A Hybrid Short-Term Traffic Flow Multistep Prediction Method Based on Variational Mode Decomposition and Long Short-Term Memory Model
Timely and accurate traffic prediction information is essential for advanced traffic management system (ATMS) and advanced traveler information system (ATIS). Because of the characteristics of nonlinearity, nonstationarity, and randomness, short-term traffic flow prediction could be still a challeng...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2021/4097149 |
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| _version_ | 1832561562751074304 |
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| author | Qichun Bing Fuxin Shen Xiufeng Chen Weijian Zhang Yanran Hu Dayi Qu |
| author_facet | Qichun Bing Fuxin Shen Xiufeng Chen Weijian Zhang Yanran Hu Dayi Qu |
| author_sort | Qichun Bing |
| collection | DOAJ |
| description | Timely and accurate traffic prediction information is essential for advanced traffic management system (ATMS) and advanced traveler information system (ATIS). Because of the characteristics of nonlinearity, nonstationarity, and randomness, short-term traffic flow prediction could be still a challenging task. In this study, a hybrid short-term traffic flow multistep prediction method is proposed by combining the variational mode decomposition (VMD) algorithm and long short-term memory (LSTM) model. Firstly, the VMD algorithm is employed to decompose the original traffic flow data into a series of intrinsic mode function (IMF) components. Secondly, different LSTM models are established to predict different IMF components. For each prediction model, one-step to three-step predictions are carried out. Finally, the component prediction results are aggregated to obtain the final traffic flow multistep prediction values. The prediction performance of the proposed hybrid model is investigated using inductive loop data measured from the north-south viaduct expressway in Shanghai. The experiment results show that (1) VMD algorithm could effectively avoid the problems of endpoint effects and modal aliasing, and the decomposition effect is better than empirical mode decomposition algorithm and wavelet decomposition algorithm; (2) among all the involved methods, the proposed hybrid model is more effective and robust in extracting the trend information, which has the best multistep prediction performance. |
| format | Article |
| id | doaj-art-4f71f72079614da394cec063fe131aa4 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-4f71f72079614da394cec063fe131aa42025-02-03T01:24:41ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2021-01-01202110.1155/2021/40971494097149A Hybrid Short-Term Traffic Flow Multistep Prediction Method Based on Variational Mode Decomposition and Long Short-Term Memory ModelQichun Bing0Fuxin Shen1Xiufeng Chen2Weijian Zhang3Yanran Hu4Dayi Qu5College of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, ChinaCollege of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, ChinaCollege of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, ChinaCollege of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, ChinaCollege of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, ChinaCollege of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, ChinaTimely and accurate traffic prediction information is essential for advanced traffic management system (ATMS) and advanced traveler information system (ATIS). Because of the characteristics of nonlinearity, nonstationarity, and randomness, short-term traffic flow prediction could be still a challenging task. In this study, a hybrid short-term traffic flow multistep prediction method is proposed by combining the variational mode decomposition (VMD) algorithm and long short-term memory (LSTM) model. Firstly, the VMD algorithm is employed to decompose the original traffic flow data into a series of intrinsic mode function (IMF) components. Secondly, different LSTM models are established to predict different IMF components. For each prediction model, one-step to three-step predictions are carried out. Finally, the component prediction results are aggregated to obtain the final traffic flow multistep prediction values. The prediction performance of the proposed hybrid model is investigated using inductive loop data measured from the north-south viaduct expressway in Shanghai. The experiment results show that (1) VMD algorithm could effectively avoid the problems of endpoint effects and modal aliasing, and the decomposition effect is better than empirical mode decomposition algorithm and wavelet decomposition algorithm; (2) among all the involved methods, the proposed hybrid model is more effective and robust in extracting the trend information, which has the best multistep prediction performance.http://dx.doi.org/10.1155/2021/4097149 |
| spellingShingle | Qichun Bing Fuxin Shen Xiufeng Chen Weijian Zhang Yanran Hu Dayi Qu A Hybrid Short-Term Traffic Flow Multistep Prediction Method Based on Variational Mode Decomposition and Long Short-Term Memory Model Discrete Dynamics in Nature and Society |
| title | A Hybrid Short-Term Traffic Flow Multistep Prediction Method Based on Variational Mode Decomposition and Long Short-Term Memory Model |
| title_full | A Hybrid Short-Term Traffic Flow Multistep Prediction Method Based on Variational Mode Decomposition and Long Short-Term Memory Model |
| title_fullStr | A Hybrid Short-Term Traffic Flow Multistep Prediction Method Based on Variational Mode Decomposition and Long Short-Term Memory Model |
| title_full_unstemmed | A Hybrid Short-Term Traffic Flow Multistep Prediction Method Based on Variational Mode Decomposition and Long Short-Term Memory Model |
| title_short | A Hybrid Short-Term Traffic Flow Multistep Prediction Method Based on Variational Mode Decomposition and Long Short-Term Memory Model |
| title_sort | hybrid short term traffic flow multistep prediction method based on variational mode decomposition and long short term memory model |
| url | http://dx.doi.org/10.1155/2021/4097149 |
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