Preparation of Papers for AIAA Technical Conferences - Pegasus

Analysis of the Validation objectives of the TITAN Concept 

of Operations (Turnaround Integration in Trajectory And 

Network) 

Andrea Villa García 1 

Universidad Politécnica de Madrid- ETSIA,Madrid, Spain 

This paper details the TITAN Concept of Operations Validation objectives analysis. 

TITAN is an advanced turnaround Operational Concept performed as an integral part of 

the aircraft trajectory. It is based on the principles of Collaborative Decision Making (CDM) 

and System Wide Information Management (SWIM). The TITAN validation approach was 

based on the application of the European Operational Concept Validation Methodology(E- 

OCVM). The Validation objectives assessment was performed through the analysis of 

simulation results from a discrete event, extended network queuing simulation model. 

Conclusions obtained from the validation activities show that the TITAN Operational 

Concept is able to deliver the expected performances in terms of efficiency, predictability, 

flexibility and cost-effectiveness. This paper describes the process, the results and the 

analysis performed in the validation. 

Nomenclature 

ACT = Actual Completion Time 

AIRS = Airport Information Report Service 

AOBT = Actual Off-Block Time 

ASRS = Aircraft Status Report Service 

AST = Actual Start Time 

ATM = Air Traffic Management 

ATTT = Actual Turnaround Time 

BFIS = Baggage Flow Information Service 

ECT = Estimated Completion Time 

E-OCVM = European Standard Methodology for Operational Concepts 

EST = Estimated Start Time 

ETTT = Estimated Turnaround Time 

KPA = Key Performance Area 

KPI = Key Performance Indicator 

OBT = Off-Block Time 

PCT = Planned Completion Time 

PFIS = Passenger Flow Information Service 

PST = Planned Start time 

SOBT = Scheduled Off-Block Time 

TITAN = Turnaround Integration in Trajectory And Network 

μ = mean value 

σ = standard deviation 

1 Graduate student, Airports and Air Navigation Systems Department. andrea.villa.garcia@alumnos.upm.es. 

1 

AIAA-Pegasus Student Conference

I. Introduction 

ALIDATION is an iterative process by which the fitness for purpose of a new system or operational concept 

V being developed is established. It can also be understood as the assessment of the candidate solutions against 

the requirements. Validation is a process composed by several parts including the analysis of the Validation 

objectives and the identification of conclusions. 

Turnaround delays cause approximately 4/5 of late departures flights 1 . As part of the project, TITAN* carried 

out an analysis of the current situation for the turnaround process and an identification of the stakeholder´s problems 

and needs 2 . Based on this information, TITAN developed a new operational concept 3 for the turnaround as an 

integral part of the trajectory. TITAN considered the landside processes within the principles of Collaborative 

Decision Making 4 (CDM) and System Wide Information Management 5 (SWIM). The TITAN concept of operations 

also included new information services -called TITAN Information Services- which provide information about 

passengers or baggage location, aircraft status or airport resources. This new operational concept aimed to improve 

turnaround predictability, flexibility, efficiency and cost-effectiveness. 

Validation activities were necessary to assess these goals and to provide operational feedback (enhancements 

and corrections) for the refinement of the operational concept. 

The first TITAN validation phase was based on the application of the European Operational Concept Validation 

Methodology 6 (E-OCVM) and aimed to reach the V1 maturity level. During next phase -V2 phase- the feasibility of 

the concept was established. This paper focuses on the analysis of the Validation objectives corresponding to the V2 

phase. 

V2 validation activities were based on simulation results from a discrete event, extended network queuing 

simulation model (TITAN Model) 7 . This software model was developed by the TITAN Consortium. The TITAN 

Model allowed emulating the processes and resources involved in the turnaround of an aircraft within a realistic 

operational environment. Variation of the parameters ensured the validation of a wide operational context. In the 

course of the simulations, data was recorded to identify the performance parameters. 

The simulation output data was analyzed in detail to assess validation objectives and KPI. The Validation 

objectives assessment was based on comparing the results from a scenario where the TITAN Concept was 

implemented with a baseline scenario (a scenario that represented the current situation). 

