research-article

On Optimizing Airline Ticket Purchase Timing

Authors:

William Groves,

Maria GiniAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 7, Issue 1

Article No.: 3, Pages 1 - 28

https://doi.org/10.1145/2733384

Published: 01 October 2015 Publication History

Abstract

Proper timing of the purchase of airline tickets is difficult even when historical ticket prices and some domain knowledge are available. To address this problem, we introduce an algorithm that optimizes purchase timing on behalf of customers and provides performance estimates of its computed action policy. Given a desired flight route and travel date, the algorithm uses machine-learning methods on recent ticket price quotes from many competing airlines to predict the future expected minimum price of all available flights. The main novelty of our algorithm lies in using a systematic feature-selection technique, which captures time dependencies in the data by using time-delayed features, and reduces the number of features by imposing a class hierarchy among the raw features and pruning the features based on in-situ performance. Our algorithm achieves much closer to the optimal purchase policy than other existing decision theoretic approaches for this domain, and meets or exceeds the performance of existing feature-selection methods from the literature. Applications of our feature-selection process to other domains are also discussed.

References

[1]

Rakesh Agrawal, Samuel Ieong, and Raja Velu. 2011. Timing when to buy. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management. ACM, New York, NY, 709--718.

Digital Library

[2]

Enrico Bachis and Claudio A. Piga. 2011. Low-cost airlines and online price dispersion. International Journal of Industrial Organization 29, 6, 655--667.

[3]

Patrick Bajari and Ali Hortacsu. 2003. The winner’s curse, reserve prices, and endogenous entry: empirical insights from eBay auctions. RAND Journal of Economics 34, 2, 329--355.

[4]

Peter P. Belobaba. 1987. Airline yield management. An overview of seat inventory control. Transportation Science 21, 2, 63--73.

Digital Library

[5]

George E. P. Box, Gwilym M. Jenkins, and Gregory C. Reinsel. 2013. Time Series Analysis: Forecasting and Control. John Wiley & Sons, Hoboken, NJ.

[6]

Sijmen de Jong. 1993. SIMPLS: An alternative approach to partial least squares regression. Chemometrics and Intelligent Laboratory Systems 18, 3, 251--263.

[7]

Ke-Lin Du and M. N. S. Swamy. 2014. Recurrent neural networks. In Neural Networks and Statistical Learning. Springer, London, 337--353.

[8]

Wedad Elmaghraby and Pinar Keskinocak. 2003. Dynamic pricing in the presence of inventory considerations: Research overview, current practices, and future directions. Management Science 49, 10, 1287--1309.

Digital Library

[9]

Oren Etzioni, Rattapoom Tuchinda, Craig Knoblock, and Alexander Yates. 2003. To buy or not to buy: Mining airfare data to minimize ticket purchase price. In SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, 119--128.

Digital Library

[10]

Graham Francis, Ian Humphreys, Stephen Ison, and Michelle Aicken. 2006. Where next for low cost airlines? A spatial and temporal comparative study. Journal of Transport Geography 14, 2, 83--94.

[11]

William Groves. 2013. Using domain knowledge to systematically guide feature selection. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI). AAAI Press, Palo Alto, CA, 3215--3216.

Digital Library

[12]

William Groves and Maria Gini. 2013a. Improving prediction in TAC SCM by integrating multivariate and temporal aspects via PLS regression. In Agent-Mediated Electronic Commerce. Designing Trading Strategies and Mechanisms for Electronic Markets. Lecture Notes in Business Information Processing, Vol. 119. Springer, Berlin, 28--43.

[13]

William Groves and Maria Gini. 2013b. Optimal airline ticket purchasing using automated user-guided feature selection. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI). AAAI Press, Palo Alto, CA, 150--156.

Digital Library

[14]

Isabelle Guyon and André Elisseeff. 2003. An introduction to variable and feature selection. The Journal of Machine Learning Research 3, 1157--1182.

Digital Library

[15]

Mark A. Hall. 1999. Correlation-Based Feature Selection for Machine Learning. Ph.D. Dissertation. The University of Waikato, Waikato, New Zealand.

[16]

Mark A. Hall. 2000. Correlation-based feature selection for discrete and numeric class machine learning. In International Conference on Machine Learning (ICML). Morgan Kaufmann, San Francisco, CA, 359--366.

Digital Library

[17]

Trevor Hastie, Robert Tibshirani, and Jerome Friedman. 2001. The Elements of Statistical Learning. Springer, New York.

[18]

Arthur E. Hoerl and Robert W. Kennard. 2000. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 42, 1, 80--86.

Digital Library

[19]

Ian T. Jolliffe. 1982. A note on the use of principal components in regression. Journal of the Royal Statistical Society (Applied Statistics) 31, 3, 300--303.

[20]

Ron Kohavi and George H. John. 1997. Wrappers for feature subset selection. Artificial Intelligence 97, 1--2, 273--324.

Digital Library

[21]

Yuri Levin, Jeff McGill, and Mikhail Nediak. 2009. Dynamic pricing in the presence of strategic consumers and oligopolistic competition. Management Science 55, 1, 32--46.

