What SafeLand is
SafeLand leverages the availability of real-time data from landing aircraft to measure and report runway surface conditions.
Analyzing data from a broad network of nearly 1,500 connected aircraft, SafeLand generates landing reports for all runways where connected aircraft land and identifies when friction limited braking occurs—i.e., when an aircraft experiences or utilizes the full amount of friction available from the tire / runway surface interface. Once the friction limit of a runway has been reached, the pilot can increase brake pressure to any level, but the frictional force will not increase.
When a connected aircraft encounters friction limited braking, SafeLand uses the experienced friction coefficient at the friction limit to assign the appropriate RwyCC and braking action description, as per FAA AC 25-32, which defines the range of wheel braking coefficients corresponding to each RCAM category. Conditions are reported using standard TALPA nomenclature: 6—Dry, 5—Good, 4—Good to Medium, 3—Medium, 2—Medium to Poor, 1—Poor, and 0—Nil. All reports are de-identified and provide only the specific airport, runway, landing time, meteorological conditions, and experienced friction; consistent with FOQA standard practices, no data from airplane landing files, flight numbers, tail numbers, or airplane types are reported.
Users can access this information via SafeLand reports or through their legacy systems. The latter is accomplished by integrating the data using AST’s Applied Program Interface.Reports are available in multiple formats and are color coded for quick and easy interpretation. Users can select airports of primary interest (high traffic, feeder, diversion, etc.) to customize their view of recent landings and fit their situational awareness and decision making needs.
Users also have the option to set up customizable alerts to have system notify them when a runway condition code below a certain threshold is reported. Users can set the relevant threshold, select airports of interest for alert generation purposes, and specify e-mail, SMS, or both as the delivery mechanism.
The computational model used in SafeLand has been tested in operational and scientific contexts over the past twelve years. It has been used to aid accident and incident investigations. The model has also been used in aircraft testing in the US, Japan, Germany, and Canada. It has been tested in scientific research within the Joint Winter Runway Friction Program (sponsored by NASA, FAA, JAA, Transport Canada and other research organizations). In 2011, a prototyping exercise with commercial carriers, involving several different airplane types, further enhanced the reliability and accuracy of the SafeLand model. The reporting system has been successfully tested on several models of modern aircraft.
Benefits & advantages over current methods
Most significantly, SafeLand provides dependable, objectively-measured runway information. Friction measurements obtained from CFME and decelerometers are not reliably related to the friction experienced by an aircraft tire, as friction results are significantly affected by scale (e.g., by weight, tire dimensions, speed, etc.). SafeLand solves this problem by using aircraft as the friction-measuring device.
In addition, SafeLand calculates experienced runway friction in a standardized way, taking into account a host of aircraft-specific parameters and other factors that affect braking action (e.g., tire design, tire pressure, flap positions, engine thrust forces, cross winds, residual lift, ambient temperature). Thus, unlike other means of measuring runway friction, SafeLand accounts for variations and delivers a normalized runway condition report—one that is independent of aircraft type, tire variations, runway surface material, etc.
By providing a steady flow of operator-independent runway assessments, SafeLand should also increase runway uptime and improve safety. During periods of high activity, SafeLand reports will occur within minutes of each other, allowing an airport to monitor runway conditions without needing to close runways for exploratory contamination or friction measurement tests. This data pattern should also allow airport operators to easily see conditions deteriorating and proactively maintain runway surfaces, leading to safer operating conditions, improved flow control, shorter runway downtime, and better schedule reliability.