IF 7.9 1区 工程技术 Q1 ENGINEERING, CIVIL IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-05 DOI:10.1109/TITS.2025.3531665
Qing Yuan;Junbo Wang;Yu Han;Zhi Liu;Wanquan Liu
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引用次数: 0

摘要

有必要在交通数据中建立一个时空关联模型,以预测交通系统的状态。现有的研究主要集中在传统的图神经网络上,这种网络使用预定义的图并具有共享参数。但直观的预定义图形会给预测任务带来偏差,而且参数共享模型无法获得精细的时空信息。在本文中,我们认为学习节点特定参数和具有完整边缘信息的自适应图至关重要。为了证明这一点,我们设计了一个基于图结构的模型,将节点和边解耦为两个模块。每个模块同时提取时间和空间特征。自适应节点优化模块用于学习所有节点的特定参数模式,而自适应边缘优化模块旨在挖掘不同节点之间的相互依赖关系。然后,我们提出了用于交通预测的解耦自适应图卷积注意力网络(DAGCAN),该网络依靠上述两个模块动态捕捉交通数据中的细粒度时空关系。在四个公共交通数据集上的实验结果表明,我们的模型可以进一步提高交通预测的准确性。
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DAGCAN: Decoupled Adaptive Graph Convolution Attention Network for Traffic Forecasting
It is necessary to establish a spatio-temporal correlation model in the traffic data to predict the state of the transportation system. Existing research has focused on traditional graph neural networks, which use predefined graphs and have shared parameters. But intuitive predefined graphs introduce biases into prediction tasks and the fine-grained spatio-temporal information can not be obtained by the parameter sharing model. In this paper, we consider it is crucial to learn node-specific parameters and adaptive graphs with complete edge information. To show this, we design a model based on graph structure that decouples nodes and edges into two modules. Each module extracts temporal and spatial features simultaneously. The adaptive node optimization module is used to learn the specific parameter patterns of all nodes, and the adaptive edge optimization module aims to mine the interdependencies among different nodes. Then we propose a Decoupled Adaptive Graph Convolution Attention Network for Traffic Forecasting (DAGCAN), which relies on the above two modules to dynamically capture the fine-grained spatio-temporal relationships in traffic data. Experimental results on four public transportation datasets, demonstrate that our model can further improve the accuracy of traffic prediction.
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来源期刊
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Intelligent Transportation Systems 工程技术-工程:电子与电气
CiteScore
14.80
自引率
12.90%
发文量
1872
审稿时长
7.5 months
期刊介绍: The theoretical, experimental and operational aspects of electrical and electronics engineering and information technologies as applied to Intelligent Transportation Systems (ITS). Intelligent Transportation Systems are defined as those systems utilizing synergistic technologies and systems engineering concepts to develop and improve transportation systems of all kinds. The scope of this interdisciplinary activity includes the promotion, consolidation and coordination of ITS technical activities among IEEE entities, and providing a focus for cooperative activities, both internally and externally.
期刊最新文献
Table of Contents IEEE Intelligent Transportation Systems Society Information Scanning the Issue IEEE INTELLIGENT TRANSPORTATION SYSTEMS SOCIETY Time-Aware and Direction-Constrained Collective Spatial Keyword Query
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