An Informer-based Spatio-Temporal network for traffic flow forecasting

Qi Feng, Kaifang Wan, Xiaoguang Gao, Neretin Evgeny, Jinliang Li

科研成果: 书/报告/会议事项章节会议稿件同行评审

1 引用 (Scopus)

摘要

Spatio-temporal data is one type of multivariate time series(MTS), which has strong time dependence and complex spatial correlation. Spatio-temporal data prediction requires mining the temporal-spatial relations implied in the data to obtain trends in the data as they change over time and space. Traffic flow is a typical class of spatio-temporal data, due to the complexity of the traffic road network, the high spatio-temporal correlation of road conditions, and the strong coupling of traffic information in time and space, which makes it much more difficult to mine the correlation of data from traffic flow. However, existing traffic flow forecasting methods often focus only on its temporal attributes leading to the loss of spatial information or can only effectively handle short-time data, To solve this problem, we propose a spatio-temporal data network, which uses Informer to process the masked spatio-temporal data to enhance its data mining capability, and subsequently using spatio-temporal graph neural network (STGNN)to complete the spatial data processing. In this way, we achieve effective processing of long-time data. Finally, after the experimental validation on public dataset, the forecasting error of the proposed model is reduced and the training speed is improved.

源语言英语
主期刊名ICCSI 2023 - 2023 International Conference on Cyber-Physical Social Intelligence
出版商Institute of Electrical and Electronics Engineers Inc.
532-537
页数6
ISBN(电子版)9798350312492
DOI
出版状态已出版 - 2023
活动2023 International Conference on Cyber-Physical Social Intelligence, ICCSI 2023 - Xi'an, 中国
期限: 20 10月 202323 10月 2023

出版系列

姓名ICCSI 2023 - 2023 International Conference on Cyber-Physical Social Intelligence

会议

会议2023 International Conference on Cyber-Physical Social Intelligence, ICCSI 2023
国家/地区中国
Xi'an
时期20/10/2323/10/23

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