Classification and prediction of road traffic using application-specific fuzzy clustering
IEEE Transactions on Fuzzy SystemsPublished 1 June 2002
C. Stutz, Thomas A. Runkler
Citations62
SJR quartileQ1
SJR score3.61
SNIP2.80
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TL;DR
F fuzzy methods for traffic data analysis are used and two successful application examples are given, one of which is the classification of traffic jam on a German autobahn and the second is a long-term prediction of traffic volume.
Abstract
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.
Keywords
Computer Science
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