Zilina , 22-24.11.04

1. How to improve Airport Efficiency by means of CDM: LEONARDO Linking Existing ON ground, ARrival and Departure Operations Zilina, 22-24.11.04 Patricia Pina ppina@aena.es Maria Mas mmas@aena.es

2. Contents • Scope & Approach • The System • Trials results • Arrival predictability • Off-Block predictability • Departure predictability • CFMU slot predictability • Conclusions

3. AIRPORT OPERATIONS FLOW CONTROL ARRIVAL PROCESS DEPARTURE PROCESS INTEGRATION LAYER • Solution: Integrate existing planning tools for: • Arrival management • Departure management • Ground operations management GMC AIRLINES GATE ALLOCATION Scope of LEONARDO • Problem: Lack of efficiency • Individual optimisation of airport processes • Existing information not available for all actors

4. LEONARDO Approach • 3 different levels of integration • Information sharing • Cooperation - Improvements in planning estimates • Negotiation among actors • 2 different validation techniques • Shadow mode trials • Real time simulations • 2 different testing airports • Barajas • Charles de Gaulle

5. ATC AIRPORT AUTHORITY Airport Authorities PARKING MANAGER CONOPER AMAN AMAN IBERIA AIRLINE TURN-AROUND MANAGER CDM MVT messages CDM MANAGER DMAN DMAN Manager ACARS 3OI ACARS INFO ATC INTEGRATED PLANNING SACTA SMAN SMAN RADAR TRACKS FLIGHT PLAN External Information Sources The System

6. Human-Machine Interface

7. Arrival Estimates LEONARDO AMAN SMAN ACARS CDM SLDT SIBT Taxitime ELDT EIBT MIBT MLDT AIBT ALDT

8. 12:58 11:31 10:05 8:38 7:12 5:46 4:19 2:53 1:26 0:00 G1 G3 G6 G9 G17 G19 G18 G14 TOTAL Average |EIBT airport - AIBT| Average |MIBT - AIBT| Average |EIBT airline - AIBT| In-Block Predictability 36L G9 Deicing Area G19 TWR G14 G18 TWR G17 G6 G1 MINUTES G3 G18 MIBT Mean Absolute Error 33

9. Turn Around Estimates LEONARDO CDM Airline ACARS SOBT MIBT Turn-Around Time EOBT AIBT TOBT ATOT

10. TOBT Mean Absolute Error. Flights with late arrival. 0:30:01 ATC 50 % error decrease when considering impact of late arrivals in departure flights 0:20:01 CDM MINUTES 0:10:00 0:00:00 0:00:00 0:05:00 0:10:00 0:15:00 0:20:00 0:25:00 0:30:00 EARLINESS WITH RESPECT TO THE START-UP CLEARANCE Off-block predictability • Better In-block time prediction, thus better TOBT prediction • Improvement of TOBT Predictability due to the info shared by the airlines with the CDM system. • 24 % error decrease when considering delay messages from the airline

11. Departure Estimates LEONARDO CDM SMAN ACARS DMAN SOBT STOT Taxitime EOBT ETOT MTOT TOBT ATOT AOBT

12. Take-Off predictability ERROR AS A FUNCTION OF % EGOP • Improvement of ETOT Predictability due to a better TOBT and taxiing time. • DMAN calculates the optimum departure sequence: MTOT

13. Probability of slot alarm to be reliable • Statistical simulation of the Alarm prediction based on taxiing time distribution • Measurement of discrepancies between simulated alarms and slot compliance

14. 30 25 ETOT-ATC & Airline 20 15 MTOT-DMAN 10 MTOT-CDM (only flights on time) 5 0 0 5 10 15 20 25 30 35 40 45 50 55 60 LEONARDO Results Up to 80% improvement Up to 50% improvement Up to 50% improvement In-block Predictability Off-block Predictability Take-off Predictability  CDM has positive effect on efficiency, further improvement possible

15. Leonardo Conclusion • CDM makes sense • Experiments in the three sites confirm us the same tendency: • Improvement in predictability of operations • Better management of existing resources (stands, handling equipment, runway) • Improvement of decision-making processes • The R&D results are available and stakeholders should use them: http://leonardo.aena.es

16. CFMU Future Work • Implement collaborative processes with CFMU • Inclusion of actors priorities and negotiation • Creation of a network: integrate tools at origin and destination airports

17. THANK YOU FOR YOUR ATTENTION