In the field of road traffic management, fuzzy techniques have already been used for traffic control. In this paper, we use fuzzy methods for traffic data analysis. The results of the data analysis are classification and prediction systems. Our work is focused on fuzzy clustering methods. The known clustering models are extended to: constrained prototypes, the use of a mix of different prototypes for one data set, partial supervision of the clustering, and the estimation of the number of clusters by cluster merging. Two successful application examples are given. The first one is the classification of traffic jam on a German autobahn, and the second application is a long-term prediction of traffic volume.
Classification and prediction of road traffic using application-specific fuzzy clustering
IEEE Transactions on Fuzzy Systems ; 10 , 3 ; 297-308
2002
12 Seiten, 25 Quellen
Article (Journal)
English
European Patent Office | 2016
|POP-TRAFFIC: A Novel Fuzzy Neural Approach to Road Traffic Analysis and Prediction
Online Contents | 2006
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