A hybrid approach for short-term traffic flow forecasting based on similarity identification | |
Li, Wenjun1; Chen, Si1; Wang, Xiaoquan2; Yin, Chaoying3; Huang, Zhaoguo4 | |
刊名 | MODERN PHYSICS LETTERS B |
2021 | |
卷号 | 35期号:13 |
关键词 | Traffic flow short-term traffic forecasting similarity identification time constrain window entropy-based gray relation analysis rank-exponent method |
ISSN号 | 0217-9849 |
DOI | 10.1142/S0217984921502122 |
英文摘要 | 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. |
WOS研究方向 | Physics |
语种 | 英语 |
出版者 | WORLD SCIENTIFIC PUBL CO PTE LTD |
WOS记录号 | WOS:000647748400001 |
内容类型 | 期刊论文 |
源URL | [http://ir.lut.edu.cn/handle/2XXMBERH/148216] |
专题 | 土木工程学院 |
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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