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基于有向图卷积与门控循环单元的短时交通流预测方法

崔文岳 谷远利 赵胜利 芮小平

崔文岳, 谷远利, 赵胜利, 芮小平. 基于有向图卷积与门控循环单元的短时交通流预测方法[J]. 交通信息与安全, 2023, 41(2): 121-128. doi: 10.3963/j.jssn.1674-4861.2023.02.013
引用本文: 崔文岳, 谷远利, 赵胜利, 芮小平. 基于有向图卷积与门控循环单元的短时交通流预测方法[J]. 交通信息与安全, 2023, 41(2): 121-128. doi: 10.3963/j.jssn.1674-4861.2023.02.013
CUI Wenyue, GU Yuanli, ZHAO Shengli, RUI Xiaoping. A Method of Predicting Short-term Traffic Flows Based on a DGC-GRU Model[J]. Journal of Transport Information and Safety, 2023, 41(2): 121-128. doi: 10.3963/j.jssn.1674-4861.2023.02.013
Citation: CUI Wenyue, GU Yuanli, ZHAO Shengli, RUI Xiaoping. A Method of Predicting Short-term Traffic Flows Based on a DGC-GRU Model[J]. Journal of Transport Information and Safety, 2023, 41(2): 121-128. doi: 10.3963/j.jssn.1674-4861.2023.02.013

基于有向图卷积与门控循环单元的短时交通流预测方法

doi: 10.3963/j.jssn.1674-4861.2023.02.013
基金项目: 

国家自然科学基金项目 41771478

北京市科技计划项目 Z121100000312101

详细信息
    作者简介:

    崔文岳(1998—),硕士研究生. 研究方向:交通规划与管理. E-mail: cui_wen_yue@163.com

    通讯作者:

    芮小平(1975—),博士,教授. 研究方向:交通地理信息系统理论与应用研究等. E-mail: ruixpsz@163.com

  • 中图分类号: U491.1+4

A Method of Predicting Short-term Traffic Flows Based on a DGC-GRU Model

  • 摘要: 为了充分挖掘快速路交通流时空特性,解决当前城市快速路交通流预测存在交通流时空特性挖掘不充分等问题,进一步提高城市快速路短时交通流的预测精度与效率,研究了基于有向图卷积神经网络和门控循环单元的组合模型(directed graph convolution network-gate recurrent unit,DGC-GRU)的城市快速路短时交通流预测方法。该方法提出空间相关性矩阵并将其引入图卷积神经网络中,构建有向图卷积神经网络用于表征交通流的有向性和流动性。将交通流参数输入有向图卷积神经网络后得到有向图卷积算子,并将有向图卷积算子引入门控循环单元,通过有向图卷积神经网络捕捉交通流的空间特性,通过门控循环单元捕捉交通流的时间特性,输出快速路交通流预测结果。选取西雅图环形快速路感应器检测数据进行实例分析,对比模型预测效果。结果表明:在数据集与参数设置均相同的情况下,DGC-GRU交通流预测模型的训练收敛速度更快,且平均绝对误差(mean absolute error,MAE)和平均绝对百分比误差(mean absolute percentage error,MAPE)均优于对比模型,与传统的GRU、GCN、DGC-LSTM模型相比,DGC-GRU模型能够将MAE和MAPE指数分别降低33.01%、5.76%、1.32%和27.75%、1.15%、7.76%,表明DGC-GRU交通流预测模型能够有效挖掘城市快速路网中的交通流时空分布特征,具有良好的预测精度与效率。

     

  • 图  1  图卷积神经网络结构图

    Figure  1.  Structure of graph convolutional neural networks

    图  2  LSTM与GRU结构对比图

    Figure  2.  Structure comparison between LSTM and GRU

    图  3  交通路网图

    Figure  3.  Traffic network

    图  4  DGC-GRU模型结构图

    Figure  4.  Structure of DGC-GRU model

    图  5  西雅图环形快速路感应器检测数据集

    Figure  5.  Seattle Inductive Loop Detector Dataset

    图  6  各模型的训练过程loss值迭代图

    Figure  6.  Iterative graph of loss value in training process of each model

    图  7  DGC-GRU交通流预测模型预测效果图

    Figure  7.  Prediction efficiency of DGC-GRU model

    表  1  4种预测模型的预测结果对比

    Table  1.   Comparison of the prediction results of the four prediction models

    指标 模型
    GRU DGC DGC-LSTM DGC-GRU
    RMSE/(km/h) 16.268 8.946 8.125 7.791
    MAE/(km/h) 6.229 4.428 4.229 4.173
    MAPE/% 8.516 6.925 6.671 6.153
    下载: 导出CSV
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  • 收稿日期:  2022-02-22
  • 网络出版日期:  2023-06-19

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