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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM63439

PREDICTING AIRLINE OVERBOOKING USING BIGDATA

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM63439

Volume:

10 |

5 |

May 2026

Pages:

1-4

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

SNEHA PRAKASH

SNEHA PRAKASH

MASTER OF COMPUTER APPLICATIONS

BAPUJI INSTITUTE OF ENGINEERING AND TECHNOLOGY

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Mentors

SKT

SIDDESH K T

MASTER OF COMPUTER APPLICATIONS

BAPUJI INSTITUTE OF ENGINEERING AND TECHNOLOGY

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Abstract

Overbooking is a revenue management strategy used in the airline industry to maximize seat utilization. Airlines sell more tickets than available seats based on expected passenger cancellations and no-shows. However, inaccurate predictions may lead to denied boarding, financial compensation costs, and customer dissatisfaction. This project proposes a Big Data and Machine Learning-based framework to predict passenger no-show probabilities. By analyzing booking history, seasonal demand, passenger behavior, and external factors, the system recommends optimal overbooking levels. The framework improves revenue generation, reduces operational risk, and enhances customer satisfaction.

KEYWORDS

Keywords: Airline Overbooking Big Data Analytics Machine Learning No-Show Prediction Revenue Optimization.

Cite This Article

SNEHA PRAKASH, SIDDESH K T, "PREDICTING AIRLINE OVERBOOKING USING BIGDATA," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-4, May 2026.

Full Article/Conclusion

The project successfully demonstrates that Big Data and Machine Learning techniques can significantly improve airline overbooking strategies. By accurately predicting passenger no-show behavior, the system enhances revenue optimization while minimizing customer inconvenience. The scalable architecture ensures adaptability across airlines of different sizes.

References

  Belobaba P. (2009). Revenue Management for Airlines. Boyd S. & Vandenberghe L. (2015). Data-Driven Optimization in Airline Overbooking Models. Ivanov S. & Webster C. (2020). Predicting Passenger No-Show Behaviour Using Machine Learning. Apache Software Foundation. Hadoop Documentation. TensorFlow Official Documentation.

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Publication Date

5/18/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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0

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2

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5

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Conflict of Interest

The authors declare no conflict of interest related to this research work.

Funding Information

No funding information available for this research.

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