图神经网络驱动的交通预测技术：探索与挑战

周毅; 胡姝婷; 李伟; 承楠; 路宁; 沈学民

doi:10.11959/j.issn.2096-3750.2021.00235

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图神经网络驱动的交通预测技术：探索与挑战

前沿与综述 | 更新时间：2024-06-05

- 图神经网络驱动的交通预测技术：探索与挑战
- Graph neural network driven traffic prediction technology:review and challenge
- 物联网学报 2021年5卷第4期页码：1-16
- 作者机构：
  
  1. 河南大学，河南郑州 450046
  2. 西安电子科技大学，陕西西安 710071
  3. 加拿大女王大学，加拿大金斯顿 K7L 3N6
  4. 滑铁卢大学，加拿大滑铁卢 N2L 3G1
- 作者简介：
  
  [ "周毅（1981− ），男，博士，河南大学教授、博士生导师，河南省车联网协同技术国际联合实验室主任，主要研究方向为车联网与智能交通、空地协同组网、平行增强学习、协作机器人等" ]
  [ "胡姝婷（1997− ），女，河南大学硕士生，主要研究方向为图神经网络、智能交通等" ]
  [ "李伟（1979− ），女，河南大学副教授、硕士生导师，主要研究方向为车联网优化控制、协作通信等" ]
  [ "承楠（1987− ），男，博士，西安电子科技大学教授、博士生导师，主要研究方向为车联网与先进交通系统、人工智能、空天地一体化网络等" ]
  [ "路宁（1984− ），男，博士，加拿大女王大学助理教授，主要研究方向为车联网与智能交通、深度强化学习、移动边缘计算等" ]
  [ "沈学民（1958− ），男，博士，中国工程院外籍院士，加拿大工程院、工程研究院院士以及皇家学会会士，加拿大滑铁卢大学教授，IEEE Fellow，主要研究方向为空天地一体化网络、车联网、网络安全、人工智能及智能电网等" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(62176088);国家自然科学基金资助项目;The National Natural Science Foundation of China(61701170);国家自然科学基金资助项目;The National Natural Science Foundation of China(62071356);河南省科技攻关项目;The Henan Science and Technology Development Program(202102310198);河南省科技攻关项目;The Henan Science and Technology Development Program(212102210412)
- DOI：10.11959/j.issn.2096-3750.2021.00235
  中图分类号： TP391
- 纸质出版日期：2021-12-30，
  
  网络出版日期：2021-12，
- 稿件说明：
移动端阅览
周毅, 胡姝婷, 李伟, 等. 图神经网络驱动的交通预测技术：探索与挑战[J]. 物联网学报, 2021,5(4):1-16.

YI ZHOU, SHUTING HU, WEI LI, et al. Graph neural network driven traffic prediction technology:review and challenge. [J]. Chinese journal on internet of things, 2021, 5(4): 1-16.
周毅, 胡姝婷, 李伟, 等. 图神经网络驱动的交通预测技术：探索与挑战[J]. 物联网学报, 2021,5(4):1-16. DOI： 10.11959/j.issn.2096-3750.2021.00235.

YI ZHOU, SHUTING HU, WEI LI, et al. Graph neural network driven traffic prediction technology:review and challenge. [J]. Chinese journal on internet of things, 2021, 5(4): 1-16. DOI： 10.11959/j.issn.2096-3750.2021.00235.

摘要

随着物联网及人工智能技术的快速发展，对交通数据进行精准的分析和预测成为智慧交通的首要环节。近年来，交通预测方法逐渐从经典的模型驱动转变为数据驱动，然而，如何通过大数据有效分析路网的时空特性是预测过程中面临的关键难题之一。时空大数据分析是交通预测的利器，将交通路网建模为图网络，将深度学习方法在图网络上进行扩展，通过图神经网络建立时空预测模型，采用图卷积的方式有效地获取路网传感器节点之间的时空相关性，可以显著提高交通预测模型的精度。针对图神经网络驱动的交通预测技术进行了探索，基于深度时空特性分析提炼了两大类交通预测模型，并通过实例进行分析和验证，探讨了图神经网络在交通预测领域的技术优势和主要挑战，挖掘了图神经网络预测机制的潜在研究方向。

Abstract

With the rapid development of Internet of things and artificial intelligence technology

accurate analysis and prediction of traffic data have become the primary target of intelligent transportations.In recent years

the method of traffic forecasting has gradually changed from the classical model-driven type to the data-driven type.However

how to effectively analyze the spatial-temporal characteristics of road networks through big data is one of the key issues in the traffic prediction process.Spatiotemporal big data analysis is a powerful tool for the traffic prediction.The traffic network can be modeled as a graph network

while the deep learning method can be extended on the graph network.Utilizing graph neural networks

we can build the spatiotemporal prediction model

and obtain the spatial-temporal correlation between the sensor nodes in road networks effectively by using graph convolution

which can significantly improve the accuracy of traffic prediction models.The traffic forecasting technology driven by graph neural networks was explored

and two kinds of traffic prediction models based on the analysis of deep spatial-temporal characteristics were extracted.The actual cases were analyzed and evaluated to discuss the technical advantages and key challenges of graph neural networks in the traffic prediction.The potential issues of graph neural network driven prediction mechanisms were also excavated.

关键词

交通预测图神经网络时空相关性同步卷积图注意力网络

Keywords

traffic predictiongraph neural networksspatial-temporal correlationsynchronous convolutiongraph at-tention networks

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