A Queue Hybrid Neural Network with Weather Weighted Factor for Traffic Flow Prediction

doi:10.1109/CSCWD49262.2021.9437626

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	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.