Railway demand forecasting in revenue management using neural networks
This study analyses the use of neural networks to produce accurate forecasts of total bookings and cancellations before departure, of a major European rail operator. Effective forecasting models, can improve revenue performance of transportation companies significantly. The prediction model used in this research is an improved multi-layer perceptron (MLP) describing the relationship between number of passengers and factors affecting this quantity based on historical data. Relevant pre-processing approaches have been employed to make learning more efficient. The generalisation of the network is tested to evaluate the accuracy prediction of the regression model for future trends of reservations and cancellations using actual railroad data. The results show that it is a promising approach in railway demand forecasting with a low prediction error.
Record created on 2015-04-15, modified on 2017-02-16