Comparison of forecasting techniques in revenue management for a national railway in an emerging Asian economy

Abstract

In this paper, we make an attempt to compare various forecasting techniques to predict railway bookings for the final day of departure in the national railways of emerging Asian economy (NREAE). We use NREAE data of 2005-2008 for a particular railway route, apply time series [moving average, exponential smoothing and auto regressive integrative moving average, linear regression and revenue management techniques (additive, incremental and multiplicative pickup] to it and compare various methods. To make an efficient forecast over a booking horizon, we employ a weighted forecasting method (a blend of time series and revenue management forecasts) and find that it is successful in producing average mean absolute percentage error (MAPE) less than 10% for all fare classes across all days of the week except one class. The advantage of the model is that it produces efficient forecasts by attaching different weights across the booking period.