CDM Final Report Ath v5

8/3/2019 CDM Final Report Ath v5

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Airport Collaborative Decision Making

ATH DATA ANALYSIS

Title of the Deliverable: A-CDM - ATH Data AnalysisFinal Report

Date: 21 st November 2005Author(s): BluSky Services


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Version: 5.0 Page: i Final Report

AMENDMENT HISTORY

Version Date Comments1.0 13 SEPT 2005 Draft of First Deliverable

2.0 20 OCT 2005 Draft of Final Deliverable

3.0 28 OCT 2005 Following comments from APT

4.0 10 NOV 2005 Final Draft

5.0 21 NOV 2005 Final Report (incorporating comments on final draft)


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Version: 5.0 Page: ii Final Report

TABLE OF CONTENTS

AMENDMENT HISTORY ............................................................................................... i

TABLE OF CONTENTS ................................................................................................ ii LIST OF FIGURES ....................................................................................................... iii EXECUTIVE SUMMARY .............................................................................................. 4 LIST OF ABBREVIATIONS .......................................................................................... 9 1. Introduction ........................................................................................................ 10 2. The Airport CDM Project ................................................................................... 11

2.1 Aim ........................................................................................................................... 11 2.2 Benefits .................................................................................................................... 11

3. The Athens CDM Project and TMTool .............................................................. 12 3.1 Overview .................................................................................................................. 12 3.2 Benefits to the Airline Operator ................................................................................ 12

3.3 The Athens TMTool .................................................................................................. 13 3.3.1 Overview .......................................................................................................................... 13 3.3.2 TMT Traffic Table ............................................................................................................ 14 3.3.3 TMT Table Fields ............................................................................................................ 15 3.3.4 TMT Alarms ..................................................................................................................... 16 3.3.5 TMT Alarms Colour Codes .............................................................................................. 16 3.3.6 TMT User Input................................................................................................................ 18

4. Data Analysis ...................................................................................................... 19 4.1 Overview .................................................................................................................. 19

4.1.1 Step 1 Analysis of Local Data ...................................................................................... 19 4.1.2 Step 2 - Comparison of CFMU Data to Local Data ......................................................... 19 4.1.3 Definition of Data Elements ............................................................................................. 20

4.2 Athens Local Situation .............................................................................................. 21 4.2.1 Overview .......................................................................................................................... 21 4.2.2 The CDM Platform ........................................................................................................... 21 4.2.3 Athens Traffic Overview .................................................................................................. 21

4.3 Analysis of local data ................................................................................................ 23 4.3.1 General ............................................................................................................................ 23 4.3.2 Time of first warning T fw .................................................................................................. 26 4.3.3 Evaluation of TOBT quality for delayed flights ................................................................ 37 4.3.4 Evaluation of MTTP ......................................................................................................... 43 4.3.5 Accuracy of system calculated TOBT ............................................................................. 45 4.3.6 Conclusions ..................................................................................................................... 46

4.4 Analysis of CFMU data ............................................................................................. 47 4.4.1 General ............................................................................................................................ 47 4.4.2 Overview of basic CFMU statistics .................................................................................. 48 4.4.3 CFMU generated traffic statistics .................................................................................... 49 4.4.4 CFMU messages per flight .............................................................................................. 54 4.4.5 Joining CDM with CFMU data ......................................................................................... 58

4.5 Comparison of CFMU data to local data - Statistics ................................................. 59 4.5.1 Overview of the comparison ............................................................................................ 59 4.5.2 Comparison of T fw with DLA messages ........................................................................... 59 4.5.3 Comparison of Delayed flights with no DLA message .................................................... 63

4.6 Analysis of CTOT compliance .................................................................................. 67 4.6.1 Introduction ...................................................................................................................... 67 4.6.2 CTOT Analysis ................................................................................................................ 67

4.7 Conclusions .............................................................................................................. 70 5. Overall Conclusions .......................................................................................... 71

ANNEX I ...................................................................................................................... 72


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Version: 5.0 Page: iii Final Report

LIST OF FIGURES

Figure 1: Number of daily departures from CDM tool ........................................................... 24

Figure 2: Scheduled, Charter and Home-based flights ......................................................... 25 Figure 3-A: First warning for flights with a delay < 30 min .................................................... 27 Figure 4-A: First warning for flights with a delay 30 - 60 min ................................................ 28 Figure 5-A: First warning for flights with a delay 60 - 90 min ................................................ 29 Table 1-A: Distribution of First Warning ................................................................................ 32 Figure 3-B: First warning for flights with a delay < 30 min .................................................... 33 Figure 4-B: First warning for flights with a delay 30 - 60 min ................................................ 34 Figure 5-B: First warning for flights with a delay 60 - 90 min ................................................ 35 Table 1-B: Distribution of First Warning ................................................................................ 37 Figure 7: Delays < 30 min ..................................................................................................... 38 Figure 8: Delays 30 - 60 min ................................................................................................. 40 Figure 9: Delays 60 - 90 min ................................................................................................. 42

Table 2: MTTP categories ..................................................................................................... 43 Table 3: Percentage of flights with an ATTP below the assigned MTTP .............................. 43 Table 4: Percentage of flights with an ATTP below the reduced MTTP ............................... 44 Table 5: Accuracy of taxi time estimate ................................................................................ 45 Table 6: CFMU message types ............................................................................................ 47 Table 7: CFMU message data fields ..................................................................................... 48 Figure 10: CFMU Data on Daily Flights during 01 AUG 15 SEPT 2004 from / to LGAV ... 49 Table 8: Distribution of CFMU messages for departures from LGAV ................................... 50 Figure 11: Relative Distribution of CFMU message types .................................................... 50 Table 9: Distribution of CFMU messages for departure flights to Domestic (Greek) andInternational (non-Greek) destinations .................................................................................. 51 Figure 12: Daily Percentage of flights with DLA messages .................................................. 52 Figure 13: Daily Percentage of flights with SRM messages ................................................. 53 Table 10: Percentage of CFMU messages for departure flights to Greek and non-Greekdestinations (AUG. 2004) ...................................................................................................... 54 Figure 14: Percent of flights that received the various message types ................................. 55 Table 11-A: Number of Messages Distribution per Flight ..................................................... 56 Table 11-B: Number of Flights per Total Message Number ................................................. 57 Figure 15: Scatter graph of T DLA T fw against actual delay time ........................................... 60 Table 12: Relative values of T DLA, T fw times .......................................................................... 61 Figure 16: Comparison of T DLA and T fw ................................................................................. 62 Table 13: Percentage of delayed flights without a DLA msg. & ............................................ 63 Percentage of delayed flights with a CDM Warning .............................................................. 63 Figure 17-A: Proportion of delayed flights without a DLA message is 53.9% (left) Proportion of delayed flights with a CDM first warning is 82.3% (right) ............................... 64 Figure 17-B: Proportion of delayed flights without a DLA message is 44.7% (left) Proportion of delayed flights with a CDM first warning is 73.7% (right) ............................... 65 Figure 17-C: Proportion of delayed flights without a DLA message is 28.2% (left) Proportion of delayed flights with a CDM first warning is 84.5% (right) ............................... 66 Figure 19: Proportion of regulated flights that missed their allocated CTOT with a CDM alert(72%) .................................................................................................................................... 69


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Version: 5.0 Page: 4 Final Report

EXECUTIVE SUMMARY

Airport Collaborative Decision Making (CDM) aims to improve the airport efficiency throughimproved predictability.

Athens International Airport (AIA) launched a CDM project aiming to increase thepredictability of operations, in order to successfully manage the increased aircraftmovements during the 2004 Olympic Games period.

EUROCONTROL (the European Organisation for the Safety of Air Navigation) hasrequested a detailed analysis of the data recorded by the local CDM Tool during the 2004Summer Olympic Games period (01 AUG 15 SEPT 2004), especially the timing ofwarnings and alarms. The local CDM data were then compared with data saved by theCentral Flow Management Unit (CFMU) during the same period.

