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图神经网络在交通预测中的应用综述

户佐安 邓锦程 韩金丽 袁凯

户佐安, 邓锦程, 韩金丽, 袁凯. 图神经网络在交通预测中的应用综述[J]. 交通运输工程学报, 2023, 23(5): 39-61. doi: 10.19818/j.cnki.1671-1637.2023.05.003
引用本文: 户佐安, 邓锦程, 韩金丽, 袁凯. 图神经网络在交通预测中的应用综述[J]. 交通运输工程学报, 2023, 23(5): 39-61. doi: 10.19818/j.cnki.1671-1637.2023.05.003
HU Zuo-an, DENG Jin-cheng, HAN Jin-li, YUAN Kai. Review on application of graph neural network in traffic prediction[J]. Journal of Traffic and Transportation Engineering, 2023, 23(5): 39-61. doi: 10.19818/j.cnki.1671-1637.2023.05.003
Citation: HU Zuo-an, DENG Jin-cheng, HAN Jin-li, YUAN Kai. Review on application of graph neural network in traffic prediction[J]. Journal of Traffic and Transportation Engineering, 2023, 23(5): 39-61. doi: 10.19818/j.cnki.1671-1637.2023.05.003

图神经网络在交通预测中的应用综述

doi: 10.19818/j.cnki.1671-1637.2023.05.003
基金项目: 

国家重点研发计划 2018YFB1601400

四川省科技计划项目 2021YJ0067

详细信息
    作者简介:

    户佐安(1979-),男,湖北黄梅人,西南交通大学副教授,工学博士,从事运输组织理论与系统优化研究

  • 中图分类号: U491.14

Review on application of graph neural network in traffic prediction

Funds: 

National Key Research and Development Program of China 2018YFB1601400

Science and Technology Project of Sichuan Province 2021YJ0067

More Information
  • 摘要: 为寻求提升交通预测时空计算任务性能的有效途径,探索图神经网络技术在交通预测中的应用前景和挑战,回顾了交通预测方法的发展,总结了模型驱动方法、统计模型、传统机器学习方法和深度学习方法的优势和局限性;阐述了图网络和交通网络的适配性,归纳了图的构造方法;将应用于交通预测的数据进行了分类,分析了不同交通预测任务的共性与差异;归纳了常用于交通预测任务的图神经网络模型,包括卷积图神经网络、图注意网络、图自编码器和图时空网络,分析了图神经网络模型应用于交通预测时主要考虑的因素和时空模块;对比了多种交通速度预测方法的性能,分析了图神经网络框架中不同组件对其预测性能的影响;从数据多源性、应用多样性、多模式、动态性、模型可解释性、不确定性和小样本学习等多个角度探讨了基于图神经网络的交通预测面临的挑战和机遇,并针对图神经网络发展趋势提出了相关建议。研究结果表明:与只考虑时间相关性的基准模型相比,基于图神经网络的预测方法性能显著提升;多模式时间相关性、时空注意力机制、边特征、外部数据均会显著影响预测性能;图神经网络为建模交通网络复杂动态的时空相关性提供了有力手段,目前针对交通状态预测问题开发了多样化的模型;未来研究可重点从设计高效的动态时空模块集成架构、设计有效整合外部数据的模块、拓展多样化的应用任务、实现多模式交通同步预测、开发高效可靠和易于解释的模型等方面进行突破,实现预测准确性和效率均衡提升,以期发展更高阶段的智慧交通。

     

  • 图  1  常见的交通预测方法

    Figure  1.  Common traffic prediction methods

    图  2  基于图的交通预测时空关系

    Figure  2.  Spatial-temporal relationship of graph-based traffic predictions

    图  3  不同模态的预定义关联

    Figure  3.  Predefined connections for different modes

    图  4  不同形式的数据类型

    Figure  4.  Data types with different forms

    图  5  交通流量密度与速度关系及预测结果

    Figure  5.  Relationship between traffic flow density and speed and prediction results

