A Queue Hybrid Neural Network with Weather Weighted Factor for Traffic Flow Prediction | |
Miao, Fengman; Tao, Long; Xue, Jianbin; Zhang, Xijun | |
2021 | |
会议日期 | MAY 05-07, 2021 |
会议地点 | Dalian, PEOPLES R CHINA |
关键词 | queue hybrid structure weather weighted factor traffic flow prediction long short-term memory gated recurrent unit |
DOI | 10.1109/CSCWD49262.2021.9437626 |
页码 | 788-793 |
英文摘要 | In recent years, the development of short-term traffic flow prediction technology has been the focus of many scholars. Although the existing traffic flow prediction methods perform well, they still fail to reach the level of accurate prediction. This is mainly because the model structure they adopted is simple, the factors considered are not enough, and the data processing methods they adopted are single. In this paper, a queue hybrid neural network (QHNN) model based on long short-term memory (LSTM) and gated recurrent unit (GRU), with weather weighted factor, is proposed to predict traffic flow. Queue hybrid neural network is proposed to extract the characteristics of traffic flow. The calculation formula of weather weighted factor is constructed to take more weather factors into consideration. The experimental results show that the method proposed in this paper is superior to the existing advanced models. The experimental process is more scientific because it is carried out in a step-by-step optimization way. |
会议录 | PROCEEDINGS OF THE 2021 IEEE 24TH INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK IN DESIGN (CSCWD) |
会议录出版者 | IEEE |
会议录出版地 | NEW YORK |
语种 | 英语 |
WOS记录号 | WOS:000716858200134 |
内容类型 | 会议论文 |
源URL | [http://ir.lut.edu.cn/handle/2XXMBERH/150133] |
专题 | 研究生院 计算机与通信学院 |
作者单位 | Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou, Peoples R China |
推荐引用方式 GB/T 7714 | Miao, Fengman,Tao, Long,Xue, Jianbin,et al. A Queue Hybrid Neural Network with Weather Weighted Factor for Traffic Flow Prediction[C]. 见:. Dalian, PEOPLES R CHINA. MAY 05-07, 2021. |
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