MS-Net: Multi-Source Spatio-Temporal Network for Traffic Flow Prediction
Fang, Shen1,2; Prinet, Veronique1; Chang, Jianlong1; Werman, Michael3; Zhang, Chunxia4; Xiang, Shiming1,2; Pan, Chunhong1
发表期刊IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
ISSN1524-9050
2021-03-24
卷号23期号:7页码:14
通讯作者Xiang, Shiming(smxiang@nlpr.ia.ac.cn)
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.
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
DOI10.1109/TITS.2021.3067024
关键词[WOS]INTELLIGENT TRANSPORTATION SYSTEMS ; KALMAN FILTER ; MODEL
收录类别SCI
语种英语
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]
项目资助者Major Project for New Generation of AI ; National Natural Science Foundation of China
WOS研究方向Engineering ; Transportation
WOS类目Engineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS记录号WOS:000732101100001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类机器学习
引用统计
被引频次:14[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/46924
专题模式识别国家重点实验室_先进时空数据分析与学习
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.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China
3.Hebrew Univ Jerusalem, Rachel & Selim Benin Sch Comp Sci & Engn, IL-91904 Jerusalem, Israel
4.Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China
第一作者单位模式识别国家重点实验室
通讯作者单位模式识别国家重点实验室
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,2021,23(7):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,23(7),14.
MLA Fang, Shen,et al."MS-Net: Multi-Source Spatio-Temporal Network for Traffic Flow Prediction".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 23.7(2021):14.
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