Automatic Node Traffic Prediction Using Neural Network Modeling

Increasing traffic volume and creating traffic nodes in interurban and urban traffic networks will reduce the efficiency of the traffic network and the desired routes. Anticipating and discovering these traffic nodes as soon as possible can help solve the problem and streamline traffic flow. Artificial neural networks have shown that they are able to perform very well on their own learning capabilities. The main objective of this research is to predict and automatically detect traffic nodes using the intelligent neural network model and compare the model's performance with other models. By using educational data, the artificial neural network can be trained so that it can detect the desired output and perform predictive prediction on target data. The research method is to predict the network architecture from three input parameters and one output parameter. In this research, three types of artificial neural networks are used to predict and automatically detect traffic nodes. The data used in this research is the actual information of the traffic control center of Tehran-Karaj Freeway on a daily, weekly and monthly basis. Initially, the multilayer perceptron of artificial neural network was used, and another neural network used in this research was neuro-fuzzy network and eventually the neural network of the radial-based function was used to examine the success of the two previous networks. Based on the results, the efficiency and accuracy of different models based on the best and most comprehensive set of evaluation indicators were used to evaluate the performance of each of the models as well as to compare their efficiency with each other. Finally, the perceptron model was introduced with optimal efficiency. Comparison of the results of the traffic flow predicted values ​​with the measured values ​​in reality shows that the proposed model satisfies the traffic flow satisfactorily.