CLC number: TP391
On-line Access: 2021-09-10
Received: 2020-05-21
Revision Accepted: 2021-02-17
Crosschecked: 2021-04-01
Cited: 0
Clicked: 5016
Citations: Bibtex RefMan EndNote GB/T7714
Dewen Seng, Fanshun Lv, Ziyi Liang, Xiaoying Shi, Qiming Fang. Forecasting traffic flows in irregular regions with multi-graph convolutional network and gated recurrent unit[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2000243 @article{title="Forecasting traffic flows in irregular regions with multi-graph convolutional network and gated recurrent unit", %0 Journal Article TY - JOUR
基于多图卷积网络和门控循环单元的不规则区域交通流量预测杭州电子科技大学计算机学院,中国杭州市,310018 摘要:区域交通流量预测对智能交通系统的交通控制和管理十分重要。借助深度神经网络,采用仅适用于规则网格的循环神经网络或残差神经网络捕获流量预测的空间依赖性。但是,考虑到路网和行政边界得到的区域通常是不规则的。因此将城市划分成网格进行预测是不准确的。提出一种基于多图卷积网络和门控循环单元(MGCN-GRU)的不规则区域交通流量预测模型。首先,构建一个城市异质区域间关联图反映各区域间的关联。在每个图中,节点表示不规则区域,边代表区域间的关联类型。然后,提出一个多图卷积网络融合不同区域间关联图和附加属性。进一步采用门控循环单元捕获时序依赖并预测未来交通流量。实验结果表明,基于3个真实大数据集(公共自行车系统数据集、出租车数据集和无桩共享自行车数据集),所提MGCN-GRU模型性能优于多个现有方法。 关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
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