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考虑动态空间关系的短时交通流预测方法

赵振兴 曾伟 唐晨嘉

赵振兴, 曾伟, 唐晨嘉. 考虑动态空间关系的短时交通流预测方法[J]. 交通信息与安全, 2023, 41(4): 143-153. doi: 10.3963/j.jssn.1674-4861.2023.04.015
引用本文: 赵振兴, 曾伟, 唐晨嘉. 考虑动态空间关系的短时交通流预测方法[J]. 交通信息与安全, 2023, 41(4): 143-153. doi: 10.3963/j.jssn.1674-4861.2023.04.015
ZHAO Zhenxing, ZENG Wei, TANG Chenjia. A Short-term Traffic Flow Forecasting Model Considering Dynamic Spatio-temporal Relationship[J]. Journal of Transport Information and Safety, 2023, 41(4): 143-153. doi: 10.3963/j.jssn.1674-4861.2023.04.015
Citation: ZHAO Zhenxing, ZENG Wei, TANG Chenjia. A Short-term Traffic Flow Forecasting Model Considering Dynamic Spatio-temporal Relationship[J]. Journal of Transport Information and Safety, 2023, 41(4): 143-153. doi: 10.3963/j.jssn.1674-4861.2023.04.015

考虑动态空间关系的短时交通流预测方法

doi: 10.3963/j.jssn.1674-4861.2023.04.015
基金项目: 

国家自然科学基金项目 71871100

详细信息
    作者简介:

    赵振兴(1999—),硕士研究生. 研究方向:智能交通. E-mail:zx_zhao@hust.edu.cn

    通讯作者:

    曾伟(1968—),博士,副教授. 研究方向:系统工程. E-mail:zengwei@mail.hust.edu.cn

  • 中图分类号: U491.14

A Short-term Traffic Flow Forecasting Model Considering Dynamic Spatio-temporal Relationship

  • 摘要: 为有效提取交通流的时空特征,提升交通流的预测精度,研究了基于动态时空图卷积网络的短时交通流预测模型(DySTGCN)。DySTGCN不仅实现了对交通流时空维度的信息建模,而且考虑了时间维度信息对空间维度信息的影响,创新性提出了基于时间信息的空间拓扑结构——时变空间图(spatial topology graph,TSG),并设计出了1种能够高效、简便地计算时变空间图的深层网络结构。该结构通过编码、解码方式提取不同节点的交通流数据的相关性特征并实现降噪处理。时变空间图反映了交通网络的实时空间特征,基于交通网络中节点空间位置的稳定空间图(stable spatial graph,SG)反映了交通网络的稳定空间特征。TSG与SG在图卷积过程中共同指导交通流预测,更加准确地刻画了交通流的时空特性,以提高预测精度。为测试模型的预测效果,在2个权威公开数据集上进行实验,结果表明:DySTGCN学习到的时变空间图可以较为准确地反映出不同节点的交通流之间的相关性,在平均绝对误差、均方根误差,以及加权平均绝对百分比误差指标上,比其他时空图卷积网络模型如STGCN、ASTGCN等降低了近13.40%、10.98%、16.72%,充分验证了动态空间关系在短时交通流预测中的重要作用。此外,DySTGCN能够提取交通流的周期性特征,实现了对交通流的连续不间断预测。

     

  • 图  1  DySTGCN框架

    Figure  1.  Framework of DySTGCN

    图  2  TSG估计器框架

    Figure  2.  Framework of TG estimator

    图  3  参数选择测试结果

    Figure  3.  Test results of parameter selection

    图  4  不同模型对于预测时长对预测时长敏感程度

    Figure  4.  Sensitivity of prediction length for models

    图  5  在2个连续时段上生成的部分节点的时变空间图

    Figure  5.  TSG of partial nodes generated over two consecutive period

    图  6  不同位置色块对应的2个节点流量变化趋势

    Figure  6.  Traffic flow trends of two nodes at different location

    图  7  消融实验结果

    Figure  7.  The results of ablation study

    图  8  模型短期预测结果

    Figure  8.  Short-term prediction of model

    图  9  7 d的交通流量预测结果

    Figure  9.  Traffic flow prediction of 7 days

    表  1  数据集格式

    Table  1.   Format of dataset

    数据集 网络节点总数/ 个 连边总数/条 可用总步长/ 个 数据单位
    PeMSD4 307 340 16992 辆/5min
    PeMSD8 170 277 17856 辆/5min
    下载: 导出CSV

    表  2  PeMSD4数据集实验结果

    Table  2.   Result of PeMSD4 dataset

    模型 MAE RMSE WMAPE/%
    HA 30.96±0.00 46.57±0.00 14.54±0.00
    SVR 24.91±0.00 39.15±0.00 11.70±0.00
    STGCN 22.43±0.61 34.38±0.73 11.06±0.21
    ASTGCN 22.28±1.22 34.86±1.87 11.44±1.24
    STSGCN 21.27±0.15 33.58±0.24 10.79±0.07
    STFGNN 19.20±0.09 31.36±0.20 9.75±0.09
    DySTGCN 18.43±0.11 29.86±0.21 8.94±0.09
    下载: 导出CSV

    表  3  PeMSD8数据集实验结果

    Table  3.   Result of PeMSD8 dataset

    模型 MAE RMSE WMAPE/%
    HA 24.51±0.00 37.13±0.00 10.42%±0.00
    SVR 19.98±0.00 32.70±0.00 8.49%±0.00
    STGCN 18.23±0.14 27.69±0.21 8.29%±0.11
    ASTGCN 17.86±0.42 27.58±0.48 8.48%±0.92
    STSGCN 17.65±0.09 27.08±0.20 8.16%±0.07
    STFGNN 16.03±0.09 25.82±0.18 7.89%±0.06
    DySTGCN 15.84±0.10 24.46±0.18 7.18%±0.07
    下载: 导出CSV
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  • 收稿日期:  2023-02-10
  • 网络出版日期:  2023-11-23

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