    图  6  VGAE模型图生成流程

    Figure  6.  Generation process of VGAE model graph

    图  7  时间和空间相关性的捕获

    Figure  7.  Captures of temporal and spatial correlations

    图  8  各模型在METR-LA数据集下预测准确性对比

    Figure  8.  Comparison of prediction accuracies of various models under METR-LA dataset

    图  9  各模型在PeMS-BAY数据集下预测准确性对比

    Figure  9.  Comparison of prediction accuracies of various models under PeMS-BAY dataset

    图  10  融合矩阵的集成过程

    Figure  10.  Integration process of fusion matrix

    图  11  基于拓扑结构和动态出行模式的超图构建

    Figure  11.  Hypergraph constructions based on topology and dynamic travel modes

    表  1  交通预测方法对比

    Table  1.   Comparison of traffic prediction methods

    类型 方法 优势 不足
    模型驱动 交通流模型 能够表征单个交通个体的运动特征或者整体交通流的运行规律,可解释性强 模型泛化能力弱,面对不同场景需重新建模和标定参数
    元胞自动机模型 在空间和时间上离散,能够描述真实的交通行为 元胞的形状和排列过于规整,且难以整合系统外部因素的影响
    数据驱动 统计方法 坚实的数学理论基础 平稳性假设导致准确性降低,非线性关系捕获能力不足
    传统机器学习方法 充分捕获非线性关系 受限于浅层架构和人工特征选择方式
    时序的深度学习方法 深入挖掘交通数据的时变特征 忽略了交通网络拓扑连接在内的复杂空间关联
    卷积神经网络+ 时序深度学习方法 同时捕获时间和空间相关性 交通网络的连接关系不是二维网格,卷积神经网络捕获的空间相关性受限
    GNN 与交通数据结构具有高度适配性,能够捕获多种空间相关性 可解释性和训练效率需进一步提升
    下载: 导出CSV

    表  2  图神经网络对比

    Table  2.   Comparison of graph neural networks

    方法 类型 优点 缺点
    卷积图网络 基于空域 灵活性强,可扩展性强 缺少理论支撑
    基于频域 理论基础扎实 结构固定,不适用于有向图
    图注意网络 自适应地关注重要邻居 只关注节点的局部结构
    图自编码器 重塑图保留动态随机特性 为学习到有效数据分布形式,需要对损失函数进行处理
    图时空网络 基于RNN 应用广泛,性能良好 迭代耗时,门控机制复杂
    基于CNN 并行计算,稳定梯度 缺乏对长序列的记忆
    下载: 导出CSV

    表  3  基于图神经网络的交通预测模型总结

    Table  3.   Summary of traffic prediction models based on graph neural networks

    表  4  公开数据集基本信息

    Table  4.   Basic information about public datasets

    类型 数据集 节点数(传感器数) 边数 统计粒度/ min 总时间步
    传感器 METR-LA 207 1 515 5 34 272
    PeMS-BAY 325 2 369 5 52 116
    PeMSD3 358 547 5 26 208
    PeMSD4 307(3 848) 340 5 16 992
    PeMSD7 883 866 5 28 224
    PeMSD7(M) 228 5 12 672
    PeMSD8 170(1 979) 295 5 17 856
    出租车轨迹 SZ-taxi 156 532 15 2 976
    地铁刷卡 SHMETRO 288 958 15 6 072
    HZMETRO 80 248 15 1 650
    BEIJING-subway 276 630 10/15/ 30 2 700/ 1 800/900
    下载: 导出CSV

    表  5  TT、STGNN和Dual Graph模型对比

    Table  5.   Comparison of TT, STGNN and Dual Graph models

    模型 时间模块 空间模块 注意力机制 时间片段
    TT 变压器 DCNN或GCN 时间维度 连续近期、日周期、周周期
    STGNN GRU、变压器 S-GNN 时空维度 连续近期
    Dual Graph 编码器-解码器架构 空域图卷积网络 连续近期
    下载: 导出CSV
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  • 收稿日期:  2023-04-07
  • 网络出版日期:  2023-11-17
  • 刊出日期:  2023-10-25

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