Following sections describe TITAN Concept Validation objectives and KPI, output data from the simulations 

and their relationship with the Validation objectives and the analysis of the objectives. Finally, conclusions about 

this analysis are presented. 

II. Validation objectives description 

TITAN consortium defined the following Validation objectives for the V2 phase. 

Exercise Id 

V2-01 

V2-02 

V2-03 

V2-04 

Validation Objectives 

To assess whether the TITAN concept meets the targeted performances levels for predictability, 

efficiency and flexibility. 

To assess the influence of passenger and baggage processes, especially under several unexpected 

situations (e.g. late passenger arrival or lost baggage). 

To assess the effect of disruptions on the performances levels for predictability, efficiency and 

flexibility. 

To validate the application of the TITAN services described in the Operational Concept (BFIS, 

PFIS, AIRS, ASRS). 

To assess whether the TITAN concept meets the targeted performances levels for cost 

effectiveness. 

Table 1. V2 Validation Objectives 

To define each Validation objective, they were specified as exercise objectives. Exercises objectives represent 

lower level validation objectives. They describe the goal of the exercise and the specific scenario where the 

objective needs to be evaluated. Each exercise objective is achieved by simulating one or several Validation 

scenarios. 

*TITAN was a 7th Framework Programme co-financed by the European Commission. Private companies related to the ATM context, Air 

Navigation Services Providers, Research and Development Centers and Universities formed the TITAN Consortium. See www.titan-project.eu 

2 


A Validation scenario is a specific scenario developed for the purposes of undertaking validation activities and to 

gather evidence relevant to the validation objective. Validation scenarios were built using the TITAN Model 

description language. For validation activities, one generic scenario and four specific scenarios (Late passenger, 

Increasing demand, Late arrival and Lack of resources) were defined (see Table 2) . Each Validation scenario was 

repeated varying several numerical parameters. As a result, there were around 260 different scenarios. 

Normal 

Airport 

Operation 

Disruption 

Generic 

Scenario 

Late 

Passenger 

Specific Validation Scenarios 

Increasing 

demand 

Late 

arrival 

Lack of 

resources 

Current situation GEN-1a SPEC-1a SPEC-2a SPEC-3a SPEC-4a 

TITAN concept GEN-1b SPEC-1b SPEC-2b SPEC-3b SPEC-4b 

Current situation GEN-2a SPEC-1c SPEC-2c SPEC-3c SPEC-4c 

TITAN concept GEN-2b SPEC-1d SPEC-2d SPEC-3d SPEC-4d 

Table 2.Validation scenarios 

For each exercise objective, it was necessary to identify the sub-processes* where the objective needed to be 

evaluated and an indicator. After that, a success criteria for each exercise objective was established. Table 3 shows 

an example of some of the exercise objectives. 

Exercise Objective 

V2.01-A. Check whether 

standard deviation of offblock 

time (AOBT- 

SOBT) with TITAN is 

reduced to 3 minutes. 

V2.02-D. Check whether 

variability of processes 

durations decreases with 

TITAN with respect to 

Non TITAN. 

Baseline Scenario/ 

Scenario to be 

evaluated 

GEN1AExe1/ 

GEN1BExe1 

GEN2AExe5&8/ 

GEN2BExe5&8 

Indicator Process Success Criteria 

Total Delay: 

ACT-PCT 

ACT-AST 

Variability of ETTT 

Standard deviation of 

(ACT-AST) - (ECT- 

EST) 

Table 3.Exercise objectives 

Off-Block 

All turnaround subprocesses 

TITAN Standard 

deviation of the Off - 

Block delayed time < 

3 min. 

σ of the turnaround 

sub-processes duration 

with TITAN < σ of 

the turnaround subprocesses 

duration 

non-TITAN 

To perform the analysis and ensure the obtention of coherent and comparable results,TITAN developed a 

Performance Framework 8 that defined the Key Performance Indicators (KPI) . KPI identify what information had to 

be obtained to reach the TITAN performance objectives. These indicators quantitatively described the performance 

of the turnaround process. Table 4 shows the identification of the measurements needed to calculate each metric for 

some KPI. 

