This study analyzes the use of neural network to produce accurate forecasts of total bookings and cancellations before departure, of a major rail operator. The model used 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 generalization 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 result is an accurate forecast of the number of passengers with a prediction error around 8%.
Published November 2009 , 19 pages