Forecast accuracy along booking profile in the national railways of an emerging Asian economy: comparison of different techniques

Abstract

The National Railways of an Emerging Asian Economy (NREAE), the second largest railway network in the world, is facing growing challenges from low fare airlines. To combat these challenges, NREAE has to adopt revenue management systems where efficient forecasting plays a crucial role. In this paper, we make an attempt to compare various forecasting techniques to predict railway bookings for the final day of departure. 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 (ARIMA), 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.