research-article

Data-Driven Frequency-Based Airline Profit Maximization

Authors:

V. S. SubrahmanianAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 8, Issue 4

Article No.: 61, Pages 1 - 28

https://doi.org/10.1145/3041217

Published: 22 March 2017 Publication History

Abstract

Although numerous traditional models predict market share and demand along airline routes, the prediction of existing models is not precise enough, and to the best of our knowledge, there is no use of data mining--based forecasting techniques for improving airline profitability. We propose the maximizing airline profits (MAP) architecture designed to help airlines and make two key contributions in airline market share and route demand prediction and prediction-based airline profit optimization. Compared to past methods used to forecast market share and demand along airline routes, we introduce a novel ensemble forecasting (MAP-EF) approach considering two new classes of features: (i) features derived from clusters of similar routes and (ii) features based on equilibrium pricing. We show that MAP-EF achieves much better Pearson correlation coefficients (greater than 0.95 vs. 0.82 for market share, 0.98 vs. 0.77 for demand) and R²-values compared to three state-of-the-art works for forecasting market share and demand while showing much lower variance. Using the results of MAP-EF, we develop MAP--bilevel branch and bound (MAP-BBB) and MAP-greedy (MAP-G) algorithms to optimally allocate flight frequencies over multiple routes to maximize an airline’s profit. We also study two extensions of the profit maximization problem considering frequency constraints and long-term profits. Furthermore, we develop algorithms for computing Nash equilibrium frequencies when there are multiple strategic airlines. Experimental results show that airlines can increase profits by a significant margin. All experiments were conducted with data aggregated from four sources: the U.S. Bureau of Transportation Statistics (BTS), the U.S. Bureau of Economic Analysis (BEA), the National Transportation Safety Board (NTSB), and the U.S. Census Bureau (CB).

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Cited By

Hui BFang YXia TAykent SKu WWilliams BChen YNeville J(2023)Constrained market share maximization by signal-guided optimizationProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i4.25552(4330-4338)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1609/aaai.v37i4.25552
Jenčová EVajdová IMackulin J(2023)Possibilities of Using Predictive Analytics in the Aviation Industry - Case Study2023 New Trends in Aviation Development (NTAD)10.1109/NTAD61230.2023.10380137(123-127)Online publication date: 23-Nov-2023
https://doi.org/10.1109/NTAD61230.2023.10380137
Liang JLi LZheng JTan Z(2023)Service-oriented container slot allocation policy under stochastic demandTransportation Research Part B: Methodological10.1016/j.trb.2023.102799176(102799)Online publication date: Oct-2023
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Index Terms

Data-Driven Frequency-Based Airline Profit Maximization
1. Computing methodologies
2. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 8, Issue 4

Special Issue: Cyber Security and Regular Papers

July 2017

288 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3055535

Editor:
Yu Zheng
Microsoft Research, China

Issue’s Table of Contents

Copyright © 2017 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 March 2017

Accepted: 01 November 2016

Received: 01 September 2016

Published in TIST Volume 8, Issue 4

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National Research Foundation
Prime Minister's Office, Singapore
IDM Futures Funding Initiative

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Cited By

Hui BFang YXia TAykent SKu WWilliams BChen YNeville J(2023)Constrained market share maximization by signal-guided optimizationProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i4.25552(4330-4338)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1609/aaai.v37i4.25552
Jenčová EVajdová IMackulin J(2023)Possibilities of Using Predictive Analytics in the Aviation Industry - Case Study2023 New Trends in Aviation Development (NTAD)10.1109/NTAD61230.2023.10380137(123-127)Online publication date: 23-Nov-2023
https://doi.org/10.1109/NTAD61230.2023.10380137
Liang JLi LZheng JTan Z(2023)Service-oriented container slot allocation policy under stochastic demandTransportation Research Part B: Methodological10.1016/j.trb.2023.102799176(102799)Online publication date: Oct-2023
https://doi.org/10.1016/j.trb.2023.102799
Abdelghany AAbdelghany KAzadian F(2023)The airline seat capacity allocation problem: An expected marginal profit approachJournal of Air Transport Management10.1016/j.jairtraman.2023.102465112(102465)Online publication date: Sep-2023
https://doi.org/10.1016/j.jairtraman.2023.102465
Selvi ASinegalatha BTrinaya SVarshaa K(2023)Airline Ticket Price Forecasting Using Time Series ModelICT with Intelligent Applications10.1007/978-981-99-3758-5_20(215-226)Online publication date: 23-Sep-2023
https://doi.org/10.1007/978-981-99-3758-5_20
Hong SShin HChoi JPark NAl Hasan MXiong L(2022)Prediction-based One-shot Dynamic Parking PricingProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557421(748-757)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557421
Samunderu EFarrugia M(2022)Predicting customer purpose of travel in a low-cost travel environment—A Machine Learning ApproachMachine Learning with Applications10.1016/j.mlwa.2022.1003799(100379)Online publication date: Sep-2022
https://doi.org/10.1016/j.mlwa.2022.100379
Shi FHuang BWu CJin L(2021)How Is Gross Profit Margin Overestimated in China?Journal of Mathematics10.1155/2021/39240622021(1-13)Online publication date: 14-Dec-2021
https://doi.org/10.1155/2021/3924062
Li DLiu JJeon JHong SLe TLee DPark NZhu FChin Ooi BMiao CWang HSkrypnyk IHsu WChawla S(2021)Large-Scale Data-Driven Airline Market Influence MaximizationProceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining10.1145/3447548.3467423(914-924)Online publication date: 14-Aug-2021
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K Tissera Pllwana AWaduge KPerera MNawinna DKasthurirathna D(2020)Predictive Analytics Platform for Airline Industry2020 2nd International Conference on Advancements in Computing (ICAC)10.1109/ICAC51239.2020.9357244(108-113)Online publication date: 10-Dec-2020
https://doi.org/10.1109/ICAC51239.2020.9357244

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