A radial basis function neural network approach to traffic flow forecasting
A radial basis function neural network approach to traffic flow forecasting
- New search for: Xi-Huai Wang,
- New search for: Jian-Mei Xiao,
- New search for: Xi-Huai Wang,
- New search for: Jian-Mei Xiao,
Many components of intelligent transportation systems require different levels of the traffic flow forecasting. This paper presents a novel short-term traffic flow forecasting model using a distributed radial basis function neural network (RBFNN) based on adaptive fuzzy c-means (FCM) clustering algorithm. FCM clustering algorithm is used to classify training objects into a couple of clusters, each cluster is trained by a sub RBFNN, and membership values are used for combining several RBFNN outputs to obtain the final result. In the online stage, the membership values are computed using an adaptive fuzzy clustering algorithm for the new object. The real traffic data are used to demonstrate the effectiveness of the method.
Document information
A radial basis function neural network approach to traffic flow forecasting
2003-01-01
242699 byte
Conference paper
Electronic Resource
English
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