KPI Metric Calculation of Metric Measurement 

Variability of ETTT Time Standard deviation of ATTT- 

ETTT 

Off Block punctuality Percentage % of flights compliant with 

AOBT- SOBT 15 min 

*Turnaround sub-processes considered in validation scenarios were: Baggage loading, baggage unloading, boarding, catering check-in, cleaning, 

de-boarding, in-blocks, off-blocks, passport (only for international turnaround), re-fueling and security 

3 


III. Description and reliability of simulation data 

TITAM Model produced a file containing all performance parameters for each simulation. According to the 

analysis of the measurements and the indicators necessary for validation purposes, datasets were selected for each 

sub-process: 

PST: Planned Start time, according to schedule input data and processes definition. 

EST: Estimated –by the TITAN Model- Start Time, according to schedule, planned and updated durations 

of the previous processes. 

AST: Actual Start Time, real start time of a process. 

PCT: Planned Completion Time. 

ECT: Estimated Completion Time. 

ACT: Actual Completion Time. 

Using these output data, following parameters were calculated to assess the exercise objectives: 

ACT-PCT: Delay of the process with regard to the schedule. 

ACT-ECT: Delay of the process with regard to the model estimation. 

ACT-AST: Real duration of the process. 

PCT-PST: Schedule duration of the process. 

(ACT-AST) – (PCT-PST): Difference between the real duration and the schedule one. 

Number of (ACT-PCT)>15min. 

To assess output data reliability a statistical analysis of output data was carried out. Statistical analysis was 

performed over the measurements that served to assess the exercise objectives: 

ACT-PCT: Delay of the process with regard to the schedule. 

ACT-ECT: Delay of the process with regard to the model estimation. 

ACT-AST: Real duration of the process. 

(ACT-AST) – (PCT-PST): Difference between the real duration and the schedule one. 

Percentage of processes in which (ACT-PCT) >15min 

The statistical parameters obtained were: 

Mean of each measurement of every turnaround process: It served to know the central tendency of the 

process measurement (process delay or process duration average). 

Standard deviation (σ) of every turnaround process: It served to know the dispersion of the data 

measurement (dispersion of the processes delays or processes durations). 

Confidence interval (C.I.) of the off block process delay: It served to ensure reliability of the data 

measurement by calculating the interval that contained an established percentage (Confidence level, α) 

of the data measurement. 

Each Simulation scenario was run until the output data allowed obtaining a confidence level of 0.95 for the offblock 

process delay and a confidence interval of 0.33 minutes (20 seconds). That means that 95% of the flights had a 

delay in off block process within the interval of ±20 seconds around the mean of the delays of all off blockprocesses. 

Following tables show the statistical data obtained for each simulation run. Numeric values correspond to GEN- 

1a scenario. 

4 


Process 

ACT-PCT 

Average 

ACT-ECT 

Average 

ACT-AST 

Average 

(duration 

average) 

(ACT-AST) 

– (PCT- 

PST) 

Average 

(duration 

deviation 

average) 

Number of 

processes in 

which 

(ACT- 

PCT)>15min 

Percentage 

of processes 

in wich 

(ACT- 

PCT)>15min 

Boarding 0,31 -1,94 21,80 -1,20 135 8 % 

Security -31,60 -30,84 88,40 -31,60 0 0 % 

Baggage Unloading -2,21 -3,67 10,64 -5,23 74 4 % 

Table 5.Calculated means for GEN-1a scenario. 

Process 

ACT-PCT 

Standard deviation 

ACT-ECT 


ACT-AST 


(duration standard 

deviation) 

(ACT-AST)– 

(PCT-PST) 


(duration deviation 

standard deviation) 

Boarding 10,52 10,00 7,66 7,66 

Security 6,38 7,85 6,38 6,38 

Baggage Unloading 8,62 5,62 2,42 3,04 

Table 6.Calculated standard deviation for GEN-1a scenario. 

