A multivariate short-term traffic flow forecasting method based on wavelet analysis and seasonal time series | |
Zhang, Hong1,2; Wang, Xiaoming2; Cao, Jie1; Tang, Minan3,4; Guo, Yirong2 | |
2018-10 | |
发表期刊 | APPLIED INTELLIGENCE |
ISSN | 0924-669X |
卷号 | 48期号:10页码:3827-3838 |
摘要 | Short-term traffic flow forecasting is a key step to achieve the performance of intelligent transportation system (ITS). Timely and accurate traffic information prediction is also the prerequisite of realizing proactive traffic control and dynamic traffic assignment effectively. Based on the fact that univariate forecasting methods have limited forecasting abilities when the data is missing or erroneous and that single models make no full use of information underline data, a new hybrid method with multivariate for short-term traffic flow forecasting is proposed. This method combines statistical analysis method with computational intelligence techniques to mine the characteristic of traffic flow as well as forecast short-term traffic state. First, the wavelet de-noising is employed to remove the noise information. Then, time series analysis is used to analyze time-varying and periodic characteristic of traffic flow. Furthermore, the seasonal auto-regressive moving average with external input (SARIMAX) is established to fit traffic flow with occupancy as exogenous variables. Finally, wavelet forecast is adopted to forecast the values of occupancy which are used as exogenous input, and a WSARIMAX is constructed to forecast traffic flow. Using the relationship of flow and occupancy at the same road section and taking traffic flow and occupancy data from freeway I-694 EB in the Twin Cities as endogenous variables and exogenous variables respectively, this paper studies the forecasting performance of the proposed method. The study results are encouraging. Compared with SARIMA newly proposed in literature, WSARIMA and SARIMAX improved method with wavelet analysis and multivariate modeling method, the proposed method gets improvements of 12.95%, 12.62% and 10.41% in forecasting accuracy of one-step ahead respectively. For ten-steps ahead forecasting, it gets improvements of 18.87%, 17.05% and 2.57% in forecasting accuracy respectively. |
关键词 | Hybrid method Traffic flow Short-term forecasting Wavelet analysis Multivariate |
DOI | 10.1007/s10489-018-1181-7 |
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:000443262400037 |
出版者 | SPRINGER |
EI入藏号 | 20181805125831 |
EI主题词 | Street traffic control |
EI分类号 | 406.2 Roads and Streets - 723.4 Artificial Intelligence - 723.5 Computer Applications - 903.3 Information Retrieval and Use - 921 Mathematics - 922 Statistical Methods - 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/32432 |
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 Comp & Commun, Lanzhou 730050, Gansu, Peoples R China; 2.Lanzhou Univ Technol, Coll Elect & Informat Engn, 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 | 计算机与通信学院; 电气工程与信息工程学院 |
通讯作者单位 | 计算机与通信学院; 电气工程与信息工程学院 |
第一作者的第一单位 | 计算机与通信学院 |
推荐引用方式 GB/T 7714 | Zhang, Hong,Wang, Xiaoming,Cao, Jie,et al. A multivariate short-term traffic flow forecasting method based on wavelet analysis and seasonal time series[J]. APPLIED INTELLIGENCE,2018,48(10):3827-3838. |
APA | Zhang, Hong,Wang, Xiaoming,Cao, Jie,Tang, Minan,&Guo, Yirong.(2018).A multivariate short-term traffic flow forecasting method based on wavelet analysis and seasonal time series.APPLIED INTELLIGENCE,48(10),3827-3838. |
MLA | Zhang, Hong,et al."A multivariate short-term traffic flow forecasting method based on wavelet analysis and seasonal time series".APPLIED INTELLIGENCE 48.10(2018):3827-3838. |
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