A Queue Hybrid Neural Network with Weather Weighted Factor for Traffic Flow Prediction
Miao, Fengman; Tao, Long; Xue, Jianbin; Zhang, Xijun
2021
会议名称24th IEEE International Conference on Computer Supported Cooperative Work in Design (IEEE CSCWD)
会议录名称PROCEEDINGS OF THE 2021 IEEE 24TH INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK IN DESIGN (CSCWD)
页码788-793
会议日期MAY 05-07, 2021
会议地点Dalian, PEOPLES R CHINA
出版地NEW YORK
出版者IEEE
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.
关键词queue hybrid structure weather weighted factor traffic flow prediction long short-term memory gated recurrent unit
DOI10.1109/CSCWD49262.2021.9437626
收录类别CPCI-S
语种英语
WOS记录号WOS:000716858200134
引用统计
被引频次:2[WOS]   [WOS记录]     [WOS相关记录]
文献类型会议论文
条目标识符https://ir.lut.edu.cn/handle/2XXMBERH/150133
专题研究生院
计算机与通信学院
通讯作者Miao, Fengman
作者单位Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou, Peoples R 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
Miao, Fengman,Tao, Long,Xue, Jianbin,et al. A Queue Hybrid Neural Network with Weather Weighted Factor for Traffic Flow Prediction[C]. NEW YORK:IEEE,2021:788-793.
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