A hybrid approach for short-term traffic flow forecasting based on similarity identification

doi:10.1142/S0217984921502122

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	A hybrid approach for short-term traffic flow forecasting based on similarity identification
	Li, Wenjun 1; Chen, Si 1; Wang, Xiaoquan 2; Yin, Chaoying 3; Huang, Zhaoguo4
	2021-05-10
发表期刊	MODERN PHYSICS LETTERS B
ISSN	0217-9849
卷号	35 期号:13
摘要	Short-term traffic flow forecasting is a key component of intelligent transportation system, yet difficult to be forecasted reliably, and accurately. A novel hybrid forecasting model is proposed by combining three predictors, namely, the autoregressive integrated moving average (ARIMA), back propagation neural network (BPNN) and support vector regression (SVR). First, it is assumed that all previous intervals can have influence on the predicted interval and then the entropy-based gray relation analysis method is applied to analyze the correlation and determine the length of time constrain window. Second, an improved Euclidean distance is employed to identify the similarity. Furthermore, the rank-exponent method is utilized to rank the results according to the similarity and fuse the predicted values of the predictors. Finally, a numerical experiment is implemented, which indicates that the performance of forecasting results is superior to the conventional ones.
关键词	Traffic flow short-term traffic forecasting similarity identification time constrain window entropy-based gray relation analysis rank-exponent method
DOI	10.1142/S0217984921502122
收录类别	SCIE ; SCOPUS
语种	英语
WOS研究方向	Physics
WOS类目	Physics, Applied ; Physics, Condensed Matter ; Physics, Mathematical
WOS记录号	WOS:000647748400001
出版者	WORLD SCIENTIFIC PUBL CO PTE LTD
来源库	WOS
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/148216
专题	土木工程学院
通讯作者	Wang, Xiaoquan
作者单位	1.Jiangsu Univ Sci & Technol, Sch Econ & Management, Zhenjiang 212003, Peoples R China; 2.Beijing Jiaotong Univ, Sch Traff & Transportat, Beijing 100044, Peoples R China; 3.Nanjing Forestry Univ, Coll Automobile & Traff Engn, Nanjing 210037, Peoples R China; 4.Lanzhou Univ Technol, Sch Civil Engn, Lanzhou 730050, Peoples R China
推荐引用方式 GB/T 7714	Li, Wenjun,Chen, Si,Wang, Xiaoquan,et al. A hybrid approach for short-term traffic flow forecasting based on similarity identification[J]. MODERN PHYSICS LETTERS B,2021,35(13).
APA	Li, Wenjun,Chen, Si,Wang, Xiaoquan,Yin, Chaoying,&Huang, Zhaoguo.(2021).A hybrid approach for short-term traffic flow forecasting based on similarity identification.MODERN PHYSICS LETTERS B,35(13).
MLA	Li, Wenjun,et al."A hybrid approach for short-term traffic flow forecasting based on similarity identification".MODERN PHYSICS LETTERS B 35.13(2021).

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