Data driven hybrid fuzzy model for short-term traffic flow prediction
文献类型：期刊论文
| 作者 | Li, Chengdong1; Yan, Bingyang1; Tang, Minjia1; Yi, Jianqiang2 ; Zhang, Xiqiao3
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| 刊名 | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
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| 出版日期 | 2018 |
| 卷号 | 35期号:6页码:6525-6536 |
| 关键词 | Traffic flow prediction fuzzy method single input rule module least square learning traffic-flow pattern |
| ISSN号 | 1064-1246 |
| DOI | 10.3233/JIFS-18883 |
| 通讯作者 | Li, Chengdong(lichengdong@sdjzu.edu.cn) |
| 英文摘要 | Traffic 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. |
| WOS关键词 | NEURAL-NETWORK ; ALGORITHM |
| 资助项目 | 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] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000459214900066 |
| 出版者 | IOS PRESS |
| 资助机构 | National Natural Science Foundation of China ; Taishan Scholar Project of Shandong Province ; Colleges and Universities Independent Innovation Program of Jinan City |
| 源URL | [http://ir.ia.ac.cn/handle/173211/25046]  |
| 专题 | 综合信息系统研究中心_飞行器智能技术 |
| 通讯作者 | Li, Chengdong |
| 作者单位 | 1.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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：自动化研究所
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