More Related Content
Similar to Slide-Igor-Mahorcic
Similar to Slide-Igor-Mahorcic (20)
Recently uploaded
Recently uploaded (20)
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 1 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 2
- 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) 3
- 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 4
- 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 5
- 6. Upper airspace – European Area 6
- 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 7
- 8. Airspace structure (3) Routes Airways (5 NM width) and Upper Air Routes Straight lines connecting navpoints Airline-defined navpoints 8
- 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 9
- 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) 10
- 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 50 EC Regulation 391/2013 allows modulation of route charges to mitigate congestion 11
- 12. CR CO Cust om er G uide t o Charges January 2020 P Annex C . E st ablishing t he dist ance f act or f or int ernat ional f light s DEP = Departure aerodrome = Fl i g ht Pl a n Route = Co-ordi n ates of ATC poi n t i f l o cated on Chargi n g Zone Boundary or, Cal c ul a ted crossi n g poi n t ARR = Arri v al aerodrome = Chargi n g Zone Boundary = ATC poi n ts = Great Ci r cl e Di s tance 12 Figure: EUROCONTROL - Central Route Charges Office, Customer Guide to Charges, January 2020
- 13. Model formulation Stackelberg game Central Planner – sets en-route charges Airspace User – asses routing Bilevel formulation Strong NP-hard 13
- 14. Central Planner problem Selects optimal rates to minimise total inefficiency of the network CP decision variables: AU decision variables: CP’s objective function: 14
- 15. CP constraints Revenue neutrality: ANSP should recover costs but not generate additional value Sector and airport capacity: 15
- 16. Airspace User problem Identify optimal route and departure time to minimise total cost AU decision variables: AU objective function: Route uniqueness constraint: 16
- 17. Bilevel problem linearisation Lower level objective function – set of equivalent constraints: Bilinear terms – variable substitution: 17
- 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 18
- 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 19
- 20. Data - Trajectories For each trajectory: ID ICAO code of all sectors it crosses Minutes needed to reach each of sectors crossed 20
- 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 21
- 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 22
- 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 23
- 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% 24
- 25. Model relaxation Computational experiments showed problem to be infeasible – relaxation Capacity constraints kept imposed Cheapest Route selection – relaxed: Revenue Neutrality – relaxed: 25
- 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 26
- 27. Scenario 1 - Results Both thresholds at 20% Global Shift: 14,882 min 0.497 min/flight 27
- 28. Scenario 1 – Results (2) Capacity violations 837 sectors-hour (3.5%) 28
- 29. Scenario 1 – Results (3) Chargin zones’ revenues 29
- 30. Scenarios comparison Global Shift rises as both thresholds decrease 30 Scenario Cheapest Route Revenue Neutrality 1 20% 20% 2 20% 10% 3 10% 20% 4 10% 10% 5 5% 5%
- 31. Scenarios comparison (2) Number of violations expected and stable, vast majority small percentage violations 31
- 32. Scenarios comparison (3) Percentage violations comparison among different scenarios 32
- 33. Scenario comparison (4) Lower Revenue Neutrality threshold leads to higher revenues due to few specific charging zones 33
- 34. 34
- 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 35
- 36. Future steps Apply model to whole instance Investigate critical sectors and charging zones Different partitions of the network Different pricing schemes: segment-based 36
- 37. Thank you for your attention 37