A hybrid short-term traffic flow forecasting model based on time series multifractal characteristics | |
Zhang, Hong1,2; Wang, Xiaoming1; Cao, Jie2; Tang, Minan3,4; Guo, Yirong1 | |
2018-08 | |
发表期刊 | APPLIED INTELLIGENCE |
ISSN | 0924-669X |
卷号 | 48期号:8页码:2429-2440 |
摘要 | Short-term traffic flow forecasting is a key problem in the area of intelligent transportation systems (ITS). Timely and accurate traffic state prediction is also the prerequisite of realizing proactive traffic control and dynamic traffic assignment effectively. In this paper, a new hybrid model for short-term traffic flow forecasting, which is built based on multifractal characteristics of traffic flow time series, is proposed. The hybrid model decomposes traffic flow series into four different components, namely a periodic part, a trend part, a stationary part and a volatility part, to unearth the traffic features hidden behind the data. Four parts are treated and modeled separately by using different methods, such as spectral analysis, time series and statistical volatility analysis, to further explore the underlying traffic patterns and improve forecasting accuracy. Performance of the proposed hybrid model is investigated with traffic flow data from freeway I-694 EB in the Twin Cities. The experimental results indicate that the proposed model outperforms in capturing nonlinear volatility and improving forecasting accuracy than traditional forecasting methods, especially for the multi-step ahead forecasting. Compared with the ARIMA-GARCH model, it gets an improvement of 8.23% in RMSE for one-step ahead forecasting and 10.69% for ten-step ahead forecasting. It is better than the hybrid model newly proposed in literature (Zhang et al. Transp Res Part C: Emerg Technol 43(1):65-78 2014) and gets an improvement of 1.27% in forecasting accuracy. |
关键词 | Hybrid model Traffic flow Multifractal characteristics Short-term forecasting Periodic regression Volatility analysis |
DOI | 10.1007/s10489-017-1095-9 |
收录类别 | SCI ; SCIE |
语种 | 英语 |
资助项目 | Science and Technology Support Program of Gansu[1304GKCA023] |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000439158700032 |
出版者 | SPRINGER |
EI入藏号 | 20174904498045 |
EI主题词 | Street traffic control |
EI分类号 | 406.2 Roads and Streets - 723.4 Artificial Intelligence - 723.5 Computer Applications - 921 Mathematics - 922.2 Mathematical Statistics |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | https://ir.lut.edu.cn/handle/2XXMBERH/32530 |
专题 | 机电工程学院 兰州理工大学 电气工程与信息工程学院 计算机与通信学院 |
通讯作者 | Zhang, Hong |
作者单位 | 1.Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou 730050, Gansu, Peoples R China; 2.Lanzhou Univ Technol, Coll Comp & Commun, Lanzhou 730050, Gansu, Peoples R China; 3.Lanzhou Jiaotong Univ, Coll Automat & Elect Engn, Lanzhou 730070, Gansu, Peoples R China; 4.Lanzhou Univ Technol, Coll Mech & Elect Engn, Lanzhou 730050, Gansu, Peoples R China |
第一作者单位 | 电气工程与信息工程学院; 计算机与通信学院 |
通讯作者单位 | 电气工程与信息工程学院; 计算机与通信学院 |
第一作者的第一单位 | 电气工程与信息工程学院 |
推荐引用方式 GB/T 7714 | Zhang, Hong,Wang, Xiaoming,Cao, Jie,et al. A hybrid short-term traffic flow forecasting model based on time series multifractal characteristics[J]. APPLIED INTELLIGENCE,2018,48(8):2429-2440. |
APA | Zhang, Hong,Wang, Xiaoming,Cao, Jie,Tang, Minan,&Guo, Yirong.(2018).A hybrid short-term traffic flow forecasting model based on time series multifractal characteristics.APPLIED INTELLIGENCE,48(8),2429-2440. |
MLA | Zhang, Hong,et al."A hybrid short-term traffic flow forecasting model based on time series multifractal characteristics".APPLIED INTELLIGENCE 48.8(2018):2429-2440. |
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