CASIA OpenIR
Data driven hybrid fuzzy model for short-term traffic flow prediction
Li, Chengdong1; Yan, Bingyang1; Tang, Minjia1; Yi, Jianqiang2; Zhang, Xiqiao3
Source PublicationJOURNAL OF INTELLIGENT & FUZZY SYSTEMS
ISSN1064-1246
2018
Volume35Issue:6Pages:6525-6536
Corresponding AuthorLi, Chengdong(lichengdong@sdjzu.edu.cn)
AbstractTraffic flow prediction can not only improve the reasonability of the managers' decision-making and road planning effectively, but also provide helpful suggestions for travelers to avoid traffic congestion. In order to further improve the prediction accuracy of traffic flow, this study presents one data driven hybrid model for short-term traffic flow prediction. This hybrid model firstly extracts the periodicity pattern from the traffic flow data, then, constructs the functionally weighted single-input-rule-modules connected fuzzy inference system (FWSIRM-FIS) for the residual data after removing the periodicity pattern from the original data, and finally, generates the final prediction results through integrating the periodicity pattern and the output from the FWSIRM-FIS model. The partial autocorrelation function (PACF) method is adopted to determine the optimal inputs for the data driven FWSIRM-FIS model, and the iterative least square method is utilized to train the parameters of the FWSIRM-FIS. Furthermore, three detailed experiments on traffic flow prediction are made, and comprehensive comparisons with three popular artificial intelligence methods are done to verify the effectiveness and advantages of the proposed hybrid model. According to five comparison indices, the proposed hybrid model can achieve the best prediction performance, although with much less fuzzy rules. The error histograms also verify that the proposed hybrid model has the smallest prediction errors comparing to the three comparative methods. The hybrid approach proposed in this study can also be extended to some other applications which have periodicity patterns, e.g. the traveling time estimate and the electricity load forecasting.
KeywordTraffic flow prediction fuzzy method single input rule module least square learning traffic-flow pattern
DOI10.3233/JIFS-18883
WOS KeywordNEURAL-NETWORK ; ALGORITHM
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61473176] ; National Natural Science Foundation of China[61573225] ; Taishan Scholar Project of Shandong Province ; Colleges and Universities Independent Innovation Program of Jinan City[201303008] ; National Natural Science Foundation of China[61473176] ; National Natural Science Foundation of China[61573225] ; Taishan Scholar Project of Shandong Province ; Colleges and Universities Independent Innovation Program of Jinan City[201303008]
Funding OrganizationNational Natural Science Foundation of China ; Taishan Scholar Project of Shandong Province ; Colleges and Universities Independent Innovation Program of Jinan City
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000459214900066
PublisherIOS PRESS
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/25047
Collection中国科学院自动化研究所
Corresponding AuthorLi, Chengdong
Affiliation1.Shandong Jianzhu Univ, Sch Informat & Elect Engn, Jinan 250101, Shandong, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
3.Harbin Inst Technol, Sch Transportat Sci & Technol, Harbin, Heilongjiang, Peoples R China
Recommended Citation
GB/T 7714
Li, Chengdong,Yan, Bingyang,Tang, Minjia,et al. Data driven hybrid fuzzy model for short-term traffic flow prediction[J]. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS,2018,35(6):6525-6536.
APA Li, Chengdong,Yan, Bingyang,Tang, Minjia,Yi, Jianqiang,&Zhang, Xiqiao.(2018).Data driven hybrid fuzzy model for short-term traffic flow prediction.JOURNAL OF INTELLIGENT & FUZZY SYSTEMS,35(6),6525-6536.
MLA Li, Chengdong,et al."Data driven hybrid fuzzy model for short-term traffic flow prediction".JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 35.6(2018):6525-6536.
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