Adaptive Neuro-Fuzzy Inference System for Traffic Cycle Optimization

Document Type

Article

Publication Date

1-2004

Publisher

Taylor & Francis

Abstract

An adaptive neuro-fuzzy approach is employed to optimize the traffic cycle on Al-Rabia signalized intersection in Amman, the capital of Jordan. A structural fuzzy framework is proposed to estimate traffic flow volumes on the intersection using actual traffic counts for four weeks. More than 80% of the data is used to train the model, and the rest is used to test the generalization capability of the model. Results show that the neuro-fuzzy approach is better in estimating traffic volume than the neural networks approach.

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