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

doi:10.1109/TITS.2023.3308903

CORC > 自动化研究所 > 中国科学院自动化研究所 > 多模态人工智能系统全国重点实验室

	A Spatial-Temporal Approach for Multi-Airport Traffic Flow Prediction Through Causality Graphs
	Du, Wenbo 2,3; Chen, Shenwen 2,3; Li, Zhishuai 4; Cao, Xianbin 2,3; Lv, Yisheng 1,5,6
刊名	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
	2023-09-04
页码	13
关键词	Airport traffic flow predictive models deep learning causality graph spatiotemporal analysis
ISSN号	1524-9050
DOI	10.1109/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
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:001064546000001
资助机构	National Natural Science Foundation of China ; National Key Research and Development Program of China
内容类型	期刊论文
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.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 2.Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China 3.Beihang Univ, Key Lab Adv Technol Near Space Informat Syst, Beijing 100191, Peoples R China 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 5.Shandong Jiaotong Univ, Shandong Key Lab Smart Transportat Preparat, Jinan 250353, Peoples R China 6.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, 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.