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

Authors:

Deshuang Huang,

Jaesoo YooAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data, Volume 17, Issue 7

Article No.: 97, Pages 1 - 19

https://doi.org/10.1145/3586164

Published: 14 April 2023 Publication History

Abstract

Traffic prediction is the core task of intelligent transportation system (ITS) and accurate traffic prediction can greatly improve the utilization of public resources. Dynamic interaction of multiple spatial relationships will influence the accuracy of traffic prediction. However, many existing methods only consider static spatial relationships, which restricts the accuracy of the prediction. To address the above problem, in this article, we propose the Dynamic Multi-Graph Fusion Network (DMGF-Net) to model the spatial-temporal correlations in traffic network. In the DMGF-Net, the fusion graph is designed to leverage and extract the various spatial correlations between different regions by fusing spatial graph, semantic graph, and spatial-semantic graph. Further, to dynamically learn the importance of different neighbors, we design the Dynamic Spatial-Temporal Unit (DSTU), which can adjust the aggregation weights of different neighbors by combining the convolution operation and the attention mechanism. It can selectively aggregate spatial-temporal features from different neighbors. Extensive experiments on three datasets demonstrate that effectiveness of our model, especially on PEMS08, our model achieves an increase of about 8.55% and 7.55% in terms of MAE and RMSE than the static model STGCN.

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Cited By

Sui HYan HZheng THuang WZhuang YLi Y(2024)Congestion-aware Spatio-Temporal Graph Convolutional Network-based A* Search Algorithm for Fastest Route SearchACM Transactions on Knowledge Discovery from Data10.1145/365764018:7(1-19)Online publication date: 19-Jun-2024
https://dl.acm.org/doi/10.1145/3657640
Liu XHao YZhao LLiu GSheng VZhao P(2024)LMACL: Improving Graph Collaborative Filtering with Learnable Model Augmentation Contrastive LearningACM Transactions on Knowledge Discovery from Data10.1145/365730218:7(1-24)Online publication date: 19-Jun-2024
https://dl.acm.org/doi/10.1145/3657302
Fu CYu PYu YHuang CZhao ZDong J(2024)MHGCN+: Multiplex Heterogeneous Graph Convolutional NetworkACM Transactions on Intelligent Systems and Technology10.1145/365004615:3(1-25)Online publication date: 15-Apr-2024
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Index Terms

DMGF-Net: An Efficient Dynamic Multi-Graph Fusion Network for Traffic Prediction
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
    2. Robotics
2. Networks
  1. Network properties
    1. Network reliability

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Information & Contributors

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 17, Issue 7

August 2023

319 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3589018

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

Issue’s Table of Contents

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 the author(s) 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

New York, NY, United States

Publication History

Published: 14 April 2023

Online AM: 03 March 2023

Accepted: 23 February 2023

Revised: 20 February 2023

Received: 30 July 2022

Published in TKDD Volume 17, Issue 7

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STI 2030’Major Projects
Institute of Information & Communications Technology Planning & Evaluation (IITP)
Korea government (MSIT)
Development of High Performance Visual BigData Discovery Platform for Large-Scale Realtime Data Analysis
MSIT (Ministry of Science and ICT), Korea
Grand Information Technology Research Center support program
IITP (Institute for Information & communications Technology Planning & Evaluation

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Sui HYan HZheng THuang WZhuang YLi Y(2024)Congestion-aware Spatio-Temporal Graph Convolutional Network-based A* Search Algorithm for Fastest Route SearchACM Transactions on Knowledge Discovery from Data10.1145/365764018:7(1-19)Online publication date: 19-Jun-2024
https://dl.acm.org/doi/10.1145/3657640
Liu XHao YZhao LLiu GSheng VZhao P(2024)LMACL: Improving Graph Collaborative Filtering with Learnable Model Augmentation Contrastive LearningACM Transactions on Knowledge Discovery from Data10.1145/365730218:7(1-24)Online publication date: 19-Jun-2024
https://dl.acm.org/doi/10.1145/3657302
Fu CYu PYu YHuang CZhao ZDong J(2024)MHGCN+: Multiplex Heterogeneous Graph Convolutional NetworkACM Transactions on Intelligent Systems and Technology10.1145/365004615:3(1-25)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3650046
Yi JChen Z(2024)Deconfounded Cross-modal Matching for Content-based Micro-video Background Music RecommendationACM Transactions on Intelligent Systems and Technology10.1145/365004215:3(1-25)Online publication date: 15-Apr-2024
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Gao CZheng YWang WFeng FHe XLi Y(2024)Causal Inference in Recommender Systems: A Survey and Future DirectionsACM Transactions on Information Systems10.1145/363904842:4(1-32)Online publication date: 9-Feb-2024
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Chai HJiang TYu LBaeza-Yates RBonchi F(2024)Diffusion Model-based Mobile Traffic Generation with Open Data for Network Planning and OptimizationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671544(4828-4838)Online publication date: 25-Aug-2024
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Han DHuang YLiu JLiao KLin K(2024)LSAB: User Behavioral Pattern Modeling in Sequential Recommendation by Learning Self-Attention BiasACM Transactions on Knowledge Discovery from Data10.1145/363262518:3(1-20)Online publication date: 13-Jan-2024
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Jiang ZHuang ALuo QGuan W(2024)Local-Perception-Enhanced Spatial–Temporal Evolving Graph Transformer Network: Citywide Demand Prediction of Taxi and Ride-HailingIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.345084625:11(17105-17121)Online publication date: Nov-2024
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Sun HXue RHu TPan THe LRao YWang ZWang YChen YHe H(2024)Predicting Citywide Crowd Flows in Critical Areas Based on Dynamic Spatio-Temporal NetworkIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2024.33724208:5(3703-3715)Online publication date: Oct-2024
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Zhu ZLi SLiu YZhang XFeng ZHou Y(2024)High-level preferences as positive examples in contrastive learning for multi-interest sequential recommendationWorld Wide Web10.1007/s11280-024-01263-627:2Online publication date: 14-Mar-2024
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