Conference Paper2021Cited by 4

Unified spatio-temporal modeling for traffic forecasting using graph neural network

Amit Roy, Kashob Kumar Roy, Amin Ahsan Ali, M Ashraful Amin, AKM Mahbubur Rahman

2021 International Joint Conference on Neural Networks (IJCNN)

IEEE, pp. 1–8

  • Sustainable Cities and Communities — Relevance: 45%
DOI: 10.1109/ijcnn52387.2021.9533319Open AccessURL

Abstract

Research in deep learning models to forecast traffic intensities has gained great attention in recent years due to their capability to capture the complex spatio-temporal relationships within the traffic data. However, most state-of-the-art approaches have designed spatial-only (e.g. Graph Neural Networks) and temporal-only (e.g. Recurrent Neural Networks) modules to separately extract spatial and temporal features. However, we argue that it is less effective to extract the complex spatiotemporal relationship with such factorized modules. Besides, most existing works predict the traffic intensity of a particular time interval only based on the traffic data of the previous one hour of that day. And thereby ignores the repetitive daily/weekly pattern that may exist in the last hour of data. Therefore, we propose a Unified Spatio-Temporal Graph Convolution Network (USTGCN) for traffic forecasting that performs both spatial and temporal aggregation through direct information propagation across different timestamp nodes with the help of spectral graph convolution on a spatio-temporal graph. Furthermore, it captures historical daily patterns in previous days and current-day patterns in current-day traffic data. Finally, we validate our work's effectiveness through experimental analysis11Code is available at github.com/AmitRoy7781/USTGCN, which shows that our model USTGCN can outperform state-of-the-art performances in three popular benchmark datasets from the Performance Measurement System (PeMS). Moreover, the training time is reduced significantly with our proposed USTGCN model.

Keywords

  • spectral graph convolution
  • traffic prediction
  • graph neural networks
  • spatio-temporal graph convolutional network
  • spatio-temporal graph
  • PeMS dataset

CCDS Authors

Amit Roy
Amit Roy

Amit Roy

  • Research Assistant, CCDS

PhD Student

Purdue University

Published at

PAKDD, IJCNN

Research Interests

Machine Learning

Kashob Kumar Roy
Kashob Kumar Roy

Kashob Kumar Roy

  • Research Assistant, CCDS

PhD Student

University of Illinois Urbana-Champaign

Published at

PAKDD, IJCNN

Research Interests

Machine Learning

Amin Ahsan Ali, PhD
Amin Ahsan Ali, PhD

Amin Ahsan Ali, PhD

  • Director, AI/ML Wing

Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

ACL, CHI, UbiComp, ECCV, IJCAI, ECAI, WACV, IPSN, PAKDD, LREC, IJCNN, ICPR, ICIP, BEA, BLP, ICMLA, Scientific Reports, Scientific Data, Pattern Recognition, Information Sciences, Ad Hoc Networks, Drug and Alcohol Dependence, PLOS ONE

Research Interests

Artificial Intelligence, Machine Learning

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Professor

+1 more affiliation

Department of Information Sciences and Technology

George Mason University, USA

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics

AKM Mahbubur Rahman, PhD
AKM Mahbubur Rhman, PhD

AKM Mahbubur Rahman, PhD

  • Director, DS & IPT Wings

Associate Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

CHI, ECCV, WACV, PAKDD, LREC, IJCNN, ICPR, ICIP, HCII, BEA, Scientific Reports, Information Sciences

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Cited By

  1. 1.Yun Li, Dazhou Yu, Zhenke Liu, Minxing Zhang, Xiaoyun Gong, Liang Zhao. (2024). Graph Neural Network for Spatiotemporal Data: Methods and Applications. SSRN Electronic Journal[10.2139/ssrn.4725185]
  2. 2.Xiao Wu, Hua Huang, Tong Zhou, Yudan Tian, Shisen Wang, Jingting Wang. (2025). An urban road traffic flow prediction method based on multi-information fusion. Scientific Reports, 15(1), 5568[10.1038/s41598-025-88429-y]
  3. 3.Yuen Hoi Lau, Raymond Chi-Wing Wong. (2021). Spatio-Temporal Graph Convolutional Networks for Traffic Forecasting. , 427–430[10.1145/3474717.3484207]
  4. 4.Jian Yang, Jinhong Li, Lu Wei, Lei Gao, Fuqi Mao. (2022). ST-AGRNN: A Spatio-Temporal Attention-Gated Recurrent Neural Network for Traffic State Forecasting. Journal of Advanced Transportation, 2022, 1–17[10.1155/2022/2806183]
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