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A radial basis function neural network approach to traffic flow forecasting

Xi-Huai Wang, / 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.

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A radial basis function neural network approach to traffic flow forecasting

Xi-Huai Wang, / 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.

A radial basis function neural network approach to traffic flow forecasting

Xi-Huai Wang, / 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.

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Title:

A radial basis function neural network approach to traffic flow forecasting

Contributors:

Xi-Huai Wang, (author) / Jian-Mei Xiao, (author)

Published in:

Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems ; 1 ; 614-617 vol.1

Publication date:

2003-01-01

Size:

242699 byte

ISBN:

DOI:

https://doi.org/10.1109/ITSC.2003.1252025

Type of media:

Conference paper

Type of material:

Electronic Resource

Language:

English

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