The aim of this paper is to present an artificial neural network model with high accuracy to predict the delay of passenger trains in Iranian Railways. In the proposed model, we use three different methods to define inputs including normalized real number, binary coding, and binary set encoding inputs. One of the great challenges of using neural network is how to design a superior network for a specific task. To find an appropriate architecture, three different strategies called quick method, dynamic method, and multiple method are investigated. To prevent the proposed model from overfitting in modeling, according to cross validation, we divide existing passenger train delays data set into three subsets called training set, validation set, and testing set. To evaluate the proposed model, we compare the results of three different data input methods and three different architectures with each other and with some common prediction methods such as decision tree and multinomial logistic regression. For comparing different neural networks, we consider training time and accuracy of neural networks on test data set and network size. In addition, for comparing neural networks with other well-known prediction methods, we consider training time and the accuracy of neural network on test data sets. To make a fair comparison among all models, we sketch a time-accuracy graph. The results revealed that the proposed model has higher accuracy. Ã , the hypothesis of independence between two attributes is accepted; otherwise, it is rejected. a is the level of significance (here, a = 0.05), and r and c are the number of rows and columns, respectively. The dependence between two categorical attributes is high if the probability of independence becomes lower than 0.05. For delay, corridor, day, month, year, and
