Short-term Railway Passenger Demand Forecasting: Artificial Neural Networks Approaches

Forecasting passenger arrival is crucial for daily operations. In revenue management, predicting the number of passengers at departure offers essential information for seat allocation, overbooking, and pricing decisions. In recent years, Artificial Neural Networks have been successfully applied on solving time series forecasting problems. In this study, we show how to design ANN models to predict short-term railway passenger demand by using input information as effective as possible. The concept of divide-and-conquer is utilized in designing new structures in this study; three novel networks termed multiple temporal units neural network, parallel ensemble neural network and input recurrent neural network are proposed. Furthermore, six related issues are tested to show the predictive capability of individual models and their combinations. The book should shed some light on ANN network structures and also the benefit of combining models within ANN and between various methodologies; it should be useful for researchers and practitioners who are in the field of time series forecasting, ANN, revenue management and railway transportation.