Research on Traffic Flow Forecasting Based on Deep Learning
Zhang, Hong1; Zhao, Tianxin1; Cao, Jie1,2,3; Kan, Sunan1
2024
Conference Name38th CCF National Conference of Computer Applications, CCF NCCA 2023
Source PublicationCommunications in Computer and Information Science
Volume1960 CCIS
Pages85-100
Conference DateJuly 16, 2023 - July 20, 2023
Conference PlaceSuzhou, China
PublisherSpringer Science and Business Media Deutschland GmbH
AbstractTraffic 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.
KeywordDeep 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
DOI10.1007/978-981-99-8761-0_8
Indexed ByEI
Language英语
EI Accession Number20235215288403
EI KeywordsGraph neural networks
EI Classification Number406.1 Highway Systems - 461.4 Ergonomics and Human Factors Engineering - 723.4 Artificial Intelligence - 723.5 Computer Applications
ISSN1865-0929
Original literature typeConference article (CA)
Citation statistics
none
Document Type会议论文
Identifierhttps://ir.lut.edu.cn/handle/2XXMBERH/169331
Collection计算机与通信学院
兰州理工大学
Corresponding AuthorCao, Jie
Affiliation1.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
First Author AffilicationLanzhou University of Technology
Corresponding Author AffilicationLanzhou University of Technology
Recommended Citation
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.
Files in This Item:
There are no files associated with this item.
Related Services
Usage statistics
Google Scholar
Similar articles in Google Scholar
[Zhang, Hong]'s Articles
[Zhao, Tianxin]'s Articles
[Cao, Jie]'s Articles
Baidu academic
Similar articles in Baidu academic
[Zhang, Hong]'s Articles
[Zhao, Tianxin]'s Articles
[Cao, Jie]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Zhang, Hong]'s Articles
[Zhao, Tianxin]'s Articles
[Cao, Jie]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.