1) The document discusses using trajectory pricing to redistribute air traffic in the European airspace by modulating en-route charges. It aims to address capacity-demand imbalances and reduce delays and costs.
2) A bilevel optimization model is formulated to find the optimal charge rates set by the central planner to minimize inefficiency in the network, while airspace users determine optimal routes and departure times based on the charges.
3) The model is implemented on real air traffic data from 17 clusters of flights in Europe. Different scenarios are tested by relaxing constraints to trade off computation time and solution quality. Modulating charges resulted in redistributing over 14,000 minutes of flight time across scenarios.
Processing & Properties of Floor and Wall Tiles.pptx
Slide-Igor-Mahorcic
1. Trajectory pricing for the
European Air Traffic
Management system using
modulation of en-route
charges
Laureando: Igor Mahorčič Relatore: prof. Lorenzo Castelli
Correlatore: Dott. Giovanni Scaini
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Corso di Laurea magistrale in Ingegneria Elettronica e Informatica
Anno accademico: 2020/2021
2. Goal of our work
Air traffic – steady growth
Network capacity-demand imbalances
Delays and costs
Traffic redistribution during strategic phase
Modulation of en-route charges
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3. Introduction to European ATM
Eurocontrol – network manager
European Organisation for Safety of Air
Navigation
Coordinates and plans air traffic
control
Central Flow Management Unit (CFMU)
Central Route Charges Office (CRCO)
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4. Introduction to European ATM (2)
Air Navigation Service Providers – ANSPs
Flight traffic management at regional/national level
Member State’s responsibility
Usually belonging to the public sector
Mainly funded by CRCO
Airline Operators – AOs
Final user of ATM system
More than 10 million IFR flights in ECAC region
In 2019 – 28,313 IFR flights per day
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5. Airspace structure
Flight Information Regions
(FIRs)
Mainly in line with
national borders
Upper Information Regions
(UIRs)
Possible vertical split
from FIRs
Managed by Area Control
Centres (ACCs)
Around 70 ACCs in
ECAC area
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7. Airspace structure (2)
Sectors
ACCs’ further subdivision
Smallest portion of airspace
Capacity – number of aircrafts able to handle (typically 30 entries/hour)
Sector-hour
Sectorisation not unique
Configuration and capacity changes
Not official entity
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9. Air Traffic Flow Management ATFM
ATFM service – optimally exploit limited capacity; safe and efficient traffic
flow
Strategic (6 months to 7 days before): long term demand and capacity matching
Pre-Tactical (6 days before): flight plans received, strategic plan fine-tuned, ATFM
Daily Plan published
Tactical (on day of operations): ATFM Daily Plan updated, traffic managed through
slot allocation and re-routings
Flight planning controlled by Central Flow Management Unit CFMU
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10. Air Navigation Service Charges
ANSPs cost recovery – ANS charges
Route charges
Terminal navigation charges
Communication charges
Charging zones – single unit rate
Billing and collection by CRCO (monthly), distributed to ANSPs (weekly)
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11. Route charges calculation
Sum of route charges over each charging zone flown through
Charging zone’s unit rate: 𝑢𝑛
Distance factor: 𝑑𝑛, 1
100 of great circle distance between entry and exit
Weight factor: 𝑤 = 𝑀𝑇𝑂𝑊
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EC Regulation 391/2013 allows modulation of route charges to mitigate
congestion
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13. Model formulation
Stackelberg game
Central Planner – sets en-route charges
Airspace User – asses routing
Bilevel formulation
Strong NP-hard
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14. Central Planner problem
Selects optimal rates to minimise total inefficiency of the network
CP decision variables:
AU decision variables:
CP’s objective function:
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15. CP constraints
Revenue neutrality:
ANSP should recover costs but not generate additional value
Sector and airport capacity:
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16. Airspace User problem
Identify optimal route and departure time to minimise total cost
AU decision variables:
AU objective function:
Route uniqueness constraint:
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17. Bilevel problem linearisation
Lower level objective function – set of equivalent constraints:
Bilinear terms – variable substitution:
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18. Data
Data instance from Eurocontrol’s Demand Data Repository 2 (DDR2)
One day of real traffic – September 1st 2017
Filtering on flights (helicopters, military, circular flights, overflying flights)
Final instance: 29,917 flights
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19. Data - Flights
For each flight:
ID: string c_o_d_p (e.g. DLH3CJ_EDDF_LIRF_20170901083500)
Requested departure time
o_d_a triad: origin and destination airport, and type of aircraft
Trajectory operated by flight
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20. Data - Trajectories
For each trajectory:
ID
ICAO code of all sectors it crosses
Minutes needed to reach each of sectors crossed
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21. Data – Sectors-hour
For each sector hour:
Sector’s ICAO code
Minute sector-hour begins to be active
Minute sector-hour stops to be active
Declared nominal capacity
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22. Clusterisation
Critical aspect – computational complexity
Applying models to whole data instance (e.g. Bolić et. al)
Community detection by Zaoli et. al to produce clusters
Dataset split in 17 subsets
Our model applied to single clusters and results merged
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23. Model implementation
Implemented in Mosel language and solved through Xpress solver
Computational experiments conducted on standard hardware and software
64 bit Intel Xeon W-2145 CPU @ 3.70 GHz 16 core CPU computer, with 32 GB of
RAM memory and Ubuntu 20.04 operating system
Objective function change
Equity with a superlinear function
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24. Model implementation (2)
Modulation coefficient range between 80% and 120%
Time interval for shift composed of time slots
Maximum shift: 30 min
Time steps: 15 min
Big-M value: 1010
Optimal solution gap: 1%
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25. Model relaxation
Computational experiments showed problem to be infeasible – relaxation
Capacity constraints kept imposed
Cheapest Route selection – relaxed:
Revenue Neutrality – relaxed:
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26. Results
Optimisation process on single clusters
Results merged
Different threshold values used for «Cheapest Route» and «Revenue
Neutrality» constraints
Advantage: low computational time
Disadvantage: violations generation
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27. Scenario 1 - Results
Both thresholds at 20%
Global Shift: 14,882 min
0.497 min/flight
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35. Conclusions
Addressed capacity-demand imbalances
Computational time drastically reduced
Satisfactory results in terms of Global Shift
Violations: trade-off between time and quality
Small set of critical sectors-hour
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36. Future steps
Apply model to whole instance
Investigate critical sectors and charging zones
Different partitions of the network
Different pricing schemes: segment-based
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