Developing forecasting models for estimating the number of passengers on a high speed train line

Correct and reliable order forecasting has an important role in increasing the efficiency of companies. Similar to other sectors, the demand forecasts are very important in Food industry. Especially in one day shelf life products, it is a must to make accurate order forecast and prevent food waste excess. In this study an approach is developed for forecasting the passenger numbers in the the high speed train leg between Ankara and İstanbuls. The firms is a subcontractor to The Republic of Turkey State Railways. As a first step, it was aimed to create a daily order system and the forecasting with multiple regression of the passenger numbers who traveled Ankara-İstanbul and İstanbul-Ankara route in 'Business Plus' and 'Economy Plus' coaches using data from a catering company between 2016-2019 year. The accurate Passenger number estimation will create a better meal order and will lead into a financial savings. In our study several forecasting models are constructed based on different ways of data groping and the choices of independent regression variables. We selected the best three models among the models develop and we found the mean absolute percent error performance for selected models, which turn out to be quite accurate. In the second stage our study, using the same passenger data the Winters exponential smoothing method is studied to forecast the weekly meal consumption and the forecast accuracy is tested using MAPE values. Key Words: Demand forecast, Multiple linear regression method, Passenger numbers forecast