This is an archived article.

April 1, 2003

Airfare analyzer could save big bucks by advising when to buy tickets

It’s a classic dilemma for air travelers in today’s world of wildly varying ticket prices — should you purchase now if the rate seems reasonable, or wait for a better deal and take the risk that the price will go up?

Researchers at the University of Washington and the University of Southern California appear to have taken out some of the uncertainty with a new computer program that approaches a 90 percent score in saving money by predicting air fares.

“If you’re on an airplane and look around at your fellow passengers, probably every single person on that plane has paid a different price,” said Oren Etzioni, associate professor in the Department of Computer Science & Engineering in the UW’s College of Engineering. On one flight studied, the ticket price varied by more than $2,000, depending on when the purchase was made.

“It’s the same product, but people are paying radically different prices,” he said. “So ‘to buy or not to buy’ is the question — that’s why we named our program ‘Hamlet.’”

Initial testing indicates that Hamlet does a good job of edging things toward the consumer’s side of the equation.

During a 41-day pilot run, the algorithm saved 607 simulated passengers a total of $283,904 on airline fares by advising when to buy and when to postpone purchases. If one could predict the future and had perfect knowledge of how the ticket prices would vary, the greatest possible savings was $320,572. That means Hamlet’s savings were 88.6 percent of optimal.

Put another way, the algorithm saved an average of 27.1 percent per simulated passenger in cases where savings were possible. In a number of cases, savings were not possible because the virtual passenger tried to buy a ticket too close to the departure date.

Etzioni and his colleagues at the UW and USC have filed for a patent on the algorithm and the approach.

Airlines are famous for raising and lowering fares in an attempt to maximize revenue. It’s an issue that many service-oriented businesses face. If you have a standing inventory — passenger seats, hotel rooms, rental cars — you want to sell as much of that inventory as possible and may decide to do a promotion if some of it is standing empty. And airlines have become among the most sophisticated users of this so-called “dynamic pricing” to get the most out of their product, using algorithms that are closely guarded as trade secrets to vary pricing.

That leaves the traveler at a distinct disadvantage, Etzioni said.

“The airlines have a bunch of high-powered consultants who work with them to figure out the algorithm in the back room,” he said. “We’re looking at this from the consumer’s point of view. Can we help consumers save money?”

To answer that question, Etzioni had to design an algorithm that could accurately predict what the airlines’ algorithms would do, without having access to the information the airlines use to drive their pricing. The key, as it turns out, is the World Wide Web.

“That’s really what makes this possible,” Etzioni said. “All of the prices are available on the Web now.”

Hamlet uses a combination of techniques to find patterns in the variation of prices over time. But before the algorithm could work its predictive magic, the researchers had to gather the data from which Hamlet could “learn.”

For that, Etzioni teamed up with Craig Knoblock, a colleague with the Information Sciences Institute at the University of Southern California.

“They have all the machinery for gathering this kind of information on the Web,” Etzioni said.

Researchers built a data-mining program to collect pricing information directly from the Web, store it in a database and anticipate price changes. Data mining is the process of discovering patterns by sifting through large volumes of information. For the purposes of the study, the researchers restricted themselves to two non-stop flights — Los Angeles to Boston and Seattle to Washington, D.C. They looked at six airlines on those routes, gathering pricing data every three hours.

In examining how the fares behaved, they found that the price of particular flights changed as often as seven times a day. Over time, the range in prices could be extreme — for one flight on the LA to Boston route, for example, the high was $2,524 for a round trip ticket, compared to a brief low of $275. The high from Seattle to Washington, D.C., was $1,668 compared to a low of $281.

“Look how big those jumps are,” Etzioni said. “If you were going from Los Angeles to Boston and could buy in that window when the price dropped, you could save more than $2,000 on the ticket price. The potential savings are huge.”

While the airlines aren’t likely to welcome Hamlet, Etzioni believes their options for retaliation will be somewhat limited.

“We’re looking at prices that are available to people,” Etzioni said. “If the airlines start playing with the prices to trick us, they also have the potential of shooting themselves in the foot.” In other words, the prices are juggled to maximize profits, and if airlines manipulate them to obfuscate things for algorithms like Hamlet, they are also changing their chance of selling a ticket.

“I would never underestimate the cleverness of another party,” Etzioni added. “But their hands are somewhat tied.”

Also participating in the research were graduate students Alexander Yates from the UW and Rattapoom Tuchinda from USC.

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For more information, contact Etzioni at (206) 685-3035 or etzioni@cs.washington.edu. For a copy of a more detailed, scholarly paper on Hamlet, contact Etzioni or Rob Harrill at rharrill@u.washington.edu.