To Buy or Not to Buy: Mining Airfare Data to Minimize Ticket Purchase

Retrieving and analyzing data from a flight data recorder after a typical flight is not new. Airlines often check a quick-access recorder that operates in parallel with the flight data recorder, examining certain parameters to improve operations and safety. But current tools are limited to looking for known issues, and the amount of data can be staggering. MIT professor John Hansman says the key is developing analysis tools that can effectively utilize all the information.

Commercial airlines in the United States are not required to implement a flight-data monitoring program. But the Federal Aviation Administration has a flight-operations quality-assurance program that includes guidelines airlines can follow on a voluntary basis.

Airlines typically monitor known parameters that have helped identify issues in the past. Things like engine thrust and aircraft speeds, as well as flight control positions such as elevator and rudder inputs, are among the things studied at the end of a day’s flying or when flight data is analyzed after a crash.

Professor John Hansman says that “it’s a classic data-mining problem.”

A group of researchers in University of Washington developed very interesting data mining technique to predict the most optimal prices of flight tickets. You can see the full version of the paper in the following link below. Here is an interesting part that I like to share with you.

“Corporations often use complex policies to vary product prices over time. The airline industry is one of the most sophisticated in its use of dynamic pricing strategies in an attempt to maximize its revenue. Airlines have many fare classes for seats on the same ight, use di_erent sales channels (e.g., travel agents, priceline.com, consolidators), and frequently vary the price per seat over time based on a slew of factors including seasonality, availability of seats, competitive moves by other airlines, and more. The airlines are said to use proprietary software to compute ticket prices on any given day, but the algorithms used are jealously guarded trade secrets.”

“Product prices become increasingly available on the World Wide Web, consumers have the opportunity to become more sophisticated shoppers. They are able to comparison shop especiently and to track prices over time; they can attempt to identify pricing patterns and rush or delay purchases based on anticipated price changes (e.g., I'll wait to buy because they always have a big sale in the spring...").”



1- To Buy or Not to Buy: Mining Airfare Data to Minimize Ticket Purchase Price, 
        Oren Etzioni , Dept. Computer Science    University of Washington, Seattle, Washington 98195            
        etzioni@cs.washington.edu.