Developing a multiview spatiotemporal model based on deep graph neural networks to predict the travel demand by bus

doi:10.1080/13658816.2023.2203218

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

	Developing a multiview spatiotemporal model based on deep graph neural networks to predict the travel demand by bus
	Zhao, Tianhong 1,2; Huang, Zhengdong 1; Tu, Wei 1; Biljecki, Filip 2,3; Chen, Long 4
刊名	INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
	2023-04-25
页码	27
关键词	Graph deep learning multiview learning travel demand prediction multimodal transportation smart card data
ISSN号	1365-8816
DOI	10.1080/13658816.2023.2203218
通讯作者	Tu, Wei(tuwei@szu.edu.cn)
英文摘要	The accurate prediction of travel demand by bus is crucial for effective urban mobility demand management. However, most models of travel demand prediction by bus tend to focus on the bus's spatiotemporal dependencies, while ignoring the interactions between buses and other transportation modes, such as metros and taxis. We propose a Multiview Spatiotemporal Graph Neural Network (MSTGNN) model to predict short-term travel demand by bus. It emphasizes the ability to capture the interaction dependencies among the travel demand of buses, metros, and taxis. Firstly, a multiview graph consisting of bus, metro, and taxi views is constructed, with each view containing both a local and global graph. Secondly, a multiview attention-based temporal graph convolution module is developed to capture spatiotemporal and cross-view interaction dependencies among different transport modes. Especially, to address the uneven spatial distributions of features in multiview learning, the cross-view spatial feature consistency loss is introduced as an auxiliary loss. Finally, we conduct intensive experiments using a real-world dataset from Shenzhen, China. The results demonstrate that our proposed MSTGNN model performs better than the existing models. Ablation experiments validate the contributions of various modes of transportation to the improvement of the model's performance.
资助项目	National Natural Science Foundation of China[42071357] ; National Natural Science Foundation of China[42071360] ; National Natural Science Foundation of China[42001393] ; Key Project of Natural Science Foundation of Shenzhen[JCYJ20220818100200001] ; Basic Research Program of Shenzhen Science and Technology Innovation Committee[JCYJ20220530152817039] ; KartoBit Research Network[KRN2202GK] ; Guangdong Science and Technology Strategic Innovation Fund ; Guangdong-Hong Kong-Macau Joint Laboratory Program[2020B1212030009]
WOS关键词	MOBILITY PATTERNS ; PASSENGER DEMAND ; GPS TRAJECTORIES ; URBAN TRANSPORT ; PUBLIC-TRANSIT ; RIDERSHIP ; METRO ; WEATHER ; CHOICE ; DETERMINANTS
WOS研究方向	Computer Science ; Geography ; Physical Geography ; Information Science & Library Science
语种	英语
出版者	TAYLOR & FRANCIS LTD
WOS记录号	WOS:000977063900001
资助机构	National Natural Science Foundation of China ; Key Project of Natural Science Foundation of Shenzhen ; Basic Research Program of Shenzhen Science and Technology Innovation Committee ; KartoBit Research Network ; Guangdong Science and Technology Strategic Innovation Fund ; Guangdong-Hong Kong-Macau Joint Laboratory Program
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/53215]
专题	多模态人工智能系统全国重点实验室
通讯作者	Tu, Wei
作者单位	1.Shenzhen Univ, Sch Architecture & Urban Planning, Shenzhen, Peoples R China 2.Natl Univ Singapore, Dept Architecture, Singapore, Singapore 3.Natl Univ Singapore, Dept Real Estate, Singapore, Singapore 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Tianhong,Huang, Zhengdong,Tu, Wei,et al. Developing a multiview spatiotemporal model based on deep graph neural networks to predict the travel demand by bus[J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,2023:27.
APA	Zhao, Tianhong,Huang, Zhengdong,Tu, Wei,Biljecki, Filip,&Chen, Long.(2023).Developing a multiview spatiotemporal model based on deep graph neural networks to predict the travel demand by bus.INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,27.
MLA	Zhao, Tianhong,et al."Developing a multiview spatiotemporal model based on deep graph neural networks to predict the travel demand by bus".INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE (2023):27.