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Augmented Multi-Component Recurrent Graph Convolutional Network for Traffic Flow Forecasting

文献类型：期刊论文

作者Zhang, Chi7; Zhou, Hong-Yu1; Qiu, Qiang2; Jian, Zhichun3; Zhu, Daoye7; Cheng, Chengqi4; He, Liesong6; Liu, Guoping5; Wen, Xiang5; Hu, Runbo5
刊名ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
出版日期2022-02-01
卷号11期号:2页码:17
关键词traffic flow forecasting spatial-temporal prediction graph convolutional networks augmented multi-component
DOI10.3390/ijgi11020088
英文摘要Due to the periodic and dynamic changes of traffic flow and the spatial-temporal coupling interaction of complex road networks, traffic flow forecasting is highly challenging and rarely yields satisfactory prediction results. In this paper, we propose a novel methodology named the Augmented Multi-component Recurrent Graph Convolutional Network (AM-RGCN) for traffic flow forecasting by addressing the problems above. We first introduce the augmented multi-component module to the traffic forecasting model to tackle the problem of periodic temporal shift emerging in traffic series. Then, we propose an encoder-decoder architecture for spatial-temporal prediction. Specifically, we propose the Temporal Correlation Learner (TCL) which incorporates one-dimensional convolution into LSTM to utilize the intrinsic temporal characteristics of traffic flow. Moreover, we combine TCL with the graph convolutional network to handle the spatial-temporal coupling interaction of the road network. Similarly, the decoder consists of TCL and convolutional neural networks to obtain high-dimensional representations from multi-step predictions based on spatial-temporal sequences. Extensive experiments on two real-world road traffic datasets, PEMSD4 and PEMSD8, demonstrate that our AM-RGCN achieves the best results.
资助项目Science and Technology Major Special Project of Guangxi, China[GUIKEAA18118025]
WOS研究方向Computer Science ; Physical Geography ; Remote Sensing
语种英语
WOS记录号WOS:000762474900001
出版者MDPI
源URL[http://119.78.100.204/handle/2XEOYT63/18914]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Jian, Zhichun
作者单位1.Univ Hong Kong, Dept Comp Sci, Hong Kong 999077, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Beijing 101408, Peoples R China
3.Shopee Informat Technol Co Ltd, Shenzhen 518063, Peoples R China
4.Peking Univ, Coll Engn, Beijing 100871, Peoples R China
5.Didi Chuxing, Beijing 100085, Peoples R China
6.Xian Res Inst Surveying & Mapping, Xian 710000, Peoples R China
7.Peking Univ, Acad Adv Interdisciplinary Studies, Beijing 100871, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Chi,Zhou, Hong-Yu,Qiu, Qiang,et al. Augmented Multi-Component Recurrent Graph Convolutional Network for Traffic Flow Forecasting[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2022,11(2):17.
APA Zhang, Chi.,Zhou, Hong-Yu.,Qiu, Qiang.,Jian, Zhichun.,Zhu, Daoye.,...&Hu, Runbo.(2022).Augmented Multi-Component Recurrent Graph Convolutional Network for Traffic Flow Forecasting.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,11(2),17.
MLA Zhang, Chi,et al."Augmented Multi-Component Recurrent Graph Convolutional Network for Traffic Flow Forecasting".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 11.2(2022):17.

入库方式： OAI收割

来源：计算技术研究所

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