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时空图神经网络在交通流预测研究中的构建与应用综述

汪维泰 王晓强 李雷孝 陶乙豪 林浩

计算机工程与应用2024,Vol.60Issue(8):31-45,15.
计算机工程与应用2024,Vol.60Issue(8):31-45,15.DOI:10.3778/j.issn.1002-8331.2307-0133

时空图神经网络在交通流预测研究中的构建与应用综述

Review of Construction and Applications of Spatio-Temporal Graph Neural Network in Traffic Flow Prediction

汪维泰 1王晓强 1李雷孝 2陶乙豪 1林浩3

作者信息

  • 1. 内蒙古工业大学 信息工程学院,呼和浩特 010080
  • 2. 内蒙古工业大学 数据科学与应用学院,呼和浩特 010080
  • 3. 天津理工大学 计算机科学与工程学院,天津 300384
  • 折叠

摘要

Abstract

The prediction of traffic flow is a pivotal concern within urban traffic management and planning,yet conven-tional forecasting techniques prove inadequate in addressing challenges like data sparsity,nonlinear associations,and intri-cate dynamics.Graph neural network is a deep learning approach based on non-Euclidean structural data,which has been widely used in various complex network modeling and predictive tasks in recent times.To address traffic flow prediction,a spatiotemporal graph neural network is proposed,which can capture spatial and temporal correlations,making signifi-cant progress compared to earlier predictive models.An analysis is conducted on models utilizing spatiotemporal graph neural network for the prediction of traffic flow in recent times Firstly,various construction methods of adjacency matri-ces are summarized and compared.Then,the common components of traffic flow prediction models are listed from the perspective of spatial correlation and temporal correlation,and different spatio-temporal fusion modes are classified and compared.On the application front,spatiotemporal graph neural network models are categorized into three classes based on temporal scales:long-term prediction,short-term prediction,and combined long-short-term prediction.Analysis of respective objectives and requisites is conducted,accompanied by enumeration and comparison of prominent recent models.Finally,limitations of existing research are deliberated upon,and prospects for future studies pertaining to rele-vant models are outlined.

关键词

智能交通/交通流量预测/时间序列预测/深度学习/图神经网络

Key words

smart transportation/traffic flow forecasting/time series forecasting/deep learning/graph neural network

分类

信息技术与安全科学

引用本文复制引用

汪维泰,王晓强,李雷孝,陶乙豪,林浩..时空图神经网络在交通流预测研究中的构建与应用综述[J].计算机工程与应用,2024,60(8):31-45,15.

基金项目

内蒙古自然科学基金(2023MS06021) (2023MS06021)

自治区直属高校基本科研业务费项目(JY20230065) (JY20230065)

内蒙古自治区科技计划项目(2020GG0104) (2020GG0104)

内蒙古自治区科技成果转化专项资金项目(2020CG0073,2021CG0033) (2020CG0073,2021CG0033)

内蒙古自治区重点研发与成果转化计划项目(2022YFSJ0013). (2022YFSJ0013)

计算机工程与应用

OA北大核心CSTPCD

1002-8331

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