research-article

Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting

Authors:

Kunqing XieAuthors Info & Claims

KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

Pages 364 - 373

https://doi.org/10.1145/3447548.3467430

Published: 14 August 2021 Publication History

Abstract

Spatial-temporal forecasting has attracted tremendous attention in a wide range of applications, and traffic flow prediction is a canonical and typical example. The complex and long-range spatial-temporal correlations of traffic flow bring it to a most intractable challenge. Existing works typically utilize shallow graph convolution networks (GNNs) and temporal extracting modules to model spatial and temporal dependencies respectively. However, the representation ability of such models is limited due to: (1) shallow GNNs are incapable to capture long-range spatial correlations, (2) only spatial connections are considered and a mass of semantic connections are ignored, which are of great importance for a comprehensive understanding of traffic networks. To this end, we propose Spatial-Temporal Graph Ordinary Differential Equation Networks (STGODE).1 Specifically, we capture spatial-temporal dynamics through a tensor-based ordinary differential equation (ODE), as a result, deeper networks can be constructed and spatial-temporal features are utilized synchronously. To understand the network more comprehensively, semantical adjacency matrix is considered in our model, and a well-design temporal dialated convolution structure is used to capture long term temporal dependencies. We evaluate our model on multiple real-world traffic datasets and superior performance is achieved over state-of-the-art baselines.

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Cited By

Zhang LXu SLi SPan LGong S(2025)D-MGDCN-CLSTM: A Traffic Prediction Model Based on Multi-Graph Gated Convolution and Convolutional Long–Short-Term MemorySensors10.3390/s2502056125:2(561)Online publication date: 19-Jan-2025
https://doi.org/10.3390/s25020561
Chen HWang HChen Z(2025)Traffic Flow Prediction Based on Dynamic Time Slot Graph ConvolutionTransportation Research Record: Journal of the Transportation Research Board10.1177/03611981241308868Online publication date: 24-Jan-2025
https://doi.org/10.1177/03611981241308868
Su JWang HPrzystupa KKochan OLiu D(2025)Dynamic Spatio-Temporal Residual Hypergraph Convolutional Networks for Traffic Flow ForecastingTransportation Research Record: Journal of the Transportation Research Board10.1177/03611981241295706Online publication date: 29-Jan-2025
https://doi.org/10.1177/03611981241295706
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Index Terms

Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
2. Networks
  1. Network properties
    1. Network structure

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Published In

cover image ACM Conferences

KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

August 2021

4259 pages

ISBN:9781450383325

DOI:10.1145/3447548

General Chairs:
Feida Zhu
Singapore Management University
,
Beng Chin Ooi
National University of Singapore
,
Chunyan Miao
Nanyang Technology University
,
Program Chairs:
Haixun Wang,
Iryna Skrypnyk,
Wynne Hsu,
Sanjay Chawla

Copyright © 2021 ACM.

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Published: 14 August 2021

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KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 14 - 18, 2021

Virtual Event, Singapore

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Zhang LXu SLi SPan LGong S(2025)D-MGDCN-CLSTM: A Traffic Prediction Model Based on Multi-Graph Gated Convolution and Convolutional Long–Short-Term MemorySensors10.3390/s2502056125:2(561)Online publication date: 19-Jan-2025
https://doi.org/10.3390/s25020561
Chen HWang HChen Z(2025)Traffic Flow Prediction Based on Dynamic Time Slot Graph ConvolutionTransportation Research Record: Journal of the Transportation Research Board10.1177/03611981241308868Online publication date: 24-Jan-2025
https://doi.org/10.1177/03611981241308868
Su JWang HPrzystupa KKochan OLiu D(2025)Dynamic Spatio-Temporal Residual Hypergraph Convolutional Networks for Traffic Flow ForecastingTransportation Research Record: Journal of the Transportation Research Board10.1177/03611981241295706Online publication date: 29-Jan-2025
https://doi.org/10.1177/03611981241295706
Mao ZJu WYi SWang YXiao ZLong QYin NLiu XZhang M(2025)Learning Knowledge-diverse Experts for Long-tailed Graph ClassificationACM Transactions on Knowledge Discovery from Data10.1145/370532319:2(1-24)Online publication date: 11-Jan-2025
https://dl.acm.org/doi/10.1145/3705323
Shao ZWang FXu YWei WYu CZhang ZYao DSun TJin GCao XCong GJensen CCheng X(2025)Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity AnalysisIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.348445437:1(291-305)Online publication date: Jan-2025
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He SGuo QLi GXie KSharma P(2025)Multivariate Time Series Anomaly Detection Based on Multiple Spatiotemporal Graph ConvolutionIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2024.349389074(1-14)Online publication date: 2025
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Guo ALiu YShao SShi XFeng Z(2025)Spatial-Temporal Fusion Graph Neural Networks With Mixed Adjacency for Weather ForecastingIEEE Access10.1109/ACCESS.2025.353247313(15812-15824)Online publication date: 2025
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Zhu LGao JZhu CDeng F(2025)Short-term power load forecasting based on spatial-temporal dynamic graph and multi-scale TransformerJournal of Computational Design and Engineering10.1093/jcde/qwaf01312:2(92-111)Online publication date: 27-Jan-2025
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