The aim of this data analysis is to explore the range of predictability benefits, for both theairport and the ATM network.

The results of this data analysis are intended to become a valuable source of information to:

Measure the predictability gains by applying Airport CDM Quantify the benefits, both locally and for the ATM network Look for improvements of airport CDM applications

KEY FINDINGS

Even a basic CDM Tool provides reliable warnings for a significant percentage of

delayed flights higher than 75 %.

These warnings are available in the period 60-90 min prior to EOBT.

With such advance and reliable warnings, all airport partners have adequate time to re-

plan / re-schedule their resources and operations, in a way to minimise the

consequences of the delay.

One of the main objectives of Airport CDM predictability of operations is

demonstrated in the best possible way. (Table ES-1 and Figures ES-2 and ES-3 refer)

Even a basic CDM Tool provides a system calculated TOBT more reliable than the

SOBT.

Even without an established TOBT procedure for AO/GH inputs, the system calculated

TOBT is the closest to the AOBT available value.

An operational TOBT procedure and the implementation of variable taxi times calculation

will significantly improve the accuracy of the TOBT.


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In 71.9% of the cases, the TMTool warning values were produced earlier than the time

the DLA message was received. (Figure ES-4 refers)

Many flights were actually delayed without an associated DLA message being sent. A

significant percentage of such flights was early identified by the local CDM tool. (Figures

ES-5 and ES-6 refers)

The CDM Tool issued an advanced warning for 72% of the flights that eventually

departed outside their CTOT window. (Figure ES-7 refers)

KEY RESULTS IN GRAPHICAL FORM

Table ES-1 - Predictability BenefitFrom all the delayed flights with an actual delay between 5 90 minutes, there was a CDMFirst Warning between 1 to 5 hours in advance for a significant percentage of them, rangingbetween 69.9% and 77.5%.Furthermore, for an additional percentage of flights there was an advance warning up to 1hour before actual departure.

First WarningPeriod(hrs)

Actual Delay category(min)

5-30 30-60 60-90 90-120

1 < T fw < 5 69.9% 77.5% 75.9% 43.8%


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Figure ES-2From all the delayed flights with an actual delay between 30-60 minutes, there was a CDMFirst Warning between 1 to 5 hours in advance for 77.5% (= 33.3 + 44.2) of them.For an additional 4.5%, there was an advance warning up to 1 hour before actual departure.

Figure ES-3From all the delayed flights with an actual delay between 60-90 minutes, there was a CDMFirst Warning between 1 to 5 hours in advance for 75.9% (= 28.7 + 47.2) of them.For an additional 6.5%, there was an advance warning up to 1 hour before actual departure.


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Figure ES-4The CDM Tool warnings were produced earlier than the time the DLA message wasreceived by the CFMU for 71.9 % of the flights.

Figure ES-5 For flights with an actual delay greater than 15 min:Proportion of delayed flights without a DLA message is 44.7% (left) Proportion of delayedflights with a CDM first warning is 73.7% (right)


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Figure ES-6 For flights with an actual delay greater than 25 min:Proportion of delayed flights without a DLA message is 28.2% (left) Proportion of delayedflights with a CDM first warning is 84.5% (right)

Figure ES-7 For 72% of the regulated flights that missed their CTOT, a CDM alert was raised.


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LIST OF ABBREVIATIONS

Abbreviation Definitiona/c AircraftAIA Athens International Airport S.A.AIBT Actual In-Block TimeALDT Actual Landing TimeAO Aircraft OperatorAOC Airline Operations CentreAOBT Actual Off-Block TimeATH Athens Airport (IATA code)ATFM Air Traffic Flow Management

ATOT Actual Take Off TimeATSP Air Traffic Service ProviderATTP Actual Turn-Round PeriodAXOT Actual Taxi Out TimeCDM Collaborative Decision MakingCFMU Central Flow Management UnitCOBT (system) Calculated Off-Block TimeCTOT Calculated Take Off TimeDLA Delay messageEIBT Estimated In-Block TimeEOBT Estimated Off-Block TimeGH Ground HandlerIBT In-Block TimeLGAV Athens Airport (ICAO Code)MTTP Minimum Turn-Round PeriodOBT Off-Block TimeSOBT Scheduled Off-Block TimeSXOT Standard Taxi Out TimeTMT Traffic Monitoring ToolTOBT Target Off-Block TimeTOT Take Off TimeTTP Turn-Round PeriodUTC Universal Time Coordinated


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1. INTRODUCTION

Athens International Airport (AIA) launched a CDM project, proactively participating in theEuropean CDM initiative. The main objective of this project is to increase the predictability ofoperations in Athens International Airport by improving the real-time traffic picture, in orderto successfully manage the increased aircraft movements during the 2004 Olympic GamesPeriod.

AIA developed a Traffic Monitoring Tool (TMTool) in accordance with EUROCONTROLrequirements for initial use during the Olympic Games. The TMTool aims to build a commonsituational awareness to all airport partners. Moreover, it monitors the turn-round processwith a simplified milestones approach.

During the exceptionally busy period of the summer 2004 (i.e. 01AUG 15SEPT 2004), alldata collected and displayed by the ATH TMT were recorded, including the warnings /

alarms raised by the TMT and their corresponding timing.Eurocontrol requested a detailed analysis of the recorded data and evaluation of thewarnings. A detailed analysis of the recorded data, especially the timing of warnings, willprovide solid facts to hopefully prove how Airport CDM can be the catalyst to achieve thepredictability benefit.

The analysis is done in two steps.

Step 1 Analysis of Local Data, in order to examine if the TMTool has given early indication / warning (and if yes, how early) for delayed flights.

Step 2 - Comparison of CFMU Data to Local Data, in order to collect evidence of benefitsboth locally and for network.


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2. THE AIRPORT CDM PROJECT

2.1 AIM

Airport Collaborative Decision Making (CDM) aims to improve the airport efficiency. This willbe achieved by sharing amongst all airport partners up to date, accurate information, thusallowing better informed decisions to be made.

The main partners involved are the airport operator, the air traffic service provider (ATSP),the aircraft operators (AO), the ground handling agents (GH) and the Central FlowManagement Unit (CFMU).

The foundation of Airport CDM is Common Situational Awareness for all airport partners, i.e.

all partners have access to the same accurate and timely information.The main objective of Airport CDM is to improve the aircraft turn-round process and ensurethe best use of airport / airline / ground handling resources, which will provide benefit for allairport partners.

2.2 BENEFITS

The following benefits of Airport CDM derive from an improved accuracy and timelyavailability of information, as well as new information. When this information is shared withtransparency amongst all airport partners, the result is increased predictability of the

operational situation.

Predictability of operations with early identification of problems. Efficient utilisation of airport resources (e.g. stand allocation) based on updatedaccurate data instead of scheduled data. Better fleet utilisation results from increased predictability and improved adherence toschedules. Efficient use of ground handling resources (both manpower and equipment). Improved departure punctuality benefits passengers, airlines and the ATM systemand enhances airport slot adherence. Increased slot (CTOT) adherence with reduced number of lost slots . Optimised use of the available runway and taxiway capacity results from optimisingthe pushback, taxi and take off sequence. Improved departure planning (by ATC / Apron Control) reduces aerodromecongestion. Reduced delays in recovering from minor disruptions, as advanced warning ofminor disruptions (e.g. runway closure for sweeping, snow clearing etc) enables partners toanticipate and plan ahead. Reduced fuel emissions and ground noise result from shorter aircraft taxi time andqueuing.


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3. THE ATHENS CDM PROJECT AND TMTOOL

3.1 OVERVIEWIn preparation for the Summer 2004 Olympic Games, Athens International Airport (AIA)launched a CDM project and developed a Traffic Monitoring Tool (TMTool), in order tosuccessfully manage the increased aircraft movements.