Finally, the confidence interval was calculated according to the number of measurements. For the presented 

example (GEN1AExe1) five simulations were run (each simulation contains 339 flights), and therefore the results 

had 339*5=1695 measurements of the delay in off-block. The average of the off- block delay was 3,44 minutes, 

with a standard deviation of 8,09 minutes and a confidence interval of 23 seconds. In other words, off -block delay 

of 95% of the flights was within the interval 3,44 ± 0,39 minutes (03:05 minutes, 03:50 minutes). 

All the samples analysed which take into account time (ACT-PCT, ACT-ECT...) vs. number/ percentage of 

flights behaved as shown in Figure 1. 

Figure 1.Difference between ACT and PCT of off-block sub-process 

Using statistical software*, an analysis of the samples was performed. The distribution that best fit the samples 

whose behaviour corresponds to the previous figure was the Burr distribution. The Burr distribution is a rightskewed 

distribution with a flexible shape and controllable scale and location. It is frequently considered as an 

alternative to a Normal distribution when the data shows slight positive skewness.Figure 2 represents the statistical 

analysis of the previous general sample. Variable x represents the difference between ACT and PCT of OBT 

(minutes) and f(x) is the probability density function corresponding to the Burr distribution. 

* Statistical software used was EasyFit (http://www.mathwave.com/) 

5 


Figure 2.Burr distribution 

Exercise analysis was based on the comparison of the performances obtained in TITAN and non TITAN 

Simulation scenarios. In order to compare different simulation scenarios and indicators according to the validation 

objectives, it was necessary to choose one measure of central tendency (mean value, µ) and one measure of 

dispersion (standard deviation, σ). 

IV. Results analysis 

A global overview of the results could be obtained from histograms which represented the off-block delay 

performed against the percentage of flights. Figure 3 shows an example of the histograms comparing TITAN and 

Non TITAN for two different scenarios; Scenarios “A” are Non TITAN scenarios and scenarios “B” are TITAN 

scenarios. 

Figure 3.TITAN and Non TITAN histograms 

TITAN simulations always got fewer delayed flights( in the exaplaxes showed, 2,5% of delayed flighs vs 11,4% 

for Normal Airport Operation scenario and 9% of delayed flights vs 15,2% for Lack of Resources scenario) 

A. Validation objectives criteria 

Each exercise objective was evaluated according to the criteria shown in Table 7. 

6 


Result 

Passed 

Passed with conditions 

Not passed 

Conditions 

Success criteria is fulfilled for all sub-processes 

Severity 

H (High) 

M (Medium) 

L (Low) 

If it affects at least Off-block. 

If it affects at least Boarding subprocess. 

If affects any other sub-process 

different from Boarding or Offblock. 

None of the sub-processes fulfill the success criteria 

Table 7.Success criteria 

Severity level “High” was assigned to off-block sub-process because it is the last turnaround sub-process and it 

indicates if the flight is delayed. The boarding sub-process has a higher impact on off-block delay than the rest of 

sub-processes, thus severity level “Medium” was assigned to it. 

Each exercise objective was analyzed according to these criteria. Table 8 shows the analysis of one of the 

exercise objectives*. 

Exercise Objective V2-01.E 

Check whether number of delayed flights [(AOBT-SOBT)>15min] decreases by 9% with TITAN with respect to 

Non TITAN 

Simulation Scenario ID 

GEN1AExe1 (Non TITAN) // GEN1BExe1 (TITAN) 

Sub-process 

Offblock 

Measurement 

% of flights which : 

ACT OBT -PCT OBT >15 min 

Indicator 

% of delayed flights with TITAN < % 

delayed flights with non TITAN-9% 

Simulation Scenario ID % of delayed flights (delay>15 minutes) Is % of delayed flights with 

TITAN < % delayed flights 

with non TITAN-9%? 

GEN1AExe1 7,9 % 

GEN1BExe1 1,4 % 

 

Result 

Comments 

Passed Number of delayed flights more than 15 minutes is reduced by 82,2%. 

Table 8.V2-01.E Exercise objective analysis. 

*Full details are presented in WP3_DEL.3.3_Validation Report. See reference 9. 

