全局和局部感知的交通速度预测模型

申岩松; 李琳; 黄传明

doi:10.12263/DZXB.20221274

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全局和局部感知的交通速度预测模型

学术论文 | 更新时间：2025-12-08

- 全局和局部感知的交通速度预测模型
- Global and Local Information Aware Traffic Speed Prediction
- 电子学报 2024年52卷第9期页码：3195-3205
- 作者机构：
  
  1.武汉理工大学计算机与人工智能学院，湖北武汉 430070
  2.武汉市交管局，湖北武汉 430030
- 作者简介：
  
  [ "申岩松男，1998年11月出生于山西省运城市.2023年获得武汉理工大学硕士学位，研究领域为机器学习、数据挖掘.E-mail: yaso9527@whut.edu.cn" ]
  [ "李琳女，1977年10月出生于湖南省衡阳市.2009年于东京大学获得博士学位，现为武汉理工大学教授，主要研究领域为信息检索与推荐系统、数据挖掘与机器学习.中国电子学会会员编号：E190008559M.E-mail: cathylilin@whut.edu.cn" ]
  [ "黄传明男，1976年10月出生于黑龙江牡丹江市，武汉理工大学交通运输工程在读博士生.主要研究方向为交通信号控制以及智能交通.E-mail: 10083823@qq.com" ]
- 基金信息：
  
  国家自然科学基金(62276196);湖北省重点研发计划项目(2021BAA030)
- DOI：10.12263/DZXB.20221274
  中图分类号： TP391.1
- 收稿：2022-11-08，
  
  修回：2023-03-03，
  
  纸质出版：2024-09-25
- 稿件说明：
移动端阅览
申岩松, 李琳, 黄传明. 全局和局部感知的交通速度预测模型[J]. 电子学报, 2024, 52(09): 3195-3205.

SHEN Yan-song, LI Lin, HUANG Chuan-ming. Global and Local Information Aware Traffic Speed Prediction[J]. Acta Electronica Sinica, 2024, 52(09): 3195-3205.
申岩松, 李琳, 黄传明. 全局和局部感知的交通速度预测模型[J]. 电子学报, 2024, 52(09): 3195-3205. DOI：10.12263/DZXB.20221274

SHEN Yan-song, LI Lin, HUANG Chuan-ming. Global and Local Information Aware Traffic Speed Prediction[J]. Acta Electronica Sinica, 2024, 52(09): 3195-3205. DOI：10.12263/DZXB.20221274

摘要

面对日益严峻的交通堵塞问题，智能交通系统获得飞速发展和广泛应用，作为基石工作的交通速度预测因此备受关注. 近些年来，深度学习被广泛用于交通速度预测的研究工作，并且研究方向也从单一的建模时间相关性迁移到复杂的时空相关性，图神经网络由于契合交通路网的图结构数据这一本质属性，成为建模空间相关性的主流方法. 目前，大多数的研究工作已经注意到动态的空间相关性对交通速度预测任务的重要性. 然而，基于这一发现所提出的建模思路主要预定义矩阵或自适应矩阵，属于静态矩阵，并不足以应对空间相关性的复杂和动态的特性. 同时通过对真实交通速度数据集的分析，本文发现交通节点间依赖的局部波动相比交通路网的全局影响具有更强的动态性，这表明空间相关性可以从全局和局部的角度分开建模，因此本文提出了一个端到端全局和局部融合的动态图神经网络模型来进行交通速度预测. 首先，交通速度流被自分解层分解为静态分量和动态分量，随后动态图生成模块为动态分量构造实时的动态图以匹配其动态性. 基于构造的动态图和输入的预定义图，本文借助图卷积操作来学习这两类空间相关性的高阶表达. 除此之外，本文在时间模块使用空洞因果卷积捕获交通数据中时间相关性. 最后，残差连接被用来聚合时空相关性并输送给输出层完成最终的速度预测. 在两个高速公路数据集和一个城市路数据集上的实验结果表明本文提出的模型相比主流模型在平均绝对误差和均方根误差两个预测指标上均优于主流模型.

Abstract

Facing the increasingly severe traffic congestion problem

the intelligent transportation system has been rapidly developed and widely used

and the traffic speed prediction

a cornerstone task

has attracted much attention. In recent years

deep learning has been widely used in the research of traffic speed prediction

and the research direction has also shifted from modeling time correlation to considering complex spatiotemporal correlation. The graph neural network fits the graph structure data of the traffic network and has become the mainstream method for modeling spatial correlation. To date

most research works have noted the importance of modeling dynamic spatial correlations in the task of traffic speed prediction. However

predefined or adaptive matrices for spatial feature learning are essentially static

and are not sufficient to match the complex and dynamic characteristics of spatial correlations. Moreover

through the analysis of multiple real traffic speed datasets

we find that the local fluctuations of inter-node dependencies are more dynamic than the global influence of the traffic network

which indicates that the spatial correlation can be derived from the global and local angles. Therefore

we propose an end-to-end global and local aware dynamic graph neural network model for traffic speed prediction. The traffic speed flow is first decomposed into static components and dynamic components by the self-decomposition layer

and then the dynamic graph generation module constructs a real-time dynamic graph for the dynamic components to match their dynamics. With the constructed dynamic graph and the input predefined graph

we model higher-order representations of these two classes of spatial correlations through graph convolution operations. Besides

we use causal convolution in the temporal module to capture temporal correlations in traffic data. Finally

residual connections are used to aggregate spatiotemporal correlations and feed to the output layer for final speed prediction. Experimental results on two highway datasets and one urban road dataset show that our proposed model outperforms state-of-the art models in terms of MAE and RMSE.

关键词

Keywords

references

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