OGC Testbed-14: Semantically Enabled Aviation Data Models Engineering Report

The Flight Information Exchange Model (FIXM) is an exchange model capturing Flight and Flow information that is globally standardized. It captures data related to flights throughout their lifecycle from pre-flight planning through departure, en-route, and arrival segments, including data on aircraft location, aircraft characteristics, and cargo contents. The model is decomposed in modular packages ( Figure 1) encoded in XML Schema.

The AIXM model describes airspace routes, procedures, boundaries, fixes, navaids, and airport surface elements (runways, gates, etc.). The objective of the Aeronautical Information Exchange Model (AIXM) is to enable the provision in digital format of the aeronautical information that is in the scope of Aeronautical Information Services (AIS). The AIS information/data flows are increasingly complex and made up of interconnected systems. They involve many actors including multiple suppliers and consumers. There is also a growing need in the global Air Traffic Management (ATM) system for high data quality and for cost efficiency.

The WXXM is designed to enable the management and distribution of weather data in digital format (XML). WXXM version 2.0 is based on Geography Markup Language (GML) and is one of the GML Application Schemas. It has been developed by the FAA and the European Organization for the Safety of Air Navigation (EUROCONTROL). WXXM is a member of a family of data models designed for use in aviation safety, notably AIXM and FIXM.

WXXM models meteorological observations, forecasts, and measurement procedures for weather features and phenomena with spatial and temporal extent. Independent from governmental standardization efforts, the airline industry has been developing Airline Industry Data Model (AIDM).

The National Aeronautics and Space Administration (NASA) has developed a prototype data integration system that demonstrates how ontologies can be used to integrate, query, and search over various sources of heterogeneous air traffic management (ATM) data, including data from FAA, National Oceanic and Atmospheric Administration (NOAA), NASA, and other providers. In this prototype, a common ontology is used to bridge multiple types of aviation data models and enable cross-dataset querying. In general, cross-dataset querying is very challenging due to differing data formats, nomenclature, and organizational structure. Data can only be combined from multiple sources by expending significant effort to write customized code that integrates selected data on an as-needed, piecemeal basis. To address this problem, NASA is building an integrated data source that obviates this need to write special- purpose, one-off integration code.

As part of this approach, an overarching data model (the NASA ATM Ontology ^[1]) has been designed and implemented to serve as a backbone upon which to overlay data from multiple sources. The ontology data model is scoped sufficiently broadly to interconnect data from several different aviation realms, including flight, traffic management, aeronautical information, weather, and carrier operations. The ontology is currently populated with instance data corresponding to over 100K flights arriving and departing the three major airports in the New York Metroplex (KEWR, KJFK, KLGA) during July 2014. This data is encoded in approximately 250M triple statements and incorporates the following sources: flight track data from FAA’s ASDI (Aircraft Situation Display to Industry) data feed; airport weather from the NOAA’s METeorological Aerodrome Reports (METAR) and Terminal Aerodrome Forecasts (TAF) data feeds; information on traffic management initiatives from FAA’s Command Center website; historical traffic counts and delay statistics from the Department of Transportation (DOT)’s Aviation System Performance Metrics (ASPM)database; infrastructure data on US routes, fixes, airways, and sectors used by FAA’s En Route Automation Modernization (ERAM) system; aircraft information from FAA’s aircraft registry and from the Commercial Aviation Safety Team (CAST)/International Civil Aviation Organization (ICAO) common aircraft taxonomy; and web-based information on airlines, airport terminals and gates. Of the 250M triples, about 99.5% represent specific flight, weather, or traffic management data. These are dominated overwhelmingly by triples capturing flight track parameters from ASDI – such as altitude, location, and groundspeed – captured at the rate of one per minute of flight. The remaining 0.5% of triples capture mostly static aeronautical infrastructure information (e.g., data on airspace routes, sectors, and fixes, NAS facilities, airports, aircraft makes and models, airlines, etc.).

The figures below (all extracted from the abovementioned NASA Technical Memo) provide some intuition about how the classes and properties represent the structure, content, and relationships found in ATM data. Only a subset of available figures is shown here; please consult the NASA Technical Memo for additional illustrations.

Figure 2 illustrates the key relationships among selected New York area airspace structure and facility instances. Sector nas:ZNYsector075 is one of the sectors located in the New York Air Route Traffic Control Center (ARTCC) instance nas:ZNYcenter. Sector 075 is composed of two stacked horizontal layers of airspace, each represented by a shear-sided polygon of a certain height (only one polygon is depicted in the figure). The ZNY ARTCC has operational agreements with the FAA command center and the New York Terminal Radar Approach Control (TRACON) facility, which in turn has agreements with each of the airports in its territory. The ZNY Tier 1 structure contains all ARTCCs that directly border the ZNY ARTCC. (Note that only a small subset of instances is illustrated in order to keep the figure uncluttered and readable.)

Figure 3 illustrates the basic components of the ontology representation of a flight — in this illustration, Delta Airlines flight DAL435 on 2014-07-15. The flight is associated with its departure and arrival airports; the aircraft, aircraft type, and operating carrier; and the actual and planned flight route.

Figure 4 illustrates the relationships among instances of aircraft, carrier, flight, model, manufacturer, and other classes associated with Delta Airlines flight DAL435 on 2014-07-15 (shown in Figure 3, above). The aircraft flown for this flight is N713TW, a Boeing model 757-2Q8, one of the B757-200 family of aircraft. The aircraft family is represented as a model class and the specific model is represented as an instance of that class. The FAA also designates an aircraft type, which may cover models in multiple aircraft families. The aircraft type for model 757-2Q8 is B752. Associated with type B752 aircraft are a set of instances that describe the engine type, wake turbulence category, and weight class of all B752 type aircraft.

Figure 5 illustrates how the actual and planned flight routes for American Airlines Flight #AAL335 are connected to the flight instance (atm:AAL335-201407150017), which is depicted at the root of the tree structure shown. The actual flight route is represented as a sequence of track points (atm:AircraftTrackPoint). Each track point represents a specific reporting time when the aircraft’s fix and speed is captured and relayed to ground systems. The track points are each linked to an instance of atm:LatLonFix, which stores the latitude, longitude, and altitude. The planned flight root is detailed in Figure 6 below.