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MS-Net: Multi-Source Spatio-Temporal Network for Traffic Flow Prediction

文献类型：期刊论文

作者Fang, Shen1,3; Prinet, Veronique1; Chang, Jianlong1; Werman, Michael2; Zhang, Chunxia4; Xiang, Shiming1,3; Pan, Chunhong1
刊名IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
出版日期2021-03-24
页码14
关键词Feature extraction Convolution Predictive models Data models Correlation Roads Kernel Graph convolution deep attention mechanism traffic network traffic flow prediction artificial intelligence deep learning
ISSN号1524-9050
DOI10.1109/TITS.2021.3067024
通讯作者Xiang, Shiming(smxiang@nlpr.ia.ac.cn)
英文摘要Predicting urban traffic flow is a challenging task, due to the complicated spatio-temporal dependencies on traffic networks. Urban traffic flow usually has both short-term neighboring and long-term periodic temporal dependencies. It is also noticed that the spatial correlations over different traffic nodes are both local and non-local. What's more, the traffic flow is affected by various external factors. To capture the non-local spatial correlations, we propose a Dilated Attentional Graph Convolution (DAGC). The DAGC utilizes a dilated graph convolution kernel to expand the nodes' receptive field and exploit multi-order neighborhood. Technically, the lower-order neighborhood corresponds to local spatial dependencies, while the higher-order neighborhood corresponds to non-local spatial dependencies between nodes. Based on DAGC, a Multi-Source Spatio-Temporal Network (MS-Net) is designed, which suffices to integrate long-range historical traffic data as well as multi-modal external information. MS-Net consists of four components: a spatial feature extraction module, a temporal feature fusion module, an external factors embedding module, and a multi-source data fusion module. Extensive experiments on three real traffic datasets demonstrates that the proposed model performs well on both the public transportation networks, road networks, and can handle large-scale traffic networks in particular the Beijing bus network which has more than 4,000 traffic nodes.
WOS关键词INTELLIGENT TRANSPORTATION SYSTEMS ; KALMAN FILTER ; MODEL
资助项目Major Project for New Generation of AI[2018AAA0100400] ; National Natural Science Foundation of China[91646207] ; National Natural Science Foundation of China[62072039] ; National Natural Science Foundation of China[62076242] ; National Natural Science Foundation of China[61976208]
WOS研究方向Engineering ; Transportation
语种英语
WOS记录号WOS:000732101100001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构Major Project for New Generation of AI ; National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/46923]  
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
通讯作者Xiang, Shiming
作者单位1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Hebrew Univ Jerusalem, Rachel & Selim Benin Sch Comp Sci & Engn, IL-91904 Jerusalem, Israel
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China
4.Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China
推荐引用方式
GB/T 7714		 Fang, Shen,Prinet, Veronique,Chang, Jianlong,et al. MS-Net: Multi-Source Spatio-Temporal Network for Traffic Flow Prediction[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2021:14.
APA Fang, Shen.,Prinet, Veronique.,Chang, Jianlong.,Werman, Michael.,Zhang, Chunxia.,...&Pan, Chunhong.(2021).MS-Net: Multi-Source Spatio-Temporal Network for Traffic Flow Prediction.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,14.
MLA Fang, Shen,et al."MS-Net: Multi-Source Spatio-Temporal Network for Traffic Flow Prediction".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2021):14.

入库方式： OAI收割

来源：自动化研究所

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