Isi Analysis Of Arrival Procedure On Terminal Airspace Using Simulation ModelRcmeaeok

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AE-14

The th AUN/SEED-Net Regional Conference on Mechanical and Aerospace or ... ..lil Jo .

Bangkok, February 2

Analysis

Of

Arrival Procedure On Terminal Airspace Using

Simulation

odel

Mahardi Sadono

Rully

Mediante

*Researcher in Aircraft Design, Maintenance, and Operation Research Group

Lecturer

in

Faculty

of

Mechanical and Aerospace Engineering

lnstitut Teknologi Bandung, Indonesia

Email: [email protected]

Abstract:

Efforts to increase the capacity of the airport of course must

be

coupled with a

analysis

of

their impact on flight safety. We need any method

that

capable

to

intended for

analyzing the development of arrival procedures at terminal airspace.This paper presents t e

development

of

a simulation model

that

can be

used to analyze the arrival proced ure

terminal airspace. The

model is built using the concept of discrete event-based s i m u

model, and it

is

used

to

analyze the arrival procedures for instrument flight called Standar

Terminal Arrival Routes STAR) and the

case

is a

STAR

at Soekarno-Hatta International Airpor:

The

simulation model is buil t using ARENA I

in

the form of a stochastic model which is expectec

to mimic the characteristics of a real air traffic. One measure

that

determines the leve l

aviation safety in the analysis

is

the dynamic complexity

of

air traffic. Analysis was performec

on several simulation scenarios such as the use

of

a different runway configurations. Ana l

of the available airspace capacity also

has

been carried out. The results showed that

analysis of flight procedures can

be

performed using a discrete event-based simulation moce

The results

of

this analysis can

be

used as consideration in airport management plann ing

improve aviation safety and airport capacity.

Keywords: arrival procedures, terminal airspace, simulation model, air tra

complexity.


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Regional Conference on Mechanical and Aerospace Technology

Bangkok, February 12 13, 2013

Analysis Of Arrival Procedure On Terminal Airspace Using

Simulation Model

Mahardi Sadono* & Rully Medianto

*Researcher in Aircraft Design, Maintenance, and Operation Research GroupLecturer in Faculty of Mechanical and Aerospace Engineering

Institut Teknologi Bandung, Indonesia

Email: [email protected]

Abstract : Efforts to increase the capacity of the airport of course must be coupled withan analysis of their impact on flight safety. We need any method that capable to intendedfor analyzing the development of arrival procedures at terminal airspace.This paper

presents the development of a simulation model that can be used to analyze the arrival

procedure of terminal airspace. The model is built using the concept of discrete event-

based simulation model, and it is used to analyze the arrival procedures for instrument

flight called Standard Terminal Arrival Routes (STAR) and the case is a STAR at

Soekarno-Hatta International Airport. The simulation model is built using ARENA in

the form of a stochastic model which is expected to mimic the characteristics of a real air

traffic. One measure that determines the level of aviation safety in the analysis is the

dynamic complexity of air traffic. Analysis was performed on several simulation scenarios

such as the use of a different runway configurations. Analysis of the available airspace

capacity also has been carried out. The results showed that the analysis of flightprocedures can be performed using a discrete event-based simulation model. The results of

this analysis can be used as consideration in airport management planning to improve

aviation safety and airport capacity.

Keywords : arrival procedures, terminal airspace, simulation model, air trafficcomplexity

1. Introduction

Demand for air transport in the Asia Pacificregion specifically in Indonesia experienced anaverage growth of over 10% in the last five years.

The number of average passenger movementgrowth in Indonesia reached 12.4% per year in the

past five years with passenger numbers more than58 million passengers in 2010. While the growth

of the movement of its airplane reached an

average of 10.2% per year with its airplanemovements of more than 460 thousand in 2010[1].

High growth must be balanced with increasedservice air transportation system adequately. Thisincrease was mainly done by increasing airport

service which is the primary node of the air

transportation system. The addition will increasethe comfort of users of air transport, improvement

of services will also provide higher aviation safety

and security

The efforts include the development of flightprocedures carried out by the renewal arrivalprocedures to fly instruments or better known as

the Standard Terminal Arrival Route (STAR).

One example is a STAR renewal as practiced bythe Soekarno-Hatta International Airport which

improved conventional STAR to RNAV (AreaNavigation) STAR. Updates are intended to

improve the efficiency and effectiveness ofterminal airspace.

This study will focus on efforts to develop a

simulation model to analyze the terminal airspace

specifically STAR flight procedures. Simulationmodels are constructed in the form of discrete-

event simulation model. The analysis is mainlyfocused on the complexity of air traffic andairspace capacity of the airport terminal.

