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基于深度学习的短期交通流预测方法综述

崔建勋 要甲 赵泊媛

崔建勋, 要甲, 赵泊媛. 基于深度学习的短期交通流预测方法综述[J]. 交通运输工程学报, 2024, 24(2): 50-64. doi: 10.19818/j.cnki.1671-1637.2024.02.003
引用本文: 崔建勋, 要甲, 赵泊媛. 基于深度学习的短期交通流预测方法综述[J]. 交通运输工程学报, 2024, 24(2): 50-64. doi: 10.19818/j.cnki.1671-1637.2024.02.003
CUI Jian-xun, YAO Jia, ZHAO Bo-yuan. Review on short-term traffic flow prediction methods based on deep learning[J]. Journal of Traffic and Transportation Engineering, 2024, 24(2): 50-64. doi: 10.19818/j.cnki.1671-1637.2024.02.003
Citation: CUI Jian-xun, YAO Jia, ZHAO Bo-yuan. Review on short-term traffic flow prediction methods based on deep learning[J]. Journal of Traffic and Transportation Engineering, 2024, 24(2): 50-64. doi: 10.19818/j.cnki.1671-1637.2024.02.003

基于深度学习的短期交通流预测方法综述

doi: 10.19818/j.cnki.1671-1637.2024.02.003
基金项目: 

国家自然科学基金项目 72371048

黑龙江省自然科学基金项目 LH2021E074

详细信息
    作者简介:

    崔建勋(1982-),男,黑龙江绥化人,哈尔滨工业大学副教授,从事智能交通与自动驾驶研究

  • 中图分类号: U491.1

Review on short-term traffic flow prediction methods based on deep learning

Funds: 

National Natural Science Foundation of China 72371048

Natural Science Foundation of Heilongjiang Province LH2021E074

More Information
  • 摘要: 针对基于深度学习的短期交通流预测问题,揭示了时空相关性建模本质,分析了建模过程中涉及的多尺度时空特性、异质性、动态性、非线性等特点,明确了基于深度学习进行短期交通流预测的核心挑战,阐述了短期交通流预测涉及的外部信息整合、多步预测与单步预测以及单体预测与集成预测等相关问题;按照网格化和拓扑化2种交通流数据组织方式,分别综述了当前最新的基于深度学习的短期交通流预测研究方向。研究结果表明:针对网格化交通流数据,当前研究主要包含了基于2D图像卷积神经网络、基于2D图像卷积神经网络与循环神经网络相结合、基于3D图像卷积神经网络3种预测建模方法;针对拓扑化交通流数据,当前研究主要包含了基于1D因果图像卷积与卷积图神经网络相结合、基于循环神经网络与卷积图神经网络相结合、基于自注意力与卷积图神经网络相结合、基于卷积图神经网络的时空同步学习4种预测建模方法;总体上,基于深度学习方法进行短期交通流预测相较于采用时间序列和经典机器学习方法获得了预测准确性上的极大提升;未来,针对物理理论、知识图谱与深度学习相结合,构建多时空数据挖掘大模型以及轻量化、可解释性、模型结构自动化搜索等维度的相关探索将成为重要研究方向。

     

  • 图  1  基于深度学习的短期交通流预测一般框架

    Figure  1.  General framework of short-term traffic flow prediction based on deep learning

    图  2  多个历史时段交通流图像拼接集成

    Figure  2.  Concatenation aggregation of traffic flow images in multiple historical periods

    图  3  基于2D图像卷积与循环神经网络的时空相关性建模

    Figure  3.  Spatiotemporal correlation modeling based on 2D image convolution and recurrent neural network

    图  4  交通观测拓扑化网络示例

    Figure  4.  Example of traffic observation topological networks

    图  5  拓扑化道路交通时空观测数据示例

    Figure  5.  Example of topological road traffic spatiotemporal observation data

    图  6  基于1D因果图像卷积与卷积图神经网络的时空相关性建模

    Figure  6.  Spatiotemporal correlation modeling based on 1D causal image convolution and convolution graph neural network

    图  7  基于循环神经网络与卷积图神经网络的时空相关性建模

    Figure  7.  Spatiotemporal correlation modeling based on recurrent neural network and convolutional graph neural network

    图  8  交通流序列中的各种相关性

    Figure  8.  Various correlations in traffic flow sequence

    表  1  基于深度学习的短期交通流预测文献概况

    Table  1.   Overview of literatures on short-term traffic flow prediction based on deep learning

    数据组织方式 时空相关性捕获方式 代表性文献 核心理念 优势 劣势
    网格化数据 2D图像卷积神经网络 [46]~[48] 将时空网格化的交通流数据转化为多通道图像数据,从而单纯采用卷积神经网络进行时空相关性特征学习 将时空相关性特征学习转化为单纯的空间特征学习,方法相对简单、高效 由于将时间维度的信息转化为图像通道的信息,导致对时间相关性捕获欠佳
    2D图像卷积与循环神经网络相结合 [21]、[25]、[49]~[54] 采用图像卷积网络捕获空间相关性,采用循环神经网络捕获时间相关性 将复杂的时空相关性捕获分解为时间相关性和空间相关性的单独捕获,降低了问题复杂度,模型思路简洁、清晰 时空相关性的分离式建模导致无法捕获时空同步相关性,此外,循环神经网络的训练和推理效率较低
    3D图像卷积神经网络 [18]、[55]~[60] 将历史网格化交通流数据视为“视频流”型数据,采用可以在时空维度上进行滑动的3D卷积核进行时空相关性特征学习 能够一定程度上捕获到时空同步的相关性,且模型仅采用卷积计算,具有并行计算的优势 3D卷积相对于2D卷积的计算量明显上升,模型的训练和推理效率有所下降
    拓扑化数据 1D因果图像卷积与卷积图神经网络相结合 [61]~[65] 采用1D因果卷积捕获时间相关性,采用拓扑图卷积捕获空间相关性 采用1D因果卷积捕获时间相关性,相较于循环神经网络在训练和推理效率上明显提升 时空相关性的捕获采用了时空分离的方式,无法捕获到同步的时空相关性
    循环神经网络与卷积图神经网络相结合 [66]~[68] 采用循环神经网络捕获时间相关性,采用卷积图神经网络捕获空间相关性 采用循环神经网络和卷积图神经网络分别进行时间和空间相关性捕获,理论较为成熟 时空相关性的捕获采用了时空分离的方式,无法捕获到同步的时空相关性,此外,循环神经网络训练和推理效率较低
    自注意力与卷积图神经网络 [69] 自注意力机制负责从时间的维度捕获相关性,卷积图神经网络负责从空间维度捕获相关性 注意力机制在时间维度相关性捕获上,由于并行本质,计算高效 无法捕获时空同步相关性,容易忽略时间顺序对未来预测的影响,需要时间位置编码补充
    基于卷积图神经网络的时空同步学习 [70]、[71] 将空间拓扑图拓展到时空拓扑图,从而统一采用拓扑图卷积捕获时空相关性 空间拓扑到时空拓扑变换,导致网络规模急剧增加,模型运算复杂度明显提升 能够捕获到时空同步的相关性
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  • 收稿日期:  2023-10-13
  • 网络出版日期:  2024-05-16
  • 刊出日期:  2024-04-30

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