Adaptive Neuro-Fuzzy Inference System for Traffic Cycle Optimization
Taylor & Francis
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.
Awad, W., Al-Agtash, S., & Nsour, A. (2004). Adaptive Neuro-Fuzzy Inference System for Traffic Cycle Optimization. International Journal of Modelling and Simulation, 24(2), 80–84. https://doi.org/10.1080/02286203.2004.11442290