基于时空联合学习的城市交通流短时预测模型

doi:10.19678/j.issn.1000-3428.0063680

摘要/Abstract

摘要： 时空联合分析可反映研究对象在时空维的变化规律，对揭示区域过程的时空交互关系和机制具有重要意义。聚焦时空联合特征的学习与交通流物理特性的建模问题，提出一种层次化的动态网络模型JST-DHNet，以融合不同尺度下的时空联合学习与内嵌领域知识学习。利用基于图乘积运算替代以往矩阵拼接方式构建多种时空图结构。结合时空小波变换与时空傅里叶变换，设计2种不同层次的时空同步学习模块，分别学习交通流的全域与局域时空特征。针对交通流的宏观流体动力学性质，通过基于图的广义偏微分方程设计一种新的时空扩散卷积，以学习真实场景下的交通波传播机制。在此基础上，采用注意力机制将不同尺度的时空联合特征进行融合。在4种不同路网规模的真实交通流数据集上进行测试，结果表明，JST-DHNet的预测性能优于采用时空分离式学习模块的预测模型，相比STSGCN时空联合学习模型，JST-DHNet预测精度的平均绝对百分比误差、平均绝对误差和均方根误差分别降低4.46%、6.65%、10.11%，且训练时间缩短近80%。

关键词: 智能交通系统, 时空域联合, 交通流预测, 图信号处理, 交通流理论

Abstract: A joint spatio-temporal analysis can reflect the changing pattern of a studied object in the spatio-temporal dimension, which is significant for revealing the spatio-temporal interactions and mechanisms of regional processes.With a focus on joint spatio-temporal feature learning and modeling of traffic flow physical characteristics, this paper proposes a dynamic hierarchical network model called the JST-DHNet, which involves multi-scale joint spatio-temporal learning and physics-informed learning.First, instead of simply connecting adjacent graph snapshots like in previous studies, we developed multiple spatio-temporal graph structures using the graph product.Next, based on the joint time-vertex wavelet transform and Fourier transform, two spatial-temporal synchronous learning modules with different scales are designed to learn the global and local spatio-temporal characteristics of traffic flow, respectively.Based on the macroscopic fluid-dynamical properties of the traffic flow, we developed a novel spatio-temporal diffusion convolution with a graph-based partial differential equation, which enables the learning of the propagation mechanism of traffic waves in actual physical scenarios.Finally, the fusion of joint spatio-temporal features at different scales is performed by adopting an attention mechanism.After testing on four datasets of actual road network traffic flow with different sizes, the experimental results show that JST-DHNet outperforms other separated learning models.Compared with the existing joint spatio-temporal learning model called Spatial-Temporal Synchronous Graph Convolutional Network(STSGCN), JST-DHNet not only improves the prediction accuracy of the Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root-Mean-Squared Error (RMSE) by 4.46%, 6.65%, and 10.11%, respectively, but also shortens the training time by nearly 80%.

Key words: Intelligent Traffic System(ITS), spatio-temporal union, traffic flow prediction, Graph Signal Processing (GSP), traffic flow theory

中图分类号:

TP391

葛宇然, 付强. 基于时空联合学习的城市交通流短时预测模型[J]. 计算机工程, 2023, 49(1): 270-278.

GE Yuran, FU Qiang. Short-Time Prediction Model for Urban Traffic Flow Based on Joint Spatio-Temporal Learning[J]. Computer Engineering, 2023, 49(1): 270-278.

https://www.ecice06.com/CN/Y2023/V49/I1/270

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