Research on Traffic Flow Forecasting Based on Deep Learning

doi:10.1007/978-981-99-8761-0_8

IR > 计算机与通信学院

	Research on Traffic Flow Forecasting Based on Deep Learning
	Zhang, Hong1 ; Zhao, Tianxin 1; Cao, Jie1,2,3 ; Kan, Sunan 1
	2024
会议名称	38th CCF National Conference of Computer Applications, CCF NCCA 2023
会议录名称	Communications in Computer and Information Science
卷号	1960 CCIS
页码	85-100
会议日期	July 16, 2023 - July 20, 2023
会议地点	Suzhou, China
出版者	Springer Science and Business Media Deutschland GmbH
摘要	Traffic flow forecasting (TFF) is the key technology of intelligent transportation systems, plays a vital role in intelligent transportation and has attracted the attention of researchers worldwide. Forecast methods and models based on deep learning (DL) are the current research hotspots in this field. On the basis of the main methods and models of TFF, this paper mainly reviews the related research on TFF based on DL. First, from the perspective of scientometrics, the researchers, countries, and institutions of TFF based on DL are counted, and the cocitation network of keywords, journals, and authors is analysed. Then, TFF methods based on DL are reviewed from three aspects: time series, space-time, and spatiotemporal graphs. This paper focuses on research on forecast methods based on spatiotemporal graphs, clarifies the research trends in this field from the aspects of graph spatiotemporal networks, graph autoencoders, and graph attention networks, and summarizes the structure and characteristics of different forecast models. Finally, from the aspects of applied research and model research, the follow-up research issues, challenges, and future research directions in this field are discussed. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
关键词	Deep learning - Forecasting - Intelligent systems - Intelligent vehicle highway systems Deep learning - Forecast method - Forecast models - Graph neural networks - Intelligent transportation systems - ITS - Key technologies - Scientific measurement - Spatio-temporal graphs - Traffic flow forecasting
DOI	10.1007/978-981-99-8761-0_8
收录类别	EI
语种	英语
EI入藏号	20235215288403
EI主题词	Graph neural networks
EI分类号	406.1 Highway Systems - 461.4 Ergonomics and Human Factors Engineering - 723.4 Artificial Intelligence - 723.5 Computer Applications
ISSN	1865-0929
原始文献类型	Conference article (CA)
EISSN	1865-0937
引用统计	无
文献类型	会议论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/169331
专题	计算机与通信学院兰州理工大学
通讯作者	Cao, Jie
作者单位	1.College of Computer and Communication, Lanzhou University of Technology, Lanzhou; 730050, China; 2.Lanzhou City University, Lanzhou; 730070, China; 3.Lanzhou University of Technology, Lanzhou; 730050, China
第一作者单位	兰州理工大学
通讯作者单位	兰州理工大学
推荐引用方式 GB/T 7714	Zhang, Hong,Zhao, Tianxin,Cao, Jie,et al. Research on Traffic Flow Forecasting Based on Deep Learning[C]:Springer Science and Business Media Deutschland GmbH,2024:85-100.