Railway passenger train delay prediction via neural network model

TLDR

Designing a neural network that performs well on a specific task remains a major challenge. This study proposes an artificial neural network to accurately predict passenger train delays in Iranian Railways. The model employs three input encoding schemes (normalized real numbers, binary coding, binary set encoding), evaluates three architecture strategies (quick, dynamic, multiple), and is trained and validated on a split of the delay dataset while its performance is compared to decision trees and multinomial logistic regression using training time, accuracy, network size, and a time‑accuracy graph. The proposed neural network achieved higher accuracy than the benchmark methods. © 2012 John Wiley & Sons, Ltd.

Abstract

SUMMARY 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. Copyright © 2012 John Wiley & Sons, Ltd.