Safer, Faster and Cheaper Air Travel thanks to Location Intelligence

13 March 2017
13 March 2017,
 0

Safer, Faster and Cheaper Air Travel thanks to Location Intelligence

Air travel - Safe separation

Safe separation is crucial!

 

The word triad “Safer”, “Faster” and “Cheaper” is counter intuitive. In general, if something is Faster it will most likely be pricier and/or riskier. Yet, thanks to Location Intelligence and Analytics it is possible to reconcile these terms to increase quality and improve business outcomes. Here we give an example related to commercial air travel.

Airspace is divided into Air Traffic Control (ATC) areas where planes must maintain radio contact. On the basis of a filed and accepted flight plan, ATC manages requested flight paths, altitudes (flight levels) and speeds to ensure safe separation between planes, from takeoff to landing. Even if the pilot in command (PIC) has final authority on what to do with his or her aircraft, in practice ATC is always providing instructions.

Airborne, a regular commercial flight will switch radio frequencies multiple times reflecting ATC airspace responsibility. For example, after takeoff, the PIC will be asked to change from tower (TWR) to departure (DEP) radio frequency. The plane no longer on visual contact with the tower and climbing is handed off to ATC who can visualize on a (secondary) radar its position and expected track allowing safe separation from other departing or arriving flights.

By now the reader understands that handing off radio communications is an important moment for both PIC and ATC.

In this article we show the use of Location Intelligence to analyze flight paths from recorded data and real communication handover to determine compliance with airspace navigation directives. Non-compliant handovers probably mean that more frequencies need to be open at a given time of day or that an unexpected situation has occurred. The important issue here is that such data analysis can help prevent incidents or accidents.

 

In the following simulated example we have hundreds of flights on a west to east route roughly overflying Lake Geneva. Compliant radio transfer above flight level FL180 (approximately 18000 feet) from French to Swiss airspace is determined by the perpendicular distance of less than 15 nautical miles (NM) to waypoint KOVAR depicted in red here below in Figure 1. To simplify, geometrically this means that planes must switch radio control within a radius of about 15 NM of KOVAR.

 

Air travel - TrafficFigure 1: Traffic around KOVAR point (in red)

Colored lines are flights paths constructed from recorded GPS positions (points on the line). The darker the line, the higher the aircraft is. Handovers are signified by purple triangles and squares on the path line. The triangle is where ATC released the PIC from its frequency and the square where collated radio contact was reestablished with the new ATC frequency.Air travel - Traffic around KOVAR

Figure 2: Traffic around KOVAR point (in red)

Data analysis is guided by several questions:

  1. Are handovers happening within the 15 NM radius of KOVAR?
  2. Where are the non-compliant handovers happening?
  3. Are non-compliant handovers happening at specific time or date intervals?

Figure 3 below answers questions 1 & 2 by depicting proportional circles. Red ones are non-compliant and blue ones are.Air travel - Handovers

Figure 3: Compliant and non-compliant handovers

 

In Figure 4, heat maps shows the concentration of non-compliant transfers over 1 day. Further analysis will help answer question 3 above.

Air travel - Handovers heatmapFigure 4: Heat map of non-compliant handover concentration over 1 day

In summary the objective of this article is to show how Galigeo can help with simple to implement yet advanced analytics:

  1. Thousands of flights can be easily visualized and analyzed by analytics users.
  2. Single flights are not relevant, aggregation however reveals tendencies. In this case compliance or not.
  3. Flights can be shown by per time period. There is the option of doing so in “motion map” mode, where analytical results flow like a movie.
  4. All this can be done quite easily in self-service mode with Galigeo for Lumira and Galigeo for Design Studio.
  5. Ultimately decision making is facilitated.

In a coming episode of Galigeo Location Intelligence we will see how predictive analytics can be used to explore air traffic what-if scenarios.

Christian TAPIA

CEO & Pilot !

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