IJPAM: Volume 107, No. 4 (2016)

PERFORMANCE OF SELF-ORGANIZED AND
METACOGNITIVE NEUROFUZZY SYSTEM
FOR TRAFFIC FLOW PREDICTION

Kesavan Asaithambi$^1$, A. Nagoor Gani$^2$
$^{1,2}$Jamal Mohamed College (Autonomous)
Tiruchy, INDIA


Abstract. Accurate prediction of traffic flow is an important step needed in urban traffic management systems. While several neurofuzzy approaches have been used in literature for this particular problem, most of them need manual intervention in the formulation of the fuzzy rule base and also in determining the architecture of the neurofuzzy system. This paper evaluates two recent neurofuzzy algorithms that are capable of automatically determining the rule base and architecture in a purely data driven approach. An open source traffic data has been evaluate and compare the performance of these neurofuzzy systems.

Received: February 27, 2016

AMS Subject Classification:

Key Words and Phrases: system identification, neuro-fuzzy inference system, traffic flow prediction, meta-cognition

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DOI: 10.12732/ijpam.v107i4.20 How to cite this paper?

Source: International Journal of Pure and Applied Mathematics
ISSN printed version: 1311-8080
ISSN on-line version: 1314-3395
Year: 2016
Volume: 107
Issue: 4
Pages: 1025 - 1036


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