Adaptive Correction Forecasting Approach for Urban Traffic Flow Based on Fuzzy c-Mean Clustering and Advanced Neural Network
Forecasting of urban traffic flow is important to intelligent transportation system (ITS) developments and implementations. The precise forecasting of traffic flow will be pretty helpful to relax road traffic congestion. The accuracy of traditional single model without correction mechanism is poor....
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2013/195824 |
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| _version_ | 1850174422950871040 |
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| author | He Huang Qifeng Tang Zhen Liu |
| author_facet | He Huang Qifeng Tang Zhen Liu |
| author_sort | He Huang |
| collection | DOAJ |
| description | Forecasting of urban traffic flow is important to intelligent transportation system (ITS) developments and implementations. The precise forecasting of traffic flow will be pretty helpful to relax road traffic congestion. The accuracy of traditional single model without correction mechanism is poor. Summarizing the existing prediction models and considering the characteristics of the traffic itself, a traffic flow prediction model based on fuzzy c-mean clustering method (FCM) and advanced neural network (NN) was proposed. FCM can improve the prediction accuracy and robustness of the model, while advanced NN can optimize the generalization ability of the model. Besides these, the output value of the model is calibrated by the correction mechanism. The experimental results show that the proposed method has better prediction accuracy and robustness than the other models. |
| format | Article |
| id | doaj-art-d5f875f0b71b411bbba8eba665808a69 |
| institution | OA Journals |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-d5f875f0b71b411bbba8eba665808a692025-08-20T02:19:40ZengWileyJournal of Applied Mathematics1110-757X1687-00422013-01-01201310.1155/2013/195824195824Adaptive Correction Forecasting Approach for Urban Traffic Flow Based on Fuzzy c-Mean Clustering and Advanced Neural NetworkHe Huang0Qifeng Tang1Zhen Liu2Shanghai Urban-Rural Construction and Transportation Development Institute, Shanghai 300032, ChinaDepartment of Automation, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai 200240, ChinaShanghai Urban-Rural Construction and Transportation Development Institute, Shanghai 300032, ChinaForecasting of urban traffic flow is important to intelligent transportation system (ITS) developments and implementations. The precise forecasting of traffic flow will be pretty helpful to relax road traffic congestion. The accuracy of traditional single model without correction mechanism is poor. Summarizing the existing prediction models and considering the characteristics of the traffic itself, a traffic flow prediction model based on fuzzy c-mean clustering method (FCM) and advanced neural network (NN) was proposed. FCM can improve the prediction accuracy and robustness of the model, while advanced NN can optimize the generalization ability of the model. Besides these, the output value of the model is calibrated by the correction mechanism. The experimental results show that the proposed method has better prediction accuracy and robustness than the other models.http://dx.doi.org/10.1155/2013/195824 |
| spellingShingle | He Huang Qifeng Tang Zhen Liu Adaptive Correction Forecasting Approach for Urban Traffic Flow Based on Fuzzy c-Mean Clustering and Advanced Neural Network Journal of Applied Mathematics |
| title | Adaptive Correction Forecasting Approach for Urban Traffic Flow Based on Fuzzy c-Mean Clustering and Advanced Neural Network |
| title_full | Adaptive Correction Forecasting Approach for Urban Traffic Flow Based on Fuzzy c-Mean Clustering and Advanced Neural Network |
| title_fullStr | Adaptive Correction Forecasting Approach for Urban Traffic Flow Based on Fuzzy c-Mean Clustering and Advanced Neural Network |
| title_full_unstemmed | Adaptive Correction Forecasting Approach for Urban Traffic Flow Based on Fuzzy c-Mean Clustering and Advanced Neural Network |
| title_short | Adaptive Correction Forecasting Approach for Urban Traffic Flow Based on Fuzzy c-Mean Clustering and Advanced Neural Network |
| title_sort | adaptive correction forecasting approach for urban traffic flow based on fuzzy c mean clustering and advanced neural network |
| url | http://dx.doi.org/10.1155/2013/195824 |
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