Digital Library

[22]

David Lucking-Reiley, Doug Bryan, Naghi Prasad, and Daniel Reeves. 2007. Pennies from Ebay: The determinants of price in online auctions. The Journal of Industrial Economics 55, 2, 223--233.

[23]

Benny Mantin and David Gillen. 2011. The hidden information content of price movements. European Journal of Operational Research 211, 2, 385--393.

[24]

Benny Mantin and Bonwoo Koo. 2010. Weekend effect in airfare pricing. Journal of Air Transport Management 16, 1, 48--50.

[25]

Harold Martens and Tormod Næs. 1992. Multivariate Calibration. John Wiley & Sons, Hoboken, NJ.

[26]

Luis Carlos Molina, Lluís Belanche, and Àngela Nebot. 2002. Feature selection algorithms: a survey and experimental evaluation. In IEEE International Conference on Data Mining (ICDM). IEEE, Piscataway, NJ, 306--313.

Digital Library

[27]

K. Obeng and R. Sakano. 2012. Airline fare and seat management strategies with demand dependency. Journal of Air Transport Management 24, 42--48.

[28]

Claudio A. Piga and Nicola Filippi. 2002. Booking and flying with low-cost airlines. International Journal of Tourism Research 4, 3, 237--249.

[29]

Steven L. Puller and Lisa M. Taylor. 2012. Price discrimination by day-of-week of purchase: Evidence from the U.S. airline industry. Journal of Economic Behavior & Organization 84, 3, 801--812.

[30]

J. R. Quinlan and R. M. Cameron-Jones. 1995. Oversearching and layered search in empirical learning. In Proceedings of the 14th International Joint Conference on Artificial Intelligence. Morgan Kaufmann, San Francisco, CA, 1019--1024.

Digital Library

[31]

Ilya Raykhel and Dan Ventura. 2009. Real-time automatic price prediction for eBay online trading. In Proceedings of the Innovative Applications of Artificial Intelligence Conference. AAAI, Palo Alto, CA, 135--140.

[32]

Stephen A. Rhoades. 1993. Herfindahl-Hirschman index, the. Federal Reserve Bulletin 79, 188--189.

[33]

Tim Sauer. 1994. Time series prediction by using delay coordinate embedding. In Time Series Prediction: Forecasting the Future and Understanding the Past, Andreas S. Weigend and Neil A. Gershenfeld (Eds.). Addison Wesley, Boston, MA, 175--194.

[34]

Bernhard Schölkopf, Alex J. Smola, Robert C. Williamson, and Peter L. Bartlett. 2000. New support vector algorithms. Neural Computation 12, 5, 1207--1245.

Digital Library

[35]

Barry C. Smith, John F. Leimkuhler, and Ross M. Darrow. 1992. Yield management at American Airlines. Interfaces 22, 1, 8--31.

Digital Library

[36]

Janakiram Subramanian, Shaler Stidham Jr., and Conrad J. Lautenbacher. 1999. Airline yield management with overbooking, cancellations, and no-shows. Transportation Science 33, 2, 147--167.

Digital Library

[37]

U.S. Department of Transportation. 2012. Origin-Destination Survey. Bureau of Transportation Services.

[38]

Timothy M. Vowles. 2000. The effect of low fare air carriers on airfares in the US. Journal of Transport Geography 8, 2, 121--128.

[39]

Eric A. Wan. 1994. Time series prediction by using a connectionist network with internal delay lines. In Time Series Prediction: Forecasting the Future and Understanding the Past, Andreas S. Weigend and Neil A. Gershenfeld (Eds.). Addison Wesley, Boston, MA, 195--218.

[40]

Ian H. Witten and Eibe Frank. 2005. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco, CA.

Digital Library

[41]

Svante Wold, Harold Martens, and H. Wold. 1983. Multivariate calibration problem in chemistry solved by the PLS method. Matrix Pencils 973, 18, 286--293.

[42]

Zheng Alan Zhao and Huan Liu. 2011. Spectral Feature Selection for Data Mining. Chapman & Hall/CRC, London, UK.

Digital Library

[43]

Yun Zhou, Norman Fenton, and Martin Neil. 2014. Bayesian network approach to multinomial parameter learning using data and expert judgments. International Journal of Approximate Reasoning 55, 5, 1252--1268.

