Urban traffic flow prediction using a fuzzy-neural approach

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)85-98
Journal / PublicationTransportation Research Part C: Emerging Technologies
Issue number2
Publication statusPublished - Apr 2002
Externally publishedYes


This paper develops a fuzzy-neural model (FNM) to predict the traffic flows in an urban street network, which has long been considered a major element in the responsive urban traffic control systems. The FNM consists of two modules: a gate network (GN) and an expert network (EN). The GN classifies the input data into a number of clusters using a fuzzy approach, and the EN specifies the input-output relationship as in a conventional neural network approach. While the GN groups traffic patterns of similar characteristics into clusters, the EN models the specific relationship within each cluster. An online rolling training procedure is proposed to train the FNM, which enhances its predictive power through adaptive adjustments of the model coefficients in response to the real-time traffic conditions. Both simulation and real observation data are used to demonstrative the effectiveness of the method. © 2002 Elsevier Science Ltd. All rights reserved.

Research Area(s)

  • Fuzzy-neural model, Online rolling training procedure, Time series forecasting, Traffic flow prediction, Urban traffic control system