The main objective of this project is to increase the predictability of operations and thecommon situational awareness of all airport partners.

The developed TMTool is capable of automatically sending information about a flight topredefined recipients. Presently the following messages types are available: Warnings : Minimum Ground and Minimum Boarding Periods which are likely to producedelays. Notices : Operational events (landing, TMO, departure, etc) and status notifications.

3.2 BENEFITS TO THE AIRLINE OPERATOR

The key benefits of implementing the TMTool are the following:

Enhance common situational awareness, as common picture is shared by all airportpartners, resulting in timely decisions and reduced volume of communication and workload. Improve predictability and assist airport operations and ground handlers to build a realisticpicture, in order to enhance the management of resources (manpower and equipment). Remote access by AOCs worldwide through CDM messages providing a clearer picture oflocal situation (the status of flight at Athens airport) Increased punctuality through accurate and timely information Increased ATFM and airport slot adherence


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3.3 THE ATHENS TMTOOL

3.3.1 Overview

Athens International Airport S.A. (the airport operator) has developed a Traffic MonitoringTool (TMTool). The Traffic Monitoring Tool is a Collaborative Decision Making (CDM)application for information sharing at Athens Airport. It has been developed by AIA S.A. inaccordance with the guidelines of the Airport CDM initiative of Eurocontrol.

The Traffic Monitoring Tool is a web-based application that is readily accessible by theairlines and the Ground Handlers. It provides to all interested parties with a commonplatform, without exposing individual systems.

The TMTool provides the following: Airport traffic picture based on the airframe (aircraft-centric view) Simple interface for maximising information and reducing clutter Alarms for high probability flight delays Web-based for easy accessibility and compatibility User input using web forms Automatic updates Directly connected to the Airport Operational Database

The TMT page displays the following elements: UTC time of the last update LGAV traffic table.

The CDM information is updated every 2 minutes. The interval is considered as shortenough for CDM purposes and the system is considered to provide near live information.


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3.3.2 TMT Traffic Table

The main table contains airport traffic information organised by airframe (based onregistration). Each row displays the arrival and departure of a specific aircraft. One arrivaland departure sequence is considered a rotation .

The information for each rotation is organised into the following three categories (Columnsfor each category are grouped together):

Aircraft general information Arrival leg Departure leg

When a row is missing information on the arrival or departure leg (but never both), it meansthat the time difference between the arrival and departure legs is too big to affect theoperational efficiency of this rotation. For example, an aircraft may be departing after a two-day maintenance stop - no information on the arrival leg will be displayed.

The traffic table header has a short abbreviation for describing each column. Pop-up tips areproduced by briefly resting the mouse arrow over the heading items and provideexplanations for each column heading.


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3.3.3 TMT Table Fields

Name Description Category

Regn AircraftRegistration/Identification Aircraft DataA/C Aircraft TypeF Arrival Operational Flight Type

Arrival Leg

*Status of Arrival Flight: AIRB: Airborne / En-route TMO: Ten (10) Min. Out LAND: Landed ARR: Arrived on Stand

T Departure Flight Nature TypeAFL Arrival Flight NumberOrig Airport of OriginSIBT Scheduled In-Block Time

IBT In-Block TimeLand Landing TimeRwyA Runway of ArrivalStA Stand of ArrivalSTATA Status of Arrival Flight*STATD Status of Departure Flight**

DepartureLeg

**Status of Departure Flight: OBK: Aircraft On-Block atstand GTP: Gate Open BRG: Boarding in progress FNL: Final Call GCL: Gate Closed OBK: Aircraft Off-Block DEP: Departed, take off

F Departure Operational FlightTypeT Departure Flight Nature TypeDFL Departure Flight NumberDest Airport of DestinationSOBT Scheduled Off-Block TimeOBT Off-Block TimeTOBT Target Off-Block TimeTOT Take off TimeRwyD Runway of DepartureStD Stand of DepartureGt Gate


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3.3.4 TMT Alarms

The primary objective of the TMTool is in not only identifying possible delays but also toidentify situations with a high probability that will result in a loss of an allocated slot (CTOT).Alarms are used by TMTool as visual clues to users, warning that a delay is probable basedon the provided data. They can therefore be regarded as trigger to the aircraft operators toprovide updated data.

Currently the types of alarms implemented are: Minimum Turn-round Period Boarding not started Invalid Take off time

3.3.5 TMT Alarms Colour Codes

The following colour codes are used for the alarms implemented.

Alarm Colour Proposed ActionTurn-Round Period Yellow

Turn-Round Period with loss of slot Manual update ofTOBT*Boarding Alarm pinkBoarding Alarm with loss of slotInvalid Estimated Take Off time red

N.B. When the delay is > 15 min, a DLA message should be sent to CFMU

3.3.5.1 Minimum Turn-Round Period alarmMinimum Turn-Round Period (MTTP) alarms are based on MTTP values by aircraft type (30,45, 60 or 90 min.). The MTTP is the minimum time needed for servicing and boarding of anaircraft.The alarm compares this value to the best estimated time: TTP = OBT - IBT

Whenever the TMTool identifies a rotation for which the turn-round schedule or estimate isless than the minimum, then it will take one of two sets of actions, based on the probabilityof loss of allocated slot.

If the expected delay has a high probability for slot loss, then a system-calculatedTOBT will be displayed in the TOBT field highlighted by the appropriate colour.

If despite the estimated delay the slot can still be met, the OBT value will behighlighted.

The Minimum Turn-Round Period alarm is a good indication that attention should be paid tothese rotations. The system-calculated TOBT provided should be considered only as a bestcase estimate. Whenever this alarm appears user input is encouraged.


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The operation centre of the airline is responsible to monitor the progress of each flight andtake all the necessary actions in order to handle any delays that may occur. Constantmonitoring of the TMT will identify as early as possible any probable delay.

Whenever a MTTP alarm is identified, then the airline shall do the following:

a. If the system hasnt proposed any TOBT but the OBT field of the departure leg iscoloured, no action is necessary by the airline. Closed monitoring of the specific rotation isnecessary, in order to identify further delays.

b. If the system proposes a new TOBT then the airline shall do one of the following:

In case they can meet the EOBT specified in the flight plan, then they can set asTOBT a time between EOBT and EOBT+15 min.

If they cannot meet the EOBT, then they shall send a DLA message to CFMU statinga new EOBT for the flight.

Home based airlines, based on their operational needs may proceed to an aircraftrotation change in order to comply with the EOBT specified in their flight plan.

It is very important that the airline will react as earliest as possible to the MTTP alarm, sincethis will allow the airport to proceed very early with any stand and gate allocationadjustments.

Ideally the reaction of the airline is expected at ALDT of the arriving leg. After ALDT,changes to the planning should be only due to unforeseen circumstances beyond the controlof the Airline or the Ground Handler or airport/ATC or due to slot improvement by CFMU.

3.3.5.2 Boarding alarmTMTool considers a minimum boarding period of 30 min. for passenger flights. Therefore itwill provide a visual alarm whenever it identifies a flight that has not started boarding withinthe above time period of the latest off-block time. If TMTool identifies the possibility of a lossof an allocated slot, then it will emphasise using another (but similar) colour. The boardingalarm will not set a system TOBT.

When a boarding alarm is raised by the system, then the Ground Handler and / or the airlineshall be responsible to update the TOBT based on their data.

When a boarding in progress status is received then the boarding alarm is not applied.

3.3.5.3 Invalid Take off time estimatesAn estimated / calculated take off time (TOT) is considered invalid when it is in the past andthe aircraft is still on the ground. This alarm is a warning to the aircraft operator that anupdate is needed.


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3.3.6 TMT User Input

User input is permitted only in the TOBT field. The user can click on any TOBT value --whether estimated by the system or provided by the user -- in order to provide an updatedTOBT.

This input is to reflect the aircraft operator's better knowledge of the actual operationalconditions and limitations. Any information obtained from the aircraft operator has a higherpriority from all other system entries. TMTool alarms are not applied to user input.

NoteIt should be noted that user inputs were not applicable during the period of the 2004Olympic Games, specifically in the period between 15 th of July 2004 and 30 th of September2004, when the present current data analysis refers.


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4.1.3 Definition of Data Elements

Dataelement

Definition Source Remark

Localdata

AOBT Actual Off-BlockTime

TMT

SOBT Scheduled Off-BlockTime

TMT

TOBT Target Off-BlockTime

TMT calculation

MTTP Minimum Turn-roundPeriod

Fixed value per type of a/c table in TMT

See Table 2 inpara. 4.3.4

ATTP Actual Turn-roundPeriod == AOBT - AIBT

TMT

TFW Timestamp of issueof the First Warning

TMT(both for the First Warningcalculation and for thetimestamp recording)

See para.3.3.5.1 fordefinition ofwarning

Date Day/month/year TMTActualDelay

AOBT - SOBT Calculation during theanalysis

SXOT Standard Taxi OutTime = 5 min.

TMT Default value

AXOT Actual Taxi Out Time= ATOT - AOBT

TMT

CFMUdata

CTOT Calculated Take OffTime

CFMU data set

TDLA Timestamp of receiptby CFMU of the firstDLA msg

CFMU data set

Date Day/month/year CFMU data set


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4.2 ATHENS LOCAL SITUATION

4.2.1 Overview

This report presents an analysis of the data collected by the CDM TMTool at the AthensInternational Airport for the period of Aug. 1 Sept. 15, 2004. This period was exceptionallybusy due to the Summer 2004 Olympic Games.

Eurocontrol requested to evaluate the usefulness and impact of CDM. A detailed analysis ofthe recorded data and evaluation of the warnings provides solid evidence that CDM can bea catalyst for enhanced airport operations.

4.2.2 The CDM Platform

4.2.2.1 The Athens CDM TMToolThe CDM tool at Athens described in chapter 3.3, called TMTool, is still experimental andundergoing active development. Never the less, it is sufficiently advanced to provide trafficmonitoring and a number of CDM functionalities. All data displayed by TMTool whererecorded.

The file we obtained was in XML format and a direct output of the TMTool table entries.

4.2.2.2 The TMTool alarmsTMTool provides alarms that signify a likelihood of delay from the latest estimates. For theSummer 2004 period reviewed here there were two type of alarms used:

Minimum turn-round alarms when the estimated time period from on-block to off-block isless than a minimum value. The minimum value is based on aircraft type.

Boarding alarms when flight has not started boarding within half hour of estimated off-blocktime.

Each of the above alarms has a degree factor. If the minimum calculated off-block time isbeyond 15 min. from estimate, then the alarm provides a minimum TOBT system estimate.

4.2.3 Athens Traffic Overview

The Athens Airport has two Home-based carriers (Olympic Airlines and Aegean Airlines).The traffic for this period was heavily influenced by the Olympic Games taking place.

During the game, special rules were in effect and extra temporary parking stands wereallocated. Of particular interest with respect to CDM is the fact that in anticipation ofexcessive traffic, ground based time was limited to 2 hours for most operators.


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The Olympic Games influenced the traffic in a number of ways: Increased traffic. Special scheduling rules in effect for most operators. Unusually large number of special (government, VIP) flights.

The Home-based carriers constituted 58.3% of the total traffic, 65.2% of scheduled andcharter traffic.

The traffic analysis is complicated by the transient nature and complexity of airportoperations. Therefore it is particularly difficult to assess the contribution of a live systemsuch as the CDM TMTool by looking at the final traffic as taken place.

This is particularly true for the Home-based operators. Home-based operators can forexample switch aircraft and use other options to rectify upcoming delays that are notavailable to other carriers.


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4.3 ANALYSIS OF LOCAL DATA

4.3.1 General

Total number of departures in CDM Tool: 13845

NoteCDM Tool focuses on regular airport traffic and does not include some categories of specialflights such as government, military and other categories. Such flights under normaloperating conditions do not affect the overall airport operations.

The daily departures traffic from TMTool is shown in Figure 1, based on scheduled dates.

The high traffic peak at the end of the Olympic Games is evident. Figure 1 also provides thenumber of delayed flights based on the categories requested for this analysis. Flights withlarge delays are grouped together to maintain clarity.

Input Data elements used:Date, daily total number of departures, number of departures with an actualdelay within a predefined range.From which dataset: entire period 45 days

Process Count daily departing flights in TMT.For each flight Calculate actual delay [AD = AOBT SOBT] in min. andclassify result in one of the following categories:5


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Figure 1: Number of daily departures from CDM tool

From Figure 1 the two peak traffic days at the opening and closing days of the OlympicGames are easily identified. For the rest of the days, the variations in traffic figures wererelatively small.

Please note that the pattern for the flights with actual delays < 60 min follows closely thepattern of the total traffic.


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Figure 2 shows the passenger traffic (scheduled & charter flights) and the number of flightsby home-based carriers.

Input Data elements used:Date, daily total number of departuresFrom which dataset: entire period 45 days

Process For each departing flight examine if the operating airline belongs to one of thefollowing categories:Is it a scheduled or a charter airline? Y/NIs it a home based airline? Y/N

Output Figure 2

Figure 2: Scheduled, Charter and Home-based flights


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4.3.2 Time of first warning T fw

The time of first warning is important, because the earlier a probable problem is identified,the more time there is to take action.

Here we look at CDM alarms of the minimum turn-round (MTTP) type. Boarding alarms areby definition close to the latest off-block time, so it does not make much sense for these toanalyse the time of first appearance.

4.3.2.1 Data Collection

The data collected referring to the time of first warning is depicted in Figures 3 6.

Figures 3A 6A are dot plots of the time of first warning from the scheduled off-block time (SOBT). Each dot represents one flight and therefore one warning.

The flights are divided in 4 categories, according to the duration of the delay they actuallyexperienced (i.e. actual delay of 5-30 min, 30-60 min, 60-90 min, 90-120 min.). Each figuredepicts the flights with an actual delay within the indicated time range.

The horizontal axis depicts the date, ranging in the period 01 AUG to 15 SEPT 2005.

The vertical axis depicts how many hours in advance the first warning was issued, rangingfrom 0 to 13.


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Input Data elements used:Date, subset of total number of departing flights, defined by the followingselection criterion:daily total number of departures with an actual delay [AD = AOBT SOBT]within the following range:5


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4.3.2.2 Distribution of T fw Data Analysis

Following the analysis of the data shown in Figures 3A-6A, from the obtained densities wedecided to group the data and focus into the following categories:

1. Flights with a First Warning issued up to one (1) hour earlier than the SOBT(Tfw < 1 hour)

2. Flights with a First Warning issued between one (1) hour and two and a half (2.5) hoursearlier than the SOBT (1 < T fw < 2.5 hours)

3. Flights with a First Warning issued between two and a half (2.5) hours and five (5) hoursearlier than the SOBT (2.5 < T fw < 5 hours)

Based on the data shown in Figures 3A-6A, the overall statistics for first warning aresummarized in Table 1-A .

Input Data elements used:subsets of total number of departing flights, defined by the following selectioncriterion:daily total number of departures with an actual delay [AD = AOBT SOBT]within one of the following ranges:

05


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Table 1-A: Distribution of First Warning

It should be clear that indications of probable problems 11 hours in advance signify ascheduling problem.

The percentages per delay category depicted in Table 1-A are represented in the followingFigures 3-B 6-B .

First

WarningPeriod(hrs)

Actual Delay category

(min)

5-30 30-60 60-90 90-120

Tfw < 1 4.3% 4.5% 6.5% 7.0%

1 < T fw < 2.5 34.9% 33.3% 28.7% 14.0

2.5 < T fw < 5 35.0% 44.2% 47.2% 29.8

5 < T fw < 11 2.8% 10.0% 12.0% 49.1%

11 < T fw 23.0% 8.1% 5.6% 0.0%


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Figure 3-B: First warning for flights with a delay < 30 min

From all the delayed flights with an actual delay between 5-30 minutes, there was a CDMFirst Warning between 1 to 5 hours in advance for 69.9% (= 34.9 + 35.0) of them.For an additional 4.3%, there was an advance warning up to 1 hour before actual departure.


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Figure 4-B: First warning for flights with a delay 30 - 60 min



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4.3.2.3 Distribution of T fw ConclusionsAs the main objective of this analysis was to examine any predictability benefits, thefollowing Table 1-B is showing the key results of the analysis.

It is simply a repetition of the main part of data depicted in Table 1-A , adding the main twosub-categories into one.

Table 1-B: Distribution of First Warning

Predictability BenefitFrom all the delayed flights with an actual delay between 5 90 minutes, there was a CDMFirst Warning between 1 to 5 hours in advance for a significant percentage of them, rangingbetween 69.9% and 77.5%.Furthermore, for an additional percentage of flights there was an advance warning up to 1hour before actual departure.

4.3.3 Evaluation of TOBT quality for delayed flights

When compared to the scheduled time (SOBT), the TOBT provided by the TMTool is closerto the actual departure (AOBT).

In particular, in Figure 7 we see that the TOBT estimate has a clear 15 min. marginimprovement.

Input Data elements used:subset of total number of departing flights, defined by the following selectioncriterion:daily total number of departures with an actual delay [AD = AOBT SOBT]within the following ranges: 5 value on

vertical axis plot the result on the graph

First WarningPeriod(hrs)

Actual Delay category(min)

5-30 30-60 60-90 90-120

1 < T fw < 5 69.9% 77.5% 75.9% 43.8%


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Output Figure 7

Clarification Definition of the BLUE lineThe BLUE line indicates when AOBT TOBT = 0 (i.e. when AOBT = TOBT).This would be the ideal situation, when the prediction of the CDM tool isperfect.Definition of the RED lineThe RED line (at an angle of 45 degrees) indicates when AOBT TOBT =AOBT SOBTThe fact that the actual data follows somehow this pattern proves that theCDM tool is working towards the right direction, even if there is still a marginof approx. 15 min.The pattern explicitly indicates the consistency and quality of the TMT data.By comparing Figures 7, 8 & 9, it is noticed that the quality decreases as theactual delay increases.

Figure 7: Delays < 30 min

From all the delayed flights with an actual delay between 5-30 minutes, the TOBT calculatedby the CDM Tool is closer to the AOBT ( blue line) than the originally available SOBT ( red line).However, the prediction of the AOBT is still not very accurate; there is still a margin ofapprox. 15 min.


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Output Figure 8

Clarification Definition of the BLUE lineThe BLUE line indicates when AOBT TOBT = 0 (i.e. when AOBT = TOBT).This would be the ideal situation, when the prediction of the CDM tool isperfect.Definition of the RED lineThe RED line (at an angle of 45 degrees) indicates when AOBT TOBT =AOBT SOBTThe fact that the actual data follows somehow this pattern proves that theCDM tool is working towards the right direction, even if there is still a marginof approx. 15 min.

The pattern explicitly indicates the consistency and quality of the TMT data.By comparing Figures 7, 8 & 9, it is noticed that the quality decreases as theactual delay increases.


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Figure 8: Delays 30 - 60 min

From all the delayed flights with an actual delay between 30-60 minutes, the TOBTcalculated by the CDM Tool is closer to the AOBT ( blue line) than the originally availableSOBT ( red line).However, the prediction of the AOBT is still not very accurate; there is still a margin ofapprox. 15 min.


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Output Figure 9

Clarification Definition of the BLUE lineThe BLUE line indicates when AOBT TOBT = 0 (i.e. when AOBT = TOBT).This would be the ideal situation, when the prediction of the CDM tool isperfect.Definition of the RED lineThe RED line (at an angle of 45 degrees) indicates when AOBT TOBT =AOBT SOBTThe fact that the actual data follows somehow this pattern proves that theCDM tool is working towards the right direction, even if there is still a marginof approx. 15 min.

The pattern explicitly indicates the consistency and quality of the TMT data.By comparing Figures 7, 8 & 9, it is noticed that the quality decreases as theactual delay increases.


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Figure 9: Delays 60 - 90 min

From all the delayed flights with an actual delay between 60-90 minutes, the TOBTcalculated by the CDM Tool is closer to the AOBT ( blue line) than the originally availableSOBT ( red line).However, the prediction of the AOBT is still not very accurate; there is still a margin ofapprox. 15 min.

When evaluating the TOBT one should keep in mind that:

the computed TOBT by the TMTool is a minimum ground time estimate , and it is NOT an estimate of the OBT.


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4.3.4 Evaluation of MTTP

To evaluate the effectiveness of the MTTP (Minimum Turn-round Time Period), the valuesused were compared to the actual turn-round times and how effective the chosen categories

/ values are.

It is important to look at each category separately as the dynamics of each category aredifferent. Athens airport for the period reviewed used the categories listed in Table 2 .

Category MTTP(min.)

Typical aircraft

1 30 All smaller a/c2 45 B737, A320, MD80, MD90, BA1463 60 B727, B757, DC8, T1544 75 B767, A300, A310, IL62, L1015 90 A330, A340, B777, B747, MD11

Table 2: MTTP categories

In order to reduce other factors the present analysis was limited to passenger flights(scheduled and charter). Table 3 shows the percentage of flights which achieved shorterturn-round periods compared to the MTTP - per aircraft assigned category.

Input Data elements used:MTTP, ATTPFor all flights and for the subset of flights operated by airlines not based inLGAVFrom which dataset: entire period 45 days

Process For each and every flight: Compare values of ATTP and MTTP Calculate percentage of flights with ATTP < MTTP Calculate percentage of non home based flights with ATTP < MTTP

Output Table 3

Category MTTP

(minutes)

All Flights

(%)

Non Home BasedFlights

(%)1 30 0.0 0.02 45 8.5 10.03 60 14.2 14.24 75 26.3 31.35 90 8.5 9.1

Table 3: Percentage of flights with an ATTP below the assigned MTTP


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Clearly Table 3 shows that there is a significant percentage of flights achieving better MTTPthan the assigned minimum, especially for most of the higher categories.

This is probably due to MTTP values set somewhat high or inconsistent assignment ofaircraft types into the various categories. For MTTP to be an effective alarm factor, thepercentage of flights that achieve actual turn-round periods shorter than the MTTP assignedcategory should be small. These should be the exception.

By analysing the effect of reducing the MTTP by a small value, such as 5 and 10 minutes,Table 4 shows that small adjustments to MTTP can reduce the percentage of flights with anactual turn-round time below the MTTP and therefore improve the quality of the predictionand produce significantly better results overall for the airport.

Input Data elements used:MTTP, ATTPFor all flights and for the subset of flights operated by airlines not based inLGAVFrom which dataset: entire period 45 days

Process For each and every flight: Compare values of ATTP and MTTP Calculate percentage of flights with ATTP < MTTP 5 min. Calculate percentage of flights with ATTP < MTTP 10 min. Calculate percentage of non home based flights with

ATTP < MTTP 5 min.

Calculate percentage of non home based flights withATTP < MTTP 10 min.Output Table 4

Cat MTTP(minutes)

All flightsif MTTP

reduced by5 min.

(%)

All flightsif MTTP

reduced by10 min.

(%)

Non Home basedflights

if MTTP reducedby 5 min.

(%)

Non Home basedflights

if MTTP reducedby 10 min.

(%)1 30 0.0 0.0 0.0 0.0

2 45 3.1 0.8 3.4 0.73 60 5.7 0.0 5.7 0.04 75 18.0 11.9 22.3 14.45 90 7.0 2.8 7.6 3.0

Table 4: Percentage of flights with an ATTP below the reduced MTTP

Small adjustments to MTTP values and categories should significantly reduce any numberof false alarms and can improve the prediction of the actual MTTP. It may be interesting toinvestigate the variation of MTTP values by airline. While some -few- flights have MTTP


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values slightly below the ones used in this CDM dataset, it is quite possible that a number ofairlines have greater MTTP values.

4.3.5 Accuracy of system calculated TOBT

During the period reviewed here, Athens Airport used a single fixed taxi time of 5 min. forboth arrivals and departures. Obviously this value can not be universally effective for all timeperiods, stand locations and runways. Therefore TOBT calculations are not expected to beaccurate.

This is confirmed by evaluating the actual departure taxi times with respect to the systemestimated time of 5 min.:

Input Data elements used:SXOT, AXOTFor all flights departing LGAVFrom which dataset: entire period 45 days

Process For each and every flight: Compare values of SXOT and AXOT Calculate percentage of flights with 4 < AXOT < 6 (min.) Calculate percentage of flights with 3 < AXOT < 7 (min.)

Output Table 5

Taxi time(min)

traffic

5 1 9.3%5 2 17.3%

Table 5: Accuracy of taxi time estimate

Clearly only a relatively small percentage of departures falls within a reasonable margin.There is significant room for improvement on taxiing estimates by employing variableestimated taxi times. How these should be implemented requires a detailed investigation of

taxi patterns and is definitely an airport specific issue.


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4.3.6 Conclusions

By reviewing the Figures 3-6 and the consolidated results in Table 1 , the followingconclusions can be drawn:

Even a basic CDM Tool provides reliable warnings for a significant percentage ofdelayed flights higher than 75 %.

These warnings are available in the period 60-90 min prior to EOBT. With such advance and reliable warnings, all airport partners have adequate time to re-

plan / re-schedule their resources and operations, in a way to minimise the impact ofthe delay.


demonstrated in the best possible way.

Furthermore, by reviewing the Figures 7-8 , the following conclusions can be drawn:

Even a basic CDM Tool provides a system calculated TOBT more reliable than theSOBT.

Even without an established TOBT procedure for AO/GH inputs, the system calculatedTOBT is the closest to the AOBT available value.

An operational TOBT procedure and the implementation of variable taxi times calculationwill significantly improve the accuracy of the TOBT.


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4.4 ANALYSIS OF CFMU DATA

4.4.1 General

Total number of departures in CFMU dataset: 14252

NoteCFMU dataset consists of flights that filed a flight plan and all messages associated withthese flights.

The CFMU data provided for flights to and from Athens was also covering the period from 01August to 15 September 2004.

As previously stated, this period was particularly interesting as the summer Olympic Gameswere taking place in Athens. Due to the increased traffic, special strict rules were affectingall traffic to and from Athens.

The data set provided included a total of 45201 messages for flights departing from / arrivingto LGAV, containing five different message types, identified in Table 6 .

Input MessageType

Comment

TIM FPL Flight PlanTIM DLA Delay MessageTIM CHG Change Message

TOM SAM Slot Allocation MessageTOM SRM Slot Revision Message

Table 6: CFMU message types

The Slot Revision Messages are important to air traffic management and airspace efficiency,as these are sent out to update the CTOT of regulated flights. In Table 6 the following inputdefinitions are used:

TIM: TACT input messageTOM: TACT output message


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Each message contained the headings as identified in Table 7 .

Nr. Header Description

1 FLT_EVT_FLT_UID Unique identifier of flight

2 OPL_EVT_ID Event id

3 EVT_KIND_ID Message type

4 OPL_EVT_TIME_STAMP Timestamp of messages

5 FLT_EVT_ACFT_ID Flight call-sign

6 FLT_EVT_DEP_AD Departure code

7 FLT_EVT_DEST_AD Destination code

8 FLT_EVT_EOBT Estimated OBT

9 FLT_EVT_LOBT Last OBT

10 DIALOG_MSG_NEWEOBT New EOBT

11 DIALOG_MSG_NEWCTOT New CTOT

Table 7: CFMU message data fields

The DIALOG_MSG_NEWEOBT field was empty and was not used. No other flight statusinformation was provided.

4.4.2 Overview of basic CFMU statistics

The CFMU data provides a look at the Athens traffic from a different perspective. Eachsystem has a different set of goals and constraints. These differences are useful, when

compared, in bringing out different and new views on the system under observation. Thisprocess can bring up new ways to approach the system for increased efficiencies or newtarget goals.

It was arbitrarily chosen to constrain the CFMU data analysis to 01 AUG 15 SEPT, as thiswas judged to provide a complete and consistent traffic period unit. Such a unit will beusefully in future or past comparisons.

The analysis and comparison of TMTool and CFMU data will focus on departures flights outfor Athens LGAV. The TMTool application generates warning for departure flights only andtherefore makes little sense to compare CFMU messages for arrival flights into Athensairport.


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4.4.3 CFMU generated traffic statistics

The CFMU daily traffic was based on the EOBT time of the last message.

Figure 10 shows the daily arrival, departure and total Athens LGAV traffic for the month ofAugust, based on the provided data.

Figure 10: CFMU Data on Daily Flights during 01 AUG 15 SEPT 2004 from / to LGAV

Figure 10 is fairly similar to Figure 1 , which depicts the daily departures from Athenscollected from the TMTool. There are some small differences in the numerical values, butthis is to be expected when comparing data from disparate sources.

The numbers of flights shown in Figure 10 were developed using a unique id (UID) byemploying a combination of the CFMU fields of Flight Callsign and the date portion of theEOBT field.

The reason why not to rely solely on the CFMU Flight Event Unique ID(FLT_EVT_FLT_UID) is explained at 4.4.3.2.


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4.4.3.1 Distribution of CFMU message types

The total number of CFMU messages referring to flights departing LGAV for August 2004was 23456. Table 8 displays the percentage per message type.

CFMUMessage Type

Percent of eachmessage type

(compared to thetotal number of

messages)CHG 9.1%DLA 9.4%FPL 60.5%

SAM 11.7%SRM 9.3%

Table 8: Distribution of CFMU messages for departures from LGAV

The relative distribution of CFMU message types - for departure flights - is shown in Figure11 . It is clear that the majority are flight plan messages.

Figure 11: Relative Distribution of CFMU message types


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Similarly Table 9 shows the percent distribution of CFMU messages for flights to Greek(domestic) and non-Greek (international) destinations.

Percent ofCFMU

messagesassociated

to DomesticFlights

Percent ofCFMUmessagesassociated toInternationalFlights

48.5% 51.5%

Table 9: Distribution of CFMU messages for departure flights to Domestic (Greek) andInternational (non-Greek) destinations

The daily percentage of DLA and SRM messages, for all arrival and departure flights, isshown in Figures 12 and 13 .


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Input Data elements used:All flights with all corresponding CFMU messagesFor all flights arriving at and departing from LGAVFrom which dataset: entire period 45 days

Process For each and every day: Calculate percentage of flights with at least one associated DLA

messageOutput Figure 12

Figure 12: Daily Percentage of flights with DLA messages


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Input Data elements used:All flights with all corresponding CFMU messagesFor all flights arriving at and departing from LGAVFrom which dataset: entire period 45 days

Process For each and every day: Calculate percentage of flights with at least one associated

SRM messageOutput Figure 13

Figure 13: Daily Percentage of flights with SRM messages


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4.4.3.2 The CFMU UID fieldCFMU provided an additional (complementary) set of data containing all messagesexchanged for flights to / from Athens, not just messages for terminated flights. This set is ofinterest because it provides the exchange of messages with the aircraft operators.

The CFMU data contain a field named Unique Identifier of a Flight (UID). This is the first fieldof every record in the data set provided. CFMU gives a new unique ID for every Flight Planmessage (application) it receives (at least in the provided data set). It identifies each time aflight plan is filed. By using this unique field to count the number of flights the result is10502 uniquely identified departures for only the month of August .

Often though some flights file multiple flight plans. After processing based on the flight call-sign and departure date (as each call-sign is unique on a daily basis) then the total count is10088 departure flights.

NoteThis comparison was only possible for the month of August, because the provided datasetfrom CFMU contained also raw data. This is the only paragraph in this analysis that somefigures refer to a part of the period under analysis.

This difference represents a 4.1% difference. A 4 - 5% decrease in the number of new FlightPlans could result in noticeable improvement in flow and capacity management, especiallyduring peak areas and times.

4.4.4 CFMU messages per flight4.4.4.1 Percent of messages type per flight

The message percent distribution by flight is included in Table 10 and shown in Figure 14 .

MESSAGE TYPE DOMESTICFLIGHTS

INTL.FLIGHTS

ALLFLIGHTS

SAM 9.9% 27.2% 18.8%SRM 3.7% 12.8% 8.4%FPL 99.5% 99.6% 99.2%CHG 10.6% 16.8% 13.8%

DLA 10.3% 12.9% 11.7%

Table 10: Percentage of CFMU messages for departure flights to Greek and non-Greekdestinations (AUG. 2004)

Please note that Table 10 is based on TERMINATED (CFMU status) flights only.


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Figure 14: Percent of flights that received the various message types

The figure provides breakdown into total (all flights), Greek (domestic) and Non-Greek(international) departure flights. Practically every flight in the CFMU data set has acorresponding Flight Plan (FPL) message.

Please note the relatively small percentage of DLA messages.


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4.4.4.2 Number of messages distribution per flight

Many flights received or sent more than one corresponding messages, often repeating amessage of the same type.

Table 11-A shows for how many flights each message was repeated and how many times.

NUMBER OF FLIGHTSPER MESSAGE

NUMBER OFMESSAGESPER FLIGHTSAM SRM FPL CHG DLA

11573 13053 69 12283 12587 02623 680 14183 1826 1312 1

54 263 126 257 21 129 14 53 31 72 2 24 4

26 1 6 519 8 6

4 1 74 3 82 1 9

10

Table 11-A: Number of Messages Distribution per Flight

An example from the table may help to explain it better: the SRM column shows 263 flightsthat where associated with 2 SRM messages. Going down on row, the table shows that 129flights where associated with 3 SRM messages.

Another interesting observation is that there were 69 flights without a corresponding FPLmessage. However, a closer look on those flights revealed that all of these flights took placeon 01 AUG and the corresponding FPL was filed on the day before, the 31 JUL which is notcovered by this analysis.

As already noticed in Figure 14 , there was a high number of flights (12587 flights) without acorresponding DLA message.


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Table 11-B shows the total number of flights per total number of corresponding messages.

TOTALNUMBER

OFMESSAGESPER FLIGHT

TOTALNUMBER

OF FLIGHTS

0 01 90752 31083 11214 4925 220

6 1007 528 449 17

10 1111 412 313 114 215 016 117 018 1

Table 11-B: Number of Flights per Total Message Number

As an example from the table to explain it better, for 492 flights there was a total of 4associated messages exchanged.


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4.4.5 Joining CDM with CFMU data

4.4.5.1 ChallengesDue to the different development of the CFMU data and the CDM data provided by TMTool,there are a number of challenges to overcome in joining the flight information. The CFMUdata provided did not have the following data that would simplify the joining process andmake it more reliable:

Lack of aircraft registration number from CFMU data. Lack of actual OBT (AOBT) from CFMU

Such information is of course readily available to any airport based system, such as TMTool.This should absolutely not be perceived as a critique of CFMU data. It is a simplerecognition that airport based systems are closer to their basin of operation.

To overcome the above, we note that the call-sign is unique for each day. Also, the dateportion of the Estimated Off-Block Time (EOBT) can be used to get the day of Off-Block.Therefore to join the flights from the two data sets, we used:

1. Flight Call-sign from both systems2. The departure date value, obtained from EOBT for CFMU and from the AOBT

from the CDM system.

4.4.5.2 ResultsAs stated previously, since the comparison analysis focuses on departures flights, only theLGAV departure flights where combined. Applying the joining method described in section4.4.5.1, 84.3% of CFMU departure messages were corresponded to a TMTool departureflight entry.


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4.5 COMPARISON OF CFMU DATA TO LOCAL DATA - STATISTICS

4.5.1 Overview of the comparison

The comparison of CDM data obtained from TMTool with the CFMU data is for the AthensAirport traffic for the period from 01 to 31 of August 2004, and is focused on departureflights.

CDM is a new concept being introduced to assist in the efficient management and use ofavailable resources. TMTool is part of this effort focused at the airport operational level. Theaims and goals of the CDM initiative have many dimensions. A key part is to avoid delays byidentifying probable delays early, so corrective actions can be taken.

TMTool provides a system of warnings that identify such situations. The system usesadvanced algorithms and heuristics in order to accomplish this. It is of most interest,especially since CDM is a new concept being currently introduced, to evaluate how newCDM tools such as TMTool:

1. perform compared to existing systems, and2. how they can complement existing systems

For this study, the TMTool time of first warning (T fw) will be compared with the time of DelayMessages (T DLA) received by CFMU.

4.5.2 Comparison of T fw with DLA messages

For comparing with the CDM T fw, the following procedure was used:

1. Identify DLA messages2. Join these with the TMTool data3. Using the AOBT provided by TMTool, keep only those messages for which the

messages time is prior to off-block. Delay messages after departure from Athens arenot relevant here.

4. Group messages by relevant flight. If multiple flight plans submitted for a flight, usethe relevant one.

5. Compare the DLA message time with T fw for each flight.

Based on the provided data for the period 01 AUG-15 SEPT, the following number of DLAmessages was processed:

Total number of DLA messages: 2199 Total number of DLA messages joined with flights from the TMTool: 1908 (giving a

percentage of 86.7% which is very good and close to the overall average rate). The 1908 DLA messages were associated to 1665 flights. From those flights, the total number of flights joined with flights from the TMTool: 1451 After removing the DLA messages sent after departure and other relevant factors, the

remaining DLA messages were associated with 1416 flights. Total flights with both DLA and a CDM Warning: 1203.


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[Overview: 2199 1908 1665 1451 1416 1203]

This total number of 1203 flights is the basis for the creation of Figure 15 and thecomparison in Table 12 .

Figure 15 displays points representing the time interval T DLA - T fw against the actual delay.

Input Data elements used:All flights with at least one DLA msg. and a CDM Warning (the 1203 flightsmentioned above)From which dataset: entire period 45 days

Process For each and every flight: Calculate DT = T DLA - T fw (horizontal axis) Calculate Actual Delay (= AOBT SOBT) in minutes (vertical axis) Plot one dot for each flight

Output Figure 15Clarification Definition of Blue line: T DLA = T fw i.e. if the DLA msg. is filed at the moment

the CDM First Warning is raised.

Figure 15: Scatter graph of T DLA T fw against actual delay time


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For the flights (dots) on the right side of the Blue line (i.e. with positive (+) values of theinterval) the TMTool time of first warning was prior to the DLA message time.

In the majority of cases, the TMTool warning values were produced earlier than the time theDLA message was received.

Table 12 shows the percentage where the TMTool warning values were produced earlierthan the time the DLA message was received.

Percentage of flightswith a DLA message sent

beforethe first warning was raised

by the CDM Tool(TDLA < T fw)

Percentage of flightswith the first warning by the CDM Tool

raisedbefore

a DLA message was sent(Tfw < T DLA)

28.1% 71.9%

Table 12: Relative values of T DLA, T fw times


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Figure 16 displays the relative proportions of the values of Table 12 .

Figure 16: Comparison of T DLA and T fw

The CDM Tool warnings were produced earlier than the time the DLA message wasreceived by the CFMU for 71.9 % of the flights.

