Multi-channel spatial-temporal traffic flow prediction based on hybrid static-dynamic graph convolution

Xiongtao ZHANG; Jingyu ZHENG; Qing SHEN; Danfeng SUN; Yunliang JIANG

doi:10.11959/j.issn.1000-0801.2023173

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Multi-channel spatial-temporal traffic flow prediction based on hybrid static-dynamic graph convolution

Research and Development | 更新时间：2024-06-05

- Multi-channel spatial-temporal traffic flow prediction based on hybrid static-dynamic graph convolution
- Telecommunications Science Vol. 39, Issue 9, Pages: 97-110(2023)
- 作者机构：
  
  1. 湖州师范学院信息工程学院，浙江湖州 313000
  2. 浙江省现代农业资源智慧管理与应用研究重点实验室，浙江湖州 313000
  3. 杭州电子科技大学计算机学院，浙江杭州 310018
  4. 浙江师范大学计算机科学与技术学院，浙江金华 321004
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2023173
  CLC： TP393
- Published Online：2023-08，
  
  Published：25 August 2023
- 稿件说明：
移动端阅览
Xiongtao ZHANG, Jingyu ZHENG, Qing SHEN, et al. Multi-channel spatial-temporal traffic flow prediction based on hybrid static-dynamic graph convolution[J]. Telecommunications science, 2023, 39(9): 97-110.
DOI：

Xiongtao ZHANG, Jingyu ZHENG, Qing SHEN, et al. Multi-channel spatial-temporal traffic flow prediction based on hybrid static-dynamic graph convolution[J]. Telecommunications science, 2023, 39(9): 97-110. DOI： 10.11959/j.issn.1000-0801.2023173.

摘要

针对交通流预测模型没有考虑道路上下文相关性和空间依赖关系动态性的问题，提出一种基于混合图卷积的多通道时空交通流预测模型（MHGCN）。该模型采用三明治结构（即中间多通道空间模块，两边时间模块）提取时空特征，多通道空间模块又分为静态图卷积模块和动态图卷积模块。静态图卷积模块同时从拓扑空间结构、语义空间结构及其组合中提取特定和公共的特征；动态图卷积模块对不同的特征分配不同的权重，从未知的图结构中提取动态的空间特征。时间模块中采用多头注意力机制提取全局时间特征，采用时间门控机制提取局部时间特征。该模型从不同的空间结构中提取空间信息，从不同时间间隔提取时间信息，建立全局、全面的时空关系。实验结果表明，MHGCN 模型在 4 个公开数据集上的性能优于现有的交通流预测模型。

Abstract

Aiming at the problem that the traffic flow prediction model did not consider the correlation of road context and the dynamics of spatial dependency

a multi-channel spatial-temporal traffic flow prediction based on hybrid static-dynamic graph convolution (MHGCN) was proposed.A sandwich structure (i.e.multi-channel spatial module in the middle and temporal module on both sides) was used in the model to extract spatial-temporal features

and the multi-channel spatial module was divided into static graph convolution module and dynamic graph convolution module.The static graph convolution module simultaneously extracted specific and common features from topological spatial structures

semantic spatial structures

and their combinations.The dynamic graph convolution module assigned different weights to different features and extracts dynamic spatial features from unknown graph structures.In the temporal module

the multi-head attention mechanism was used to extract the global temporal features

and the temporal gating mechanism extracted the local temporal features.The model extracted spatial information from different spatial structures and temporal information from different time intervals to establish a global and comprehensive spatial-temporal relationship.The experimental results show that the MHGCN performs better than the existing traffic flow prediction models on four real world traffic flow datasets.

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references

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