7 


B. Validation objectives assessment 

1. Validation objective V2-01 

Exercise V2-01 assessed the efficiency, predictability and flexibility performances of the TITAN concept under 

normal airport operation. Four exercise objectives were passed, three were passed with conditions and two 

objectives were not passed. The global conclusion for Exercise V2-01 could be considered as reached with 

conditions. 

The first not passed objective entended to reduce the standard deviation of off-block time to 3 minutes. Even 

though the objective was not reached, σ was reduced by 57,2% (standard deviation was reduced from 8,1 to 

3,5 minutes). The TITAN concept managed to reduce σ, although the initial objective was not reached. 

The second of the not passed objectives was focused on checking when efficiency and predictability 

performances were maintained under unexpected events. The objective was not reached because the 

TITAN model worked in a different way depending on the unexpected event when comparing TITAN 

scenarios. Efficiency and predictability performances were maintained according to the success criteria 

only when lost passenger or increasing demand scenarios were analyzed. 

Objectives related to reducing the percentage of delayed flights or sub-processes and turnaround duration 

were reached when comparing TITAN to non TITAN scenarios. Efficiency and predictability performances 

were maintained when an unexpected event occurred when comparing TITAN to non TITAN scenarios. 

Deviation between the estimated and actual end times of sub-processes was only reduced in boarding and 

off-block processes when introducing the TITAN concept. However, the mean value for these objective 

measurements improved for almost all processes. Standard deviation for the off-block process was reduced 

from 8 to 7,38 minutes. 

Deviation between the scheduled and actual duration time of sub-processes was not reduced for most 

processes due to decisions taken at TITAN decision points *. However, standard deviation for the off-block 

remained unchanged. Variability of sub process duration was only reduced for the boarding sub-process 

from 7,66 to 6,08 minutes. 


Exercise V2-02 dealt with the assessment of the impact on turnaround efficiency, predictability and flexibility 

performances of disruptions** in a TITAN environment. Five exercise objectives were passed, three were passed 

with conditions and two objectives were not passed. Exercise V2-02 could be considered as reached with conditions. 

The first not passed objective was based on reducing standard deviation of off-block time to 3 minutes. 

Despite that the objective was not reached, standard deviation was reduced when different disruptions were 

analyzed.For re-fuelling disruption standard deviation was reduced from 11,5 to 6,09 minutes and for 

baggage unloading disruption it was reduced from 10,39 to 6,51 minutes. 

The second not passed objective was focused on checking the isolated effect of a disruption in the TITAN 

concept when an unexpected event occurred. The global objective was not reached. However, standard 

deviation of the sub-processes duration with TITAN was maintained except for the in-block sub-process. 

The objective of maintaining average and standard deviation of the delay time sub-processes was only 

reached for some sub-processes when comparing TITAN scenarios. In-block values are maintained in all 

scenarios. Delay average and standard deviation for baggage loading, unloading and boarding sub-process 

were maintained only for Late arrival scenario. 

Objectives related to reducing the number of delayed flights or sub-processes and turnaround durations were 

reached when comparing TITAN to their corresponding non TITAN scenarios. Efficiency and 

predictability performances were maintained under unexpected events. The effect of an unexpected event 

on efficiency and predictability performances was maintained under success criteria comparing different 

TITAN scenarios. 

* During TITAN simulations, a set of common decisions were followed when an alert window appeared according with the 

information provided by the TITAN Services. 

** A disruption is a forced delay in one of the sub-processess. 

8 


The rest of the objectives were passed with conditions. The first one was focused on reducing deviation between 

the estimated and actual end time of sub-processes. Deviation between the estimated and actual end times of 

processes increased for most sub-processes but off-block standard deviation was reduced( from 9,90 to 9,36 

minutes).Mean values of the deviation between the estimated and actual end times of processes were reduced 

for most sub-processes. The second objective was focused on reducing the deviation between the scheduled and 

actual duration of sub-processes. Deviation between the scheduled and actual duration showed different 

behaviour for each sub-process. Mean value deviation only decreased for baggage unloading and boarding subprocesses. 

