He Li,

Duo Jin,

Xuejiao Li,

Jianbin Huang,

Jaesoo YooAuthors Info & Claims

SIGSPATIAL '21: Proceedings of the 29th International Conference on Advances in Geographic Information Systems

Pages 137 - 140

https://doi.org/10.1145/3474717.3483921

Published: 04 November 2021 Publication History

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Abstract

Traffic spatial-temporal prediction is of great significance to traffic management and urban construction. In this paper, we propose a multi-task graph Synchronous neural network (MTSGNN) to synchronously predict the spatial-temporal data at the regions and transitions between regions. The method of constructing "multitask graph representation" is proposed to retain the information of regions and transitions that existing works can not reflect. Then our model synchronously captures multiple types of dynamic spatial correlations, models dynamic temporal dependencies and re-weights different time steps to solve the problem of long-term time modeling. In three real data sets, we verify the validity of the proposed model.

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Jin GLiang YFang YShao ZHuang JZhang JZheng Y(2024)Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.333382436:10(5388-5408)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1109/TKDE.2023.3333824
Lu JXie ZChen JLi MXu CCao H(2023)GC-SALM: Multi-Task Runoff Prediction Using Spatial-Temporal Attention Graph Convolution Networks2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC53992.2023.10394287(3633-3638)Online publication date: 1-Oct-2023
https://doi.org/10.1109/SMC53992.2023.10394287
Huang HHe STabatabaie M(2023)Extreme-Aware Local-Global Attention for Spatio-Temporal Urban Mobility Learning2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00086(1059-1070)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00086

Index Terms

Multi-Task Synchronous Graph Neural Networks for Traffic Spatial-Temporal Prediction
1. Computing methodologies
  1. Artificial intelligence
2. Theory of computation
  1. Design and analysis of algorithms
    1. Graph algorithms analysis
      1. Dynamic graph algorithms

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SIGSPATIAL '21: Proceedings of the 29th International Conference on Advances in Geographic Information Systems

November 2021

700 pages

ISBN:9781450386647

DOI:10.1145/3474717

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 04 November 2021

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National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)
National Natural Science Foundation of China
MSIT(Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program
Natural Science Foundation of Shaanxi Province

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SIGSPATIAL '21

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SIGSPATIAL

SIGSPATIAL '21: 29th International Conference on Advances in Geographic Information Systems

November 2 - 5, 2021

Beijing, China

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Overall Acceptance Rate 257 of 1,238 submissions, 21%

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Jin GLiang YFang YShao ZHuang JZhang JZheng Y(2024)Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.333382436:10(5388-5408)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1109/TKDE.2023.3333824
Lu JXie ZChen JLi MXu CCao H(2023)GC-SALM: Multi-Task Runoff Prediction Using Spatial-Temporal Attention Graph Convolution Networks2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC53992.2023.10394287(3633-3638)Online publication date: 1-Oct-2023
https://doi.org/10.1109/SMC53992.2023.10394287
Huang HHe STabatabaie M(2023)Extreme-Aware Local-Global Attention for Spatio-Temporal Urban Mobility Learning2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00086(1059-1070)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00086
Aceto LAnastasiadi ECastiglioni VIngólfsdóttir ALuttik BGorla D(2021)In search of lost timeProceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science10.1109/LICS52264.2021.9470526(1-14)Online publication date: 29-Jun-2021
https://dl.acm.org/doi/10.1109/LICS52264.2021.9470526

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Adaptive Graph Convolution in Spatio-temporal Synchronous Networks for Traffic Flow Prediction

DMGF-Net: An Efficient Dynamic Multi-Graph Fusion Network for Traffic Prediction

Attention Synchronous Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Prediction

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