Data transparency

Where FlyBuffer gets its airport data

FlyBuffer combines several public sources because no single dataset answers the whole departure-planning question. Stable airport facts, current operations, weather and broad passenger pressure support different parts of the plan.

SourceUsed forTypical refreshDoes not tell us
OurAirportsAirport identity, coordinates and runway contextWeeklyLive operations or TSA queues
FAA NAS StatusClosures, ground stops and airport disruptionAbout every 15 minutesCheckpoint queue length
National Weather ServiceHourly weather contextHourlyAirline-specific delay or TSA wait
Aviation Weather CenterMETAR / TAF aviation weatherCurrent feedTSA queue length
TSA passenger volumeBroad national passenger-pressure contextDailyOne airport checkpoint queue
Traveler inputJourney time and optional current security estimateWhenever you update itAnything you did not enter

Airport and runway data

Airport identity, coordinates, location and runway facts provide structural context for airport guides and maps. These facts change slowly and should not be interpreted as live operating conditions.

OurAirports data →

FAA airport operations

FAA status information can indicate closures, ground stops and other airport-level disruptions. That helps describe the operating environment but does not tell FlyBuffer how long a TSA checkpoint queue is.

FAA NAS Status →

National Weather Service

Hourly forecasts add weather context to the departure day. Weather can make ground travel or airport operations less predictable, but the forecast is not converted directly into a claimed TSA wait.

National Weather Service API documentation →

Aviation Weather Center

METAR observations and TAF forecasts provide aviation-specific weather context. They support the airport operating picture and remain separate from passenger security estimates.

Aviation Weather Center data API →

TSA national passenger volume

National passenger-throughput figures provide a broad travel-pressure signal. They cannot reveal the queue at one individual checkpoint, so FlyBuffer uses them only as one contextual input.

TSA passenger volumes →

Traveler-controlled inputs

Ground travel is an explicit traveler input. If your map or navigation app shows a different journey time, update FlyBuffer. The same applies when you have a better current checkpoint estimate.

Freshness labels

Different sources age at different rates. Structural airport facts can remain useful for days or weeks, while an operating-status signal becomes stale much faster.

What happens when a source fails

FlyBuffer validates data before publication. A failed update should not replace the last valid dataset with an empty or corrupt file.

What public data cannot tell you

No public combination used here reveals every checkpoint lane, every parking delay, every airline desk condition or your exact ground journey. That is why the planner exposes uncertainty and allows user-supplied inputs.

Why national TSA volume is not an airport wait time

The national passenger-volume series is useful for understanding whether the entire U.S. system is experiencing a heavier or lighter travel period. It is not broken down into the queue at one checkpoint, so FlyBuffer uses it only as broad context and never presents it as an airport-specific observation.

Why weather is context rather than a security proxy

Bad weather can change road travel and airport operations, but there is no defensible rule that a particular forecast means a particular TSA wait. Weather is therefore shown as its own signal. Any effect on the departure plan should come from the part of the journey the weather actually changes.

How source freshness should be read

A structural airport fact can remain valid long after a current operations signal has become stale. FlyBuffer should not use one universal freshness threshold for every dataset. The important question is whether the value is still fresh enough for the claim being made on the page.

Why the last valid dataset is retained

An unavailable source is not evidence that the underlying real-world value became zero. Retaining the last validated dataset prevents a transient fetch failure from publishing empty airports, zero-minute waits or missing operational context. Older data can then be labeled and treated with appropriate caution.

Corrections and transparency

Airport facts and source behavior can change. When a source changes its definitions or an airport updates a structural fact, the mapping should be corrected before the new value is used in planning content.