Traffic Flow Analytics and Prediction using Nonlinear Autoregressive Network with Exogenous Inputs Technique
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Abstract
This paper proposes the traffic flow analytics and prediction using nonlinear autoregressive network with exogenous inputs technique (NARX). The learning of neural network is divided into 2 configuration parts. The configuration delays are 2 and 4 to compare the results and find optimal configuration value for create NARX model using the simulate data. The chaotic simulate data set are generate by Mackey glass equation. Then analyze data set using NARX model. This able to developed to application or web application used in Bangkok.
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References
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