Cited By

Singh KYadav MHacimahmud Abdullayev V(2024)Prediction of Flight Areas using Machine Learning AlgorithmLatIA10.62486/latia2024932(93)Online publication date: 21-Aug-2024
https://doi.org/10.62486/latia202493
Chebaane AKhelil ASalem MMabrouk B(2024)Effective Timing for Reserving Fog Computational Resources for Time-Sensitive Vehicular Applications2024 9th International Conference on Fog and Mobile Edge Computing (FMEC)10.1109/FMEC62297.2024.10710306(14-21)Online publication date: 2-Sep-2024
https://doi.org/10.1109/FMEC62297.2024.10710306
Malkawi MAlhajj R(2023)Real-time web-based International Flight Tickets Recommendation System via Apache Spark2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)10.1109/IRI58017.2023.00055(279-282)Online publication date: Aug-2023
https://doi.org/10.1109/IRI58017.2023.00055
Show More Cited By

Index Terms

On Optimizing Airline Ticket Purchase Timing
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Intelligent agents

Recommendations

An agent for optimizing airline ticket purchasing
AAMAS '13: Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems

Buying airline tickets is an ubiquitous task in which it is difficult for humans to minimize cost due to insufficient information. Even with historical data available for inspection (a recent addition to some travel reservation websites), it is ...
DAliM: Machine Learning Based Intelligent Lucky Money Determination for Large-Scale E-Commerce Businesses
Service-Oriented Computing
Abstract
E-commerce businesses compete in the market by conducting marketing strategies consisting of four aspects: customers, products, marketplaces and intermediaries. One of the widely-used marketing strategies, called Lucky Money, is capable of ...
Online Footsteps to Purchase: Exploring Consumer Behaviors on Online Shopping Sites
WebSci '15: Proceedings of the ACM Web Science Conference

As an important part of the Internet economy, online markets have gained much interest in research community as well as industry. Researchers have studied various aspects of online markets including motivations of consumer behaviors on online markets. ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 7, Issue 1

October 2015

293 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2830012

Editor:
Yu Zheng
Microsoft Research, China

Issue’s Table of Contents

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 October 2015

Accepted: 01 February 2015

Revised: 01 December 2014

Received: 01 May 2014

Published in TIST Volume 7, Issue 1

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

17
Total Citations
View Citations
920
Total Downloads

Downloads (Last 12 months)50
Downloads (Last 6 weeks)3

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Singh KYadav MHacimahmud Abdullayev V(2024)Prediction of Flight Areas using Machine Learning AlgorithmLatIA10.62486/latia2024932(93)Online publication date: 21-Aug-2024
https://doi.org/10.62486/latia202493
Chebaane AKhelil ASalem MMabrouk B(2024)Effective Timing for Reserving Fog Computational Resources for Time-Sensitive Vehicular Applications2024 9th International Conference on Fog and Mobile Edge Computing (FMEC)10.1109/FMEC62297.2024.10710306(14-21)Online publication date: 2-Sep-2024
https://doi.org/10.1109/FMEC62297.2024.10710306
Malkawi MAlhajj R(2023)Real-time web-based International Flight Tickets Recommendation System via Apache Spark2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)10.1109/IRI58017.2023.00055(279-282)Online publication date: Aug-2023
https://doi.org/10.1109/IRI58017.2023.00055
Gabor MKardos MOltean F(2022)Yield Management—A Sustainable Tool for Airline E-Commerce: Dynamic Comparative Analysis of E-Ticket Prices for Romanian Full-Service Airline vs. Low-Cost CarriersSustainability10.3390/su14221515014:22(15150)Online publication date: 15-Nov-2022
https://doi.org/10.3390/su142215150
Alapati NPrasad BSharma AKumari GVeeneetha SSrivalli NUdaya Lakshmi TSahitya D(2022)Prediction of Flight-fare using machine learning2022 International Conference on Fourth Industrial Revolution Based Technology and Practices (ICFIRTP)10.1109/ICFIRTP56122.2022.10059429(134-138)Online publication date: 23-Nov-2022
https://doi.org/10.1109/ICFIRTP56122.2022.10059429
Gounder MKumar RKumar K(2022)The Practicality of Machine Learning for Airline Forward Sales Forecast2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)10.1109/CSDE56538.2022.10089361(1-8)Online publication date: 18-Dec-2022
https://doi.org/10.1109/CSDE56538.2022.10089361
Azzolina SRazza MSartiano KWeitschek E(2021)Price Discrimination in the Online Airline Market: An Empirical StudyJournal of Theoretical and Applied Electronic Commerce Research10.3390/jtaer1606012616:6(2282-2303)Online publication date: 9-Sep-2021
https://doi.org/10.3390/jtaer16060126
Tian HPresa-Reyes MTao YWang TPouyanfar SMiguel ALuis SShyu MChen SIyengar S(2021)Data Analytics for Air Travel Data: A Survey and New PerspectivesACM Computing Surveys10.1145/346902854:8(1-35)Online publication date: 4-Oct-2021
https://dl.acm.org/doi/10.1145/3469028
Cao JXu Y(2021)To Wait or To Buy: A Recommendation Service for Airline Ticket Purchase Timing2021 IEEE International Conference on Web Services (ICWS)10.1109/ICWS53863.2021.00031(155-160)Online publication date: Sep-2021
https://doi.org/10.1109/ICWS53863.2021.00031
Chi QLiu MYang B(2020)Prediction and Analysis of Air Ticket Based on ARIMA ModelEmerging Trends in Intelligent and Interactive Systems and Applications10.1007/978-3-030-63784-2_17(128-135)Online publication date: 18-Dec-2020
https://doi.org/10.1007/978-3-030-63784-2_17
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Issue’s Table of Contents