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An artificial neural network approach to forecast airline load factors
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Industrial Engineering & Management

ISSN: 2169-0316

Open Access

An artificial neural network approach to forecast airline load factors


2nd International Conference and Exhibition on Industrial Engineering

November 16-18, 2015 Dubai, UAE

Emir Ali Goze and Alev Taskin Gumus

Yildiz Technical University, Turkey

Posters-Accepted Abstracts: Ind Eng Manage

Abstract :

Load factor (LF) is a measure of capaticy utilization and has been considered as one of the most commonly used and important performance indicators for passenger airlines. It represents the proportion of the utilized capacity of a flight or the whole airline in general. LF is calculated by dividing the number of paying passengers by the total number of seats for a single flight. Airlines have flights with different distances and having a 90% LF on a long haul flight and 90% on a short haul flight doesn├ó┬?┬?t have the same impact for the airline. Therefore the capacity utilization of an airline is better represented by an average LF calculation that takes individual flight distances into consideration. Hence, average LF is calculated by dividing Revenue Passenger Kilometers (RPK) by Available Seat Kilometers (ASK). Airlines use LF estimates as a means of planning their actions. For example, if a route├ó┬?┬?s expected LF is significantly lower for a period of time compared to last year`s same period, then pricing team may consider publishing promotional fares, whereas sales teams may deciede to incrase agency incentives and marketing may decide to make advertisement campaings in order to stimulate demand. Therefore having accurate LF forecasts is of extreme importance for airlines. In this study, an artificial neural network (ANN) approach is presented to forecast LFs and its performace is compared to airline├ó┬?┬?s current forecasting methodology. It is shown that the proposed ANN approach produce significantly better forecasts compared to the current methodology.

Biography :

Emir Ali Goze received his BSc and MSc degree with honors in Industrial Engineering from Yildiz Technical University, Turkey in 2006 and 2008 respectively. He also holds an MSc in International Production Management from Hamburg University of Technology, Germany. He has been working in the field of Revenue Management and is a PhD candidate in Industrial Engineering with a focus on Airline Revenue Management.

Email: emirali@gmail.com

Google Scholar citation report
Citations: 590

Industrial Engineering & Management received 590 citations as per Google Scholar report

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