A hybrid short-term traffic flow forecasting model based on time series multifractal characteristics

doi:10.1007/s10489-017-1095-9

IR > 机电工程学院

	A hybrid short-term traffic flow forecasting model based on time series multifractal characteristics
	Zhang, Hong1,2 ; Wang, Xiaoming 1; Cao, Jie2 ; Tang, Minan 3,4; Guo, Yirong 1
	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	被引频次：21[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/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.

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
Zhang-2018-A hybrid （3078KB）	期刊论文	出版稿	开放获取	CC BY-NC-SA	浏览下载