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  5. Advanced Flight Efficiency Key Performance Indicators to support Air Traffic Analytics: Assessment of European flight efficiency using ADS-B data
 
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Advanced Flight Efficiency Key Performance Indicators to support Air Traffic Analytics: Assessment of European flight efficiency using ADS-B data

Author(s)
López-Leonés, Javier  
Polaina Morales, Manuel  
D'Alto, Luis  
MacNamee, Brian  
Wang, Shen  
Grover, Aditya  
et al.  
Uri
http://hdl.handle.net/10197/10940
Date Issued
2018-09-27
Date Available
2019-07-22T10:22:44Z
Abstract
Flight efficiency is of great concern in the Air Traffic Management (ATM) community since today’s ATM inefficiencies affect both airspace users (AUs) and Air Navigation Service Providers (ANSPs). Each actor has their own vision of flight efficiency: whereas airlines are concerned mainly with aspects that impact their business strategy (fuel consumption, schedule adherence and cost), ANSPs consider other aspects such as sector capacity, Air Traffic Controller (ATC) interventions, emissions and noise. Capturing both visions in new Key Performance Indicators (KPIs) is important to take new steps towards more sustainable air traffic operations.

The current standard KPI used to measure flight efficiency is the “horizontal flight efficiency”, which measures the horizontal excess enroute distance compared to the orthodromic distance. This view of efficiency is very limited since it doesn’t take into account other sources of inefficiencies, namely meteorological conditions or the vertical profile of the flight, that have a big impact on the AUs operational objectives. Therefore, advanced metrics are being developed to include these objectives in the assessment of efficiency and to analyse how the inefficiencies are distributed among them, as well as new methodologies to calculate these advanced KPIs in real time.

This paper presents a consolidated set of advanced user-centric cost-based efficiency and equity indicators which address different aspects of efficiency such as the horizontal and vertical component, fuel consumption or cost of the flight, thus introducing the airspace user’s viewpoint into consideration. Also, the methodology followed for the calculation of the indicators, based on historical data and in real time, is demonstrated. For the evaluation of the indicators, Automatic Dependent Surveillance-Broadcast (ADS-B) data and a set of user-preferred trajectories (including flight plan, optimal cost and optimal distance) as reference are used. Finally, a flight efficiency and equity assessment of the European traffic flow for three different scenarios is presented, where two whole days of air traffic in the European Civil Aviation Conference (ECAC) area were used for the efficiency indicators, and one month of traffic for specific city pairs was used for the equity indicators. This proves the added value of these newly introduced indicators, showing that different indicators account for different sources of inefficiencies, and that the use of ADS-B data could serve as a reliable source for performance monitoring
Sponsorship
European Commission Horizon 2020
Other Sponsorship
Centro de Referencia I+D+i ATM (CRIDA)
Boeing Research & Technology Europe (BR&TE)
Centre for Applied Data Analytics (CeADAR)
Flightradar24 (FR24)
Iberia
Air Europa
KLM
Turkish Airlines
AURORA’s Airspace Users Group
Type of Material
Conference Publication
Publisher
IEEE
Start Page
1122
End Page
1131
Copyright (Published Version)
2018 IEEE
Subjects

Airlines

ANSP

Flight Efficiency

KPI

Air Traffic Managemen...

SESAR

ADS-B

DOI
10.1109/dasc.2018.8569584
Web versions
https://2018.dasconline.org/
Language
English
Status of Item
Not peer reviewed
Journal
2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC)
Conference Details
DASC 2018: 37th Digital Avionics Systems Conference, London, England, UK, 23-27 September 2018
ISSN
2155-7195
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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1570442719_AURORA.pdf

Size

701.5 KB

Format

Adobe PDF

Checksum (MD5)

658e2ff0e744ccd1de9ec840907af011

Owning collection
Computer Science Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
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