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

He Huang | Zendy; Qifeng Tang | Zendy; Zhen Liu | Zendy

Open Access

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

Author(s) -

He Huang,

Qifeng Tang,

Zhen Liu

Publication year - 2013

Publication title -

journal of applied mathematics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.307

H-Index - 43

eISSN - 1687-0042

pISSN - 1110-757X

DOI - 10.1155/2013/195824

Subject(s) - computer science , robustness (evolution) , traffic flow (computer networking) , python (programming language) , artificial neural network , algorithm , artificial intelligence , generalization , fuzzy logic , machine learning , data mining , mathematics , mathematical analysis , biochemistry , chemistry , computer security , gene , operating system

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

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