Variability of process duration was only reduced for the baggage unloading sub-process. The last 

objective was based on reducing the variability of sub-processes durations. The objective was only reached for 

baggage unloading sub-process. 


Exercise V2-03 aimed to validate the application of the TITAN Information Services (BFIS, PFIS, AIRS and 

ASRS). The Exercise V2-03 global objective could be considered as reached. 

Turnaround durations and variability decreased with TITAN with respect to non TITAN for all the situations 

analyzed. BFIS and PFIS resulted the most effective TITAN Services. Turnaround duration and its 

variability showed the lowest values when these services were implemented. Results applying AIRS and 

ASRS were better than Non TITAN results. However, there were no significant differences between them. 

Percentage of delayed flights decreased by using TITAN Services. Results using only BFIS and PFIS were 

quite similar to the ones obtained when all TITAN services were implemented. They provided a reduction 

of 94,6% of delayed flights. By applying only AIRS and ASRS, reduction of the percentage of delayed 

flights was 38,6%. Boarding and off-block sub-processes showed more differences when different TITAN 

Services were implemented. For these processes, the delay mean value and standard deviation were greater 

when only AIRS and ASRS were implemented. 

There were not many differences between scenarios when comparing delayed time of sub-processes. 

Differences in boarding were due to the decisions taken during the TITAN simulation. Mean values of subprocesses 

duration were quite similar and standard deviation values were reduced when TITAN services 

were implemented( standard deviation was reduced from 7,22 to 5,46 min. for baggage loading and from 

15,27 to 6,01for boarding process when all TITAN Services are implemented) . Results were similar when 

all TITAN Services were implemented and when only some of TITAN Services were implemented. 


Exercise objectives V2-04 dealt with assessing cost effectiveness. This exercise checked if certain level of 

turnaround efficiency (assessed through delays) could be maintained under normal airport operation while reducing 

the number of resources. V2-04 objective compared two TITAN scenarios, one of them with a reduction of airport 

resources. The global objective V2-04 was not reached. It meant that TITAN Concept of Operations didn’t make up 

for resources reduction. 

Standard deviation of the turnaround sub-processes delay time with TITAN was not maintained for any process. 

The percentage of delayed flights was not maintained when reducing the number of resources by a 20%; it increased 

by 79,7%. 

C. KPI analysis 

Next table shows some of the KPI analyzed for V2-01 Validation objective. This analysis was done for all KPI 

of each Validation objective 9 . 

9 


Measurement // 

(unit) 

Non 

TITAN 

TITAN 

Non TITAN 

TITAN 

Non 

TITAN 

TITAN 

Non 

TITAN 

TITAN 

Non 

TITAN 

TITAN 

Normal 

operation 

Late 

passenger 

Increasing 

demand 

Late arrival 

Lack 

resources 

of 

KPI 

Total Delay (Off- 

Block) 

Accommodate 

scheduled 

changes 

without 

increasing 

delays 

ACT-PCT // 

(minutes) 

ACT-ECT // 

(minutes) 

3,44 0,79 14,89 1,71 7,23 0,84 6,09 3,25 4,54 2,70 

1,18 -1,4 12,63 -0,55 4,60 -1,81 -0,51 -3,41 2,28 -1,04 

Table 9.KPI results 

Global results from the four validation exercises KPI are showed below: 

Variability of ETTT: Reduced with TITAN.(From 3,95 to 0,84 for V2-01 objective and Increasing demand 

scenario) 

Off Block punctuality: Increased with TITAN.( From 54,69 to 97,57 for Late passenger scenario and V2-03 

objective) 

Total Delay (Off-Block): Reduced with TITAN.( From 7,36 to 3,40 for a disruption in fuelling for V2-03 

objective) 

Accommodate scheduled changes without increasing delays: Reduced with TITAN. .( From 5,10 to 1,13 for 

a disruption in fuelling for V2-03 objective) 

Recovery delay factor when lost passenger: Increased with TITAN. ( A 34,65% for V2-02 objective) 

Recovery delay factor upon gate reallocation: Increased with TITAN. ( A 54,91% for V2-04 objective) 

Recovery delay factor when unavailability of a service is detected: Increased with TITAN. ( A 73,56% for 

V2-04 objective) 

V. Conclusion 

The main conclusions for validation activities can be summarized as follows: 

As a global result, 2 exercises could be considered as passed with conditions, 1 exercise passed and 1 

exercise not passed. 

o 

o 

o 

o 

Percentage of delayed flights, mean values of sub-processes delay, number of delayed subprocesses 

and turnaround durations were always improved when introducing TITAN Concept of 

Operations. 

Standard deviation value of OBT (ACT-PCT) was always reduced when comparing TITAN 

simulation scenario to its corresponding Non TITAN simulation scenario although the objective to 

achieve 3 minutes was not reached. 

Standard deviation of estimated indicators was worse than standard deviation of actual indicators 

for the same simulation scenario. 

Not passed objectives were focused on evaluating if performances were maintained under different 

situations comparing different TITAN simulation scenarios. Some of them could be considered as 

passed using less stringent success criteria. 

10 


o 

TITAN Services BFIS and PFIS resulted to be more effective than AIRS and ASRS despite AIRS 

and ASRS provided more information. 

All KPIs analysed for each Validation objective were improved when TITAN was implemented. 

Regarding the four KPA considered for TITAN project, the following conclusions can be obtained: 

Predictability increased when TITAN Concept of Operations was implemented. However, the 

performance objective based on reducing the standard deviation up to 3 minutes was not reached. 

 

 

 

Efficiency increased when TITAN was implemented due to the reduction of delayed flights by 

9%. The number of delayed flights decreased even more than a 9%. 

Flexibility increased when TITAN was implemented due to better compliance with Efficiency and 

Predictability in case of unexpected events. 

Cost effectiveness regarding operational costs reduction was not directly assessed. However, an 

improvement in predictability, efficiency and flexibility should result in a cost-effectiveness 

indirect improvement. 

Validation activities of the V2 phase finalized with the analysis and conclusions summarized above. TITAN 

Consortium considered that the operational concept accomplished the expectations. Next phases of the TITAN 

project, including a Decision Tool for airlines and a Cost Benefit Analysis, went on after the end of validation 

activities. 

Acknowledgments 

The author would like to acknowledge the colleagues from CRIDA, specially to N.Suárez,E.Puntero and 

I.Navarro for revision and support; and all partners from the TITAN Consortium for the excellent work carried out. 

References 

1 EUROCONTROL.Performance Review Report.2006. http://www.eurocontrol.int/ 

2 TITAN Consortium, WP1_DEL.1.1_Analysis of the current situation.May 2010. http://www.titanproject.eu/library/titan/TITAN_WP1_SLO_DEL_01_v1.0_Analysis_of_the_current_situation.pdf 

3 TITAN Consortium, WP1_DEL.1.4_TITAN Operation Concept document( Issue 1&2).November 2012. http://www.titanproject.eu/library/titan/TITAN_WP1_INE_DEL_04_v1.0_TITAN_Operational_Concept_Issue2.pdf 

4 EUROCONTROL, “Airport CDM Implementation. The Manual” V3.1. July 2010. http://www.eurocdm.org/library/cdm_ocd.pdf 

5 

EUROCONTROL SJU SWIM. http://www.sesarju.eu/programme/highlights/swim-atm-intranet 

6 EUROCONTROL, The European Operational Concept Validation Methodology( E-OCVM), V3.February 2010. 

http://www.eurocontrol.int/valfor/gallery/content/public/docs/E-OCVM3%20Vol%20II%20WebRelease.pdf 

7 TITAN Consortium, WP2_DEL.2.7_TITAN executable model. http://www.titan-project.eu/index.php/library 

8 TITAN Consortium, WP1_DEL.1.3_Performance Framework.October 2010. http://www.titanproject.eu/library/titan/TITAN_WP1_ISD_DEL_03_v1.0_Performance_framework.pdf 

9 TITAN Consortium, WP3_DEL.3.3_Validation Report.July 2012. http://www.titanproject.eu/library/titan/TITAN_WP3_AEN_DEL_04_v1.0_Validation_Report.pdf 

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