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Meta-MSNet: Meta-Learning Based Multi-Source Data Fusion for Traffic Flow Prediction

文献类型：期刊论文

作者Fang, Shen1,2; Pan, Xianbing3; Xiang, Shiming1,2; Pan, Chunhong2
刊名IEEE SIGNAL PROCESSING LETTERS
出版日期2021
卷号28页码:6-10
关键词Data fusion deep learning graph convolution meta-learning traffic flow prediction traffic network
ISSN号1070-9908
DOI10.1109/LSP.2020.3037527
通讯作者Xiang, Shiming(smxiang@nlpr.ia.ac.cn)
英文摘要Traffic flow prediction is a challenging task while most existing works are faced with two main problems in extracting complicated intrinsic and extrinsic features. In terms of intrinsic features, current methods don't fully exploit different functions of short-term neighboring and long-term periodic temporal patterns. As for extrinsic features, recent works mainly employ hand-crafted fusion strategies to integrate external factors but remain generalization issues. To solve these problems, we propose a meta-learning based multi-source spatio-temporal network (Meta-MSNet). The Meta-MSNet is designed with an encoder-decoder structure. The encoder captures neighboring temporal dependencies while the decoder extracts periodic features. Furthermore, two meta-learning based fusion modules are designed to integrate multi-source external data both on temporal and spatial dimensions. Experiments on three real-world traffic datasets have verified the superiority of the proposed model.
资助项目Major Project for New Generation of AI[2018AAA0100400] ; NationalNatural Science Foundation ofChina[91646207] ; NationalNatural Science Foundation ofChina[62076242] ; NationalNatural Science Foundation ofChina[61773377] ; NationalNatural Science Foundation ofChina[61976208]
WOS研究方向Engineering
语种英语
WOS记录号WOS:000608679700002
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构Major Project for New Generation of AI ; NationalNatural Science Foundation ofChina
源URL[http://ir.ia.ac.cn/handle/173211/42590]  
专题自动化研究所_模式识别国家重点实验室_遥感图像处理团队
中国科学院自动化研究所
通讯作者Xiang, Shiming
作者单位1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
3.Chongqing Univ Posts & Telecommun, Coll Mobile Telecommun, Chongqing 400065, Peoples R China
推荐引用方式
GB/T 7714		 Fang, Shen,Pan, Xianbing,Xiang, Shiming,et al. Meta-MSNet: Meta-Learning Based Multi-Source Data Fusion for Traffic Flow Prediction[J]. IEEE SIGNAL PROCESSING LETTERS,2021,28:6-10.
APA Fang, Shen,Pan, Xianbing,Xiang, Shiming,&Pan, Chunhong.(2021).Meta-MSNet: Meta-Learning Based Multi-Source Data Fusion for Traffic Flow Prediction.IEEE SIGNAL PROCESSING LETTERS,28,6-10.
MLA Fang, Shen,et al."Meta-MSNet: Meta-Learning Based Multi-Source Data Fusion for Traffic Flow Prediction".IEEE SIGNAL PROCESSING LETTERS 28(2021):6-10.

入库方式： OAI收割

来源：自动化研究所

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