Linking Existing ON ground, ARrival and Departure Operations

Transcription

1 Linking Existing ON ground, ARrival and Departure Operations Air Traffic Management R&D Seminar Maria Mas Patricia Pina

2 Contents Introduction to CDM and Leonardo Scope & Approach The System Trials results Arrival predictability Off-Block predictability Departure predictability CFMU slot predictability Conclusions

3 Introduction Airport partners lack up-to-date global situational awareness Inadequate and fragmented information flows Collaborative Decision Making (CDM) Improves the way Aircraft Operators, Ground Handling Agents, Airport Operators, ATC and the CFMU work together Emphasizes the importance of global collaboration in planning and managing air traffic Potential Benefits of CDM Allows best use of available infrastructure and scarce resources Supports slot compliance Gives Aircraft Operators more flexibility to maximize their own efficiency

4 CDM in Europe Eurocontrol Airport CDM Project Guide CDM implementation in a harmonised way throughout ECAC airports Airport CDM Elements Airport CDM Information Sharing Milestone Approach (The CDM Turn-round Process) Variable Taxi Time Calculation The Collaborative Pre-Departure Sequence Collaborative Management of Flight Updates CDM in Adverse Conditions Eurocontrol Airport CDM Task Force Support to Airport CDM Project Project Research and Development Project promoted by the European Commission

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

6 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

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

8 Human-Machine Interface

9 New Estimates - What Results? Safety and Capacity benefits based on subjective perception Efficiency Benefits based on: Flight predictability: Landing Time In-Block Time Off-Block Time Take-Off Time CFMU regulation compliance Decision making: Airline operations Airport operations ATC operations

10 Arrival Estimates AMAN SMAN ACARS CDM SLDT ELDT MLDT ALDT Taxitime SIBT EIBT MIBT AIBT

11 Landing Time Predictability Landing Improvement Predictability at Madrid Barajas 42.5% error decrease 20 minutes before landing Linear Polinomic Polinomic

12 In-Block Time Predictability (I) Airlines and airport authorities most interested Currently, the accuracy of the estimation is highly dependent on the skill of the person who makes the calculation The reliability of the in-block estimation depends on the reliability of the landing and taxi-time estimates AMAN and SMAN contribute to improve predictions MAD: the airline obtained 79.3% error decrease CDG: the airport obtained 55.1% error decrease

13 Turn Around Estimates CDM Airline ACARS SOBT MIBT AIBT Turn-Around Time EOBT TOBT message AOBT

14 Off-block Time Predictability Better In-block time prediction, thus better TOBT prediction 0:30:01 0:20:01 TOBT Mean Absolute Error. Flights with late arrival Madrid Barajas ATC CDM 50% error decrease for departing flights which have arrived late 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 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

15 Departure Estimates CDM SMAN ACARS DMAN SOBT EOBT TOBT AOBT Taxitime STOT ETOT MTOT MSUT ATOT

16 Take-Off Time Predictability MTOT Mean Ansolute Error. On 24 th Jan from 06:00 to 20: Minutes ETOT-ATC & Airline MTOT-DMAN Minutes before ATOT Improvement of ETOT Predictability due to a better TOBT and taxiing time. DMAN calculates the optimum departure sequence: MTOT

17 Probability of slot alarm to be reliable Slot alarms: Slot missed, Priority, Too early Measurement of discrepancies between simulated alarms and slot compliance Event of analysis Missing Alarms Wrong Alarms Correct Alarms Start-up Time 5.3% 10.8% 83.9% Off-Block Time 2.9% 2.4% 94.7%

18 Leonardo Conclusion brings technical solutions for information sharing, collaboration and negotiation amongst actors 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 Benefits more significant in disruption operations The R&D results are available and stakeholders should use them: