Provides a Method for Detecting Traffic Nodes in Order to Increase the Optimum Performance of the Traffic Network

The traffic nodes are one of the most traffic-flow problems have been known to freeways. Discover the traffic nodes and fix are one of the main arteries, authorities and researchers in concerns has been. So presenting a suitable model for the detecting of nodes and taking necessary measures to accelerate the traffic-fiowing has certain importance. The purpose of the present research is forecasting and finding of automatically nodes by using neural network capabilities. Research methodology in this study is using three kinds of artificial neural network in order to discover the outcome of auto traffic nodes. That includes some laminated prspetron neural network, neural network of radial basis function and neural network neuro-fuzzy. The data which used in this research are actual data that obtained from control center of Tehran-Karaj freeway traffic daily, weekly and monthly. Finally the results which based on the evaluation indicators show that the perceptron network with three entrance parameters with two hidden layers of 15 neural in hidden layers has the best performance at comparing to the other models that optimize performance as the model which introduced.The research application is reducing the damages and problems which their causes are traffic nodes.