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Research on Traffic Flow Forecasting Based on Deep Learning | |
Zhang, Hong1![]() ![]() | |
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
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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
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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. |
条目包含的文件 | 条目无相关文件。 |
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