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Journal of Applied Mathematics
Volume 2013, Article ID 195824, 7 pages
Research Article

Adaptive Correction Forecasting Approach for Urban Traffic Flow Based on Fuzzy -Mean Clustering and Advanced Neural Network

1Shanghai Urban-Rural Construction and Transportation Development Institute, Shanghai 300032, China
2Department of Automation, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai 200240, China

Received 5 July 2013; Revised 8 October 2013; Accepted 9 October 2013

Academic Editor: Baocang Ding

Copyright © 2013 He Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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 -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.