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

文献类型：期刊论文

作者Zhao, Tianhong2,3; Huang, Zhengdong3; Tu, Wei3; Biljecki, Filip1,2; Chen, Long4
刊名INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
出版日期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
DOI10.1080/13658816.2023.2203218
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.
WOS关键词MOBILITY PATTERNS ; PASSENGER DEMAND ; GPS TRAJECTORIES ; URBAN TRANSPORT ; PUBLIC-TRANSIT ; RIDERSHIP ; METRO ; WEATHER ; CHOICE ; DETERMINANTS
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研究方向Computer Science ; Geography ; Physical Geography ; Information Science & Library Science
语种英语
WOS记录号WOS:000977063900001
出版者TAYLOR & FRANCIS LTD
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
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.Natl Univ Singapore, Dept Real Estate, Singapore, Singapore
2.Natl Univ Singapore, Dept Architecture, Singapore, Singapore
3.Shenzhen Univ, Sch Architecture & Urban Planning, Shenzhen, Peoples R China
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.

入库方式： OAI收割

来源：自动化研究所

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