While solutions relying solely on ticketing or booking data will get you halfway, ForwardKey’s TAM gives you the complete picture — past, present, and future. It’s our core dataset, with full global coverage, exceptional accuracy, and weekly updates — providing the only comprehensive, directionally segmented view of global air traffic estimation on the market.
TAM analyses more data than any other algorithm in the industry
Accessing accurate global air travel data is challenging
Fewer than 25% of airports and airlines share official figures. There’s no single standard data file on global air traffic. Official sources may be outdated by up to one year, may contain only partial data, and may not reflect actual global traffic.
TAM addresses these challenges head on
TAM combines big data, heuristic rules, neural networks and Machine Learning to compute and continuously refine estimates, directional segmentations and forecasts.
It provides complete global air traffic monitoring combining both historic and forecast traffic — including both full-service and low-cost carriers — recreating missing tickets not shared by airlines.
With unparalleled coverage and accuracy, it can be segmented down to the passenger level and allows for unlimited analysis of segmented and directional global air traffic.
TAM’s granularity and segments analysis
TAM powers all ForwardKeys data solutions
For Data Professionals
An extensive library of prebuilt Data Smarts that allow you to focus on generating actionable insights — not wrestling with the raw data.
For experts from Travel Retail, Media and Brands
An easy-to-use analysis tool allowing you to understand key passenger and nationality metrics for major global airports — at a glance.
For DMOs & Destinations
Comprehensive in-view analysis of our data through user-friendly dashboards, providing valuable insights for all members within your organisation.
Full global coverage
Cross-projection model to validate data from official statistics and publications.
Complex bidirectional collation and analysis, including connectivity and contributing network figures.
Reverse adjustments when figures from a specific data source are inaccessible.
Continuous learning neural network to model performance figures for underreported segments.
How it works
Brands already using TAM include
