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A Spatial-Temporal Approach for Multi-Airport Traffic Flow Prediction Through Causality Graphs

文献类型：期刊论文

作者Du, Wenbo1,6; Chen, Shenwen1,6; Li, Zhishuai5; Cao, Xianbin1,6; Lv, Yisheng2,3,4
刊名IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
出版日期2023-09-04
页码13
关键词Airport traffic flow predictive models deep learning causality graph spatiotemporal analysis
ISSN号1524-9050
DOI10.1109/TITS.2023.3308903
通讯作者Cao, Xianbin(xbcao@buaa.edu.cn)
英文摘要Accurate airport traffic flow estimation is crucial for the secure and orderly operation of the aviation system. Recent advances in machine learning have achieved promising prediction results in the single-airport scenario. However, these works overlook the variational spatial interactions hidden among airports and show limited performances on the traffic flow prediction task for the aviation system which is composed of several airports. In this paper, we consider the multi-airport scenario and propose a novel spatio-temporal hybrid deep learning model to efficiently capture spatial correlations as well as temporal dependencies in a parallelized way. Specifically, we introduce the causal inference among airports to model their interactions and thus construct adaptive causality graphs in a data-driven manner to address the heterogeneity of airports. Furthermore, given that multi-source features are not applicable for all airports, a feature mask module is designated to adaptively select the features in spatial information mining. Extensive experiments are conducted on the real data of top-30 busiest airports in China. The results show that our spatio-temporal deep learning approach is superior to state-of-the-art methodologies and the improvement ratio is up to 4.7% against benchmarks. Ablation studies emphasize the power of the proposed adaptive causality graph and the feature mask module. All of these prove the effectiveness of the proposed methodology.
资助项目National Natural Science Foundation of China[61961146005] ; National Key Research and Development Program of China[2022ZD0119600] ; National Key Research and Development Program of China[2019YFF0301400]
WOS研究方向Engineering ; Transportation
语种英语
WOS记录号WOS:001064546000001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China ; National Key Research and Development Program of China
源URL[http://ir.ia.ac.cn/handle/173211/53191]  
专题多模态人工智能系统全国重点实验室
通讯作者Cao, Xianbin
作者单位1.Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.Shandong Jiaotong Univ, Shandong Key Lab Smart Transportat Preparat, Jinan 250353, Peoples R China
4.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
5.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
6.Beihang Univ, Key Lab Adv Technol Near Space Informat Syst, Beijing 100191, Peoples R China
推荐引用方式
GB/T 7714		 Du, Wenbo,Chen, Shenwen,Li, Zhishuai,et al. A Spatial-Temporal Approach for Multi-Airport Traffic Flow Prediction Through Causality Graphs[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2023:13.
APA Du, Wenbo,Chen, Shenwen,Li, Zhishuai,Cao, Xianbin,&Lv, Yisheng.(2023).A Spatial-Temporal Approach for Multi-Airport Traffic Flow Prediction Through Causality Graphs.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,13.
MLA Du, Wenbo,et al."A Spatial-Temporal Approach for Multi-Airport Traffic Flow Prediction Through Causality Graphs".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2023):13.

入库方式： OAI收割

来源：自动化研究所

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