It’s rare to find a company with a business idea based purely on prediction, but FlightCaster is such a company. FlightCaster launched less than two weeks ago (August 14) and promises to predict flight delays with high accuracy 6 hours before the flight.
The core of FlightCaster’s service is a patent-pending algorithm that pulls in data regarding weather forecasts, in-bound aircraft tracking, flight history, and other things from various sources. The algorithm actually assigns a probability to a certain flight being delayed, so it can tell you if, for instance, there is an 80% chance of a delay. This can be useful to know, since airlines usually do not warn about delays unless they are 100% sure that a delay will occur.
FlightCaster recently demonstrated the application in front of venture capitalists, and correctly predicted that a flight to New York would be delayed, even though it was reported by the airline to be on time at the moment the prediction was made.
The application is available for BlackBerry and iPhone (for 10 USD) and on the web (for free). You can test a prediction for a random flight here. (At the moment, it only works for US flights – an international version would obviously be a killer app, but it would presumably be much more difficult to pull in the needed data.) One of the nice things about this service is that it tells you the factors it used to make the prediction.
Update: An interesting interview with Bradford Cross from FlightCaster here. It seems their application is built on Hadoop and Amazon EC2 using Rails and Clojure. Peter Skomoroch, who did the interview, does a good job of explaining what I failed to put into my blog post:
FlightCaster strikes me as a great example of the next generation of web applications that will leverage [raw data that has been collected by the government and industry but sits untapped in large data warehouses]: bootstrapped startups that apply machine learning and data processing at scale to solve a focused problem people actually care about.