Optimization of spatial-temporal graph: A taxi demand forecasting model based on spatial-temporal tree

doi:10.1016/j.inffus.2023.102178

CSpace

	Optimization of spatial-temporal graph: A taxi demand forecasting model based on spatial-temporal tree
	Li, Jianbo 1; Lv, Zhiqiang 1,2; Ma, Zhaobin 1; Wang, Xiaotong 1; Xu, Zhihao 1
	2024-04-01
发表期刊	INFORMATION FUSION
ISSN	1566-2535
卷号	104 页码:12
摘要	Taxi is one of the important means of transportation for people's daily travel activities, and it is one of the important research objects of intelligent transportation system. Taxi demand forecasting research can promote the application of urban transportation basic services and the transportation department to analyze and allocate transportation resources more reasonably. Graph structure is an important method for capturing spatial correlations among urban regions. However, it has certain limitations in capturing the hierarchical features and the local path features of regional nodes. Additionally, existing research has failed to capture multiple factors influencing changes in taxi demand. Therefore, this study proposes a spatial-temporal model based on capturing multi-factor features. The model innovatively uses the tree structure as a topology structure and proposes the tree convolution for constructing data spatial distribution features. The spatial-temporal convolution module with tree convolution as the core can effectively capture the hierarchical features and the local path features among area nodes. In this study, four factors affecting taxi demand are designed. The deep features of the four factors are further fused through the spatial-temporal convolution module. The model integrates multiple influencing factors affecting taxi demand from the spatial-temporal level and shows certain advantages in experiments. Compared with existing baselines, the model designed in this paper shows certain advantages in three real urban taxi datasets.
关键词	Intelligent transportation system Taxi demand Graph structure Tree structure Multiple factors
DOI	10.1016/j.inffus.2023.102178
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[U22B2057]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS记录号	WOS:001132846200001
出版者	ELSEVIER
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/38448
专题	中国科学院计算技术研究所
通讯作者	Lv, Zhiqiang
作者单位	1.Qingdao Univ, Coll Comp Sci & Technol, Qingdao 266701, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Li, Jianbo,Lv, Zhiqiang,Ma, Zhaobin,et al. Optimization of spatial-temporal graph: A taxi demand forecasting model based on spatial-temporal tree[J]. INFORMATION FUSION,2024,104:12.
APA	Li, Jianbo,Lv, Zhiqiang,Ma, Zhaobin,Wang, Xiaotong,&Xu, Zhihao.(2024).Optimization of spatial-temporal graph: A taxi demand forecasting model based on spatial-temporal tree.INFORMATION FUSION,104,12.
MLA	Li, Jianbo,et al."Optimization of spatial-temporal graph: A taxi demand forecasting model based on spatial-temporal tree".INFORMATION FUSION 104(2024):12.