Interval prediction of short-term traffic speed with limited data input: Application of fuzzy-grey combined prediction model

doi:10.1016/j.eswa.2021.115878

CORC > 兰州理工大学 > 兰州理工大学 > 土木工程学院

	Interval prediction of short-term traffic speed with limited data input: Application of fuzzy-grey combined prediction model
	Song, Zhanguo 1; Feng, Wei 2; Liu, Weiwei 3
刊名	Expert Systems with Applications
	2022
卷号	187
关键词	Decision support systems Forecasting Intelligent vehicle highway systems Speed Street traffic control System theory Autoregressive modelling Autoregressive prediction model Fuzzy information granulation Gray autoregressive prediction model Intelligent transportation systems Interval prediction Limited data Prediction interval Short-term traffic speed Traffic speed
ISSN号	09574174
DOI	10.1016/j.eswa.2021.115878
英文摘要	Short-term traffic speed prediction, including level and interval prediction, is a key component of proactive traffic control in the intelligent transportation systems (ITS). In particular, predicting intervals may provide traffic managers with more useful information for making reasonable decisions than predicting traffic levels. In this study, a combined model (FIG-GARM) of fuzzy information granulation (FIG) and grey autoregressive model (GARM) is proposed for the prediction interval (PI) of traffic speed. In order to investigate the performance of the FIG-GARM model, using real-world traffic speed data collected from an urban freeway in Edmonton, Canada, and the proposed FIG-GARM model is compared with the interval-grey model first order single variable (GM (1,1)), FIG-GM (1,1), and interval-GARM for PI of traffic speed. The results show that the FIG-GARM model can generate workable PI of the traffic speed, proving the validity of the proposed model. In addition, the PI of traffic speed obtained by FIG-GARM model has higher prediction interval coverage probability (PICP), narrower width interval (WI), and higher index P, which can provide decision support for the robust and accurate prediction of intelligent transportation systems. © 2021 Elsevier Ltd
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science
语种	英语
出版者	Elsevier Ltd
WOS记录号	WOS:000705637500014
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/151147]
专题	土木工程学院
作者单位	1.School of Transportation, Southeast University, Nanjing; 210003, China; 2.School of Civil Engineering, Lanzhou University of Technology, Lanzhou; 730050, China; 3.School of Automobile, Chang'an University, Xi'an; Shaanxi; 710064, China
推荐引用方式 GB/T 7714	Song, Zhanguo,Feng, Wei,Liu, Weiwei. Interval prediction of short-term traffic speed with limited data input: Application of fuzzy-grey combined prediction model[J]. Expert Systems with Applications,2022,187.
APA	Song, Zhanguo,Feng, Wei,&Liu, Weiwei.(2022).Interval prediction of short-term traffic speed with limited data input: Application of fuzzy-grey combined prediction model.Expert Systems with Applications,187.
MLA	Song, Zhanguo,et al."Interval prediction of short-term traffic speed with limited data input: Application of fuzzy-grey combined prediction model".Expert Systems with Applications 187(2022).