71.9%


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4.5.3 Comparison of Delayed flights with no DLA message

A significant number of flights were delayed, but had no associated DLA message.

Table 13 depicts the number of delayed flights without DLA messages and those with aCDM Warning (T fw).

ActualDelay(min)

%DelayedFlightswithout

DLAmsg.

%Delayed

Flights witha CDM

Warning

TMTool CFMU TMTool > 10 53.9 82.3

> 15 44.7 73.7

> 25 28.2 84.5

> 60 12.5 84.7

Table 13: Percentage of delayed flights without a DLA msg. &

Percentage of delayed flights with a CDM Warning

As samples from Table 13:

Figure 17-A depicts the relative proportions for flights with an actual delay greaterthan 10 min.

Figure 17-B depicts the relative proportions for flights with an actual delay greaterthan 15 min.

Figure 17-C depicts the relative proportions for flights with an actual delay greaterthan 25 min.


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Figure 17-A: Proportion of delayed flights without a DLA message is 53.9% (left) Proportionof delayed flights with a CDM first warning is 82.3% (right)


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Figure 17-B: Proportion of delayed flights without a DLA message is 44.7% (left) Proportionof delayed flights with a CDM first warning is 73.7% (right)


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Figure 17-C: Proportion of delayed flights without a DLA message is 28.2% (left) Proportionof delayed flights with a CDM first warning is 84.5% (right)


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4.6 ANALYSIS OF CTOT COMPLIANCE

4.6.1 Introduction

Regulated flights are of primary importance for air traffic flow and capacity management(ATFCM). These flights are allocated a calculated take off time slot (CTOT) by CFMU. TheCTOT values are allocated by CFMU to regulated flights with SAM and SRM messages(Slot Allocation Message and Slot Revision Message).

According to the current rule, the take off must take place within a time window of 15minutes, ranging form CTOT-5 min. to CTOT + 10 min. Regulated flights that take offoutside this time window are considered to have missed their CTOT.

Whenever a CTOT is missed, there are two major consequences:

First, this slot is wasted, as no other flight can make use of it.

Second, a flight departing outside its allocated CTOT might affect the total traffic load atcritical en-route sectors, causing over-deliveries and possible overloads.

Timely and proactive action, whenever there is indication that a regulated flight will miss itsCTOT, results in efficiency benefits, from an air transport network managementperspective. The earlier this information is communicated, the easier it is to re-use the slotand possibly benefit another flight. On the other hand, keeping a slot until the very lastminute might result in cascading effects due to rescheduling of other flights, which couldoriginally be unaffected.

4.6.2 CTOT Analysis

4.6.2.1 OverviewDuring the time period of this study (i.e. 01AUG 15SEPT 2004), there was a total of 2497regulated departure flights from Athens.

The following analysis of regulated flights focuses on Athens departure flights only. Flightswith missed CTOT inbound to Athens from other airports are not related to the local Athenssituation and therefore are irrelevant for this study.

4.6.2.2 Missed CTOTBased on the CFMU data provided, the percentage of flights which missed their CTOT was46.5% of all regulated flights.

Figure 18 displays the percentage of regulated flights that missed their allocated CTOT.


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Figure 18: Proportion of regulated departing flights that missed their allocated CTOT (46.5%)

4.6.2.3 CDM alerts for regulated departuresA high percentage of regulated flights were matched between the two data sets ofTMTool and CFMU.

More specific, 89.7% of the regulated flights from CFMU data set were correlated withregulated flights from TMTool. This is a higher percentage than the average as alreadyseen in previous sections.

As mentioned in para. 4.6.2.1, the 46.5% of the regulated departure flights missed theirallocated CTOT. However, after a detailed examination of the regulated departing flights thatmissed their CTOT, it is evident that the TMTool raised alerts for 72%, a significant portionof these flights.

46.5%


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Figure 19: Proportion of regulated flights that missed their allocated CTOT with a CDM alert(72%)

72%


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4.7 CONCLUSIONS

This analysis was able to match 84.6% of the CFMU messages with actual flights from itsTMTool application.

Based on the CDM principles, the analysis of TMTool can be useful in identifying reasonablywell probable delays and providing wake-up calls to action. CDM tools in general andTMTool in particular focus at the level of local operations (at the airport) and therefore areable to identify delay issues more clearly than at higher levels (e.g. network level).

The intelligence that is put into a CDM system clearly identifies significantly more warningflags for delayed flights than current systems. Furthermore, these are identified sooner. Thiscould be understandable, as often the operator may be reluctant to admit to delays.

CDM tools can be complimentary to other systems and interaction with these should beencouraged. Interaction with other systems will increase the orderly flow of information,providing required data for early action.

For example, CDM tools could:

provide aircraft registration number to flight plans, as soon as known. identify multiple flight plans for the same flight. and of course many other uses to be discovered as usual with

technology.

As shown in section 4.4.3.2, CDM tools ability to identify flights and aircraft registrationnumbers could contribute towards a 4% reduction in flight plans and allocated slots.


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5. OVERALL CONCLUSIONS

Even a basic CDM Tool provides reliable warnings for a significant percentage of

delayed flights higher than 75 %.

These warnings are available in the period 60-90 min prior to EOBT.

With such advance and reliable warnings, all airport partners have adequate time to re-

plan / re-schedule their resources and operations, in a way to minimise the

consequences of the delay.


demonstrated in the best possible way. Even a basic CDM Tool provides a system calculated TOBT more reliable than the

SOBT.

Even without an established TOBT procedure for AO/GH inputs, the system calculated

TOBT is the closest to the AOBT available value.

An operational TOBT procedure and the implementation of variable taxi times calculation

will significantly improve the accuracy of the TOBT.

In 71.9% of the cases, the TMTool warning values were produced earlier than the time

the DLA message was received. Many flights were actually delayed without an associated DLA message being sent. A

significant percentage of such flights was early identified by the local CDM tool.

The CDM Tool issued an advanced warning for 72% of the flights that eventually

departed outside their CTOT window.

CDM tools could contribute towards a 4% reduction in flight plans and allocated slots


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ANNEX I

PROCESSING OF LOCAL CDM TOOL DATA METHODOLOGY

The data received consisted of daily files containing the output of TMTool for the period ofAug. 01 to Sept. 15, 2004. The size of each daily file was approximately 50MB, with slightvariations depending on the amount of daily traffic.

The data were in XML text format (see http://www.w3.org/XML ). XML is a widely used textformat - being text based has the advantage that it is easy for a human to read andunderstand. Also there are many utilities and software for processing XML data.

TMTool is a display application that shows the current operational situation and appliesvarious filters to identify probable delays. It is not a storage engine; therefore each display

snapshot was saved. This significantly increased the amount of data as flight informationthat did not change was constantly regenerated. The first step was to reduce the volume ofentries to only those that had a change from the previous state. Each entry line was timestamped, so it is easy to sort entries in sequential order.

Each entry contained information on a complete aircraft rotation. This means that an entrycontains data for both the arrival and the departure legs. The unique entries (i.e. noredundant duplicate entries) of each flight rotation were placed in separate files, based onthe scheduled departure date.

Using these files, further data reduction can be achieved by extracting the arrival, departureand key transient information (such as time and type of first warning, etc.) into a single entry

for each rotation. This single entry per rotation file can readily be transformed into anyformat or placed into a database for analysis and manipulation.

Each analysis / graph / table is accompanied by a standard table, describing themethodology used for the corresponding analysis. A sample of this standard table is thefollowing:

Input Data elements used (select from table in Part B above)From which dataset (e.g. daily, weekly, monthly, entire period 45 days,)If subset, describe how the subset was selectede.g. AOBT, SOBT

Process Compare values < / > / =

Calculate AOBT SOBT and compare if result is > / < / = to 0 (zero)The result belongs to 1 of three possible categories.Output Figure or Table or other as appropriate

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