Soekarno-Hatta International Airport is used as

the case in this study because it has some uniquecharacteristics compared to other airports in

Indonesia. Soekarno-Hatta International Airport
mailto:[email protected]:[email protected]


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have two parallel runways and have the busiesttraffic in Indonesia

2. Terminal Airspace Simulation

Model

2.1

Airport Air

Side Simulation Model

Several studies have been conducted in order toimprove airport services, such as policy analysisand cost-benefit assessment. Most of these studies

used the airport as a means of study raisedmodels, either analytical models or simulation

models. With increasingly sophisticated

computing technology, simulation model a top

choice today because of some advantagescompared to the analytical model. The simulation

model currently plays an important role in the

study of the airport, even in the initial design of

the airport [2].An airport simulation model based on the level of

detail can be classified into macroscopic andmicroscopic simulation models. In macroscopic

models, elements of the system in general are

described using a probabilistic model for instanceas a normal distribution model. Instead,microscopic models representing individual

aircraft movements and conflicts with other

aircraft based on individual aircraft performance[3]. In between these two types of models are mid

models of the mesoscopic models [4] and Ceno-model introduced by Carr [5]. Most of the airport

simulation model is built based on the discrete-event simulation model in which its state changes

occur on discrete times [6].

2.2

Terminal Airspace

Terminal airspace is transitional link airspacebetween the airport and en route sector. The sizeand shape depend on the number andconfiguration of the runway, airways

configuration and the number and length of arrival

and departure trajectories. Airspace formed fromseveral convergence arrival trajectories to the

airport and divergence departure trajectories thatspread out from the airport. The point of entry /exit to / from is determined with radio-navigation

aids. These points usually also function as a

holding point.

Air traffic in terminal airspace controlled by oneof the following [7], [8]:

- Trajectories are determined by the direction,distance and height of the navigation aids. Itis often called the STAR (Standard Terminal

Arrival) and SID (Standard InstrumentDeparture).

- Using Area Navigation (RNAV) and

Required Navigation Performance (RNP)methods, which will define 2D, 3D and 4D

trajectory.

- Radar vectoring from ATC, that will provideinstructions contain a reference vector in the

form of direction, point and altitudes.

Terminal airspace is a system with high

complexity and highly sensitive to changes intraffic, meteorology, technical procedures,

administration and others. The measurementsystem condition becomes an important issuewhich is largely dependent on the complexity of

the system [9].

2.3Air Traffic Complexity

Complexity is a measure of the difficulty of

specific traffic conditions that must be controlledby ATC personnel. Air traffic complexity has an

important role in safety and affects for several

other aspects such as the environment, flightdelays, operating costs and service quality of

airline service users [8]. Previous research shows

that the number of aircraft and potential conflicts,a number of hand-offs, heading and speed

variation between two or more aircraft, aircraft

proximity to each other, and presence of weather

affect complexity [10].

Terminal airspace has the highest level ofcomplexity compared to other sectors. This is

understandable because the terminal airspace haveseveral trajectories that converge towards the

airport arrival and departure trajectories thatdiverge leaving the airport. This is compoundedby variations in the speed of the aircraft

depending on the type. Changes in the runway

configuration and weather add to the complexity

of this system.

The complexity has two basic parts that need

attention, static and dynamic part [8]. Static part is

determined by the geometry of the terminalairspace (shape and dimensions), the number ofairports, the number and length of arrivals and

departures routes and the number of entry and exit

points. Dynamic part is determined by thecharacteristics of air traffic (traffic distribution of

arrival and departure, a mixture of aircraft, etc.)

and the distribution of air traffic in the terminalarea (distribution of traffic on the routes, the rulesof separation between aircraft, etc.).


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3. Methodology of Modelling

3.1Concept of Modeling

One of the important and challenging step in the

modeling is to determine the detail level ofabstraction without making the model toocomplex [11]. The level of the model that

developed in this study has a more detailed thanmesoscopic and macroscopic models but stillbelow microscopic models. Macroscopic models

of terminal airspace generally are built with just a

simple queuing equation modeling an arrival routefrom the starting point of arrival (arrival fix) to

the runway without modeling the arrival routegeometry accurately. Characteristics of arrival

flight approached by the inter-arrival timedistribution and service time use historical data

taken from actual operation. The parameters ofmodel usually not be relevant anymore for adifferent flight traffic volume or flight procedures.

On the other hand, the model in the form of

microscopic simulation models based ontrajectory complex. Terminal airspace geometryroute modeled accurately and use the propagation

model aircraft with high enough accuracy to

produce deterministic propagation during flight interminal airspace. Special algorithm use to predict

the trajectory, the possibility of conflict andgenerate a tactical maneuver to avoid such

conflicts. Tactical control mechanisms such asdirection and speed settings to maintain separation

rules should be explicitly included in the

trajectory equation [12].

In the middle position between the macroscopic

and microscopic models are mesoscopic models

such as those developed by Monish et al.[12]. Themodel uses a queue abstraction for modeling thetactical control mechanisms to guarantee

separation. In actual conditions, guaranteesseparation is done by vectoring trailing aircraft, so

it does not go beyond a predetermined separation.Arrival route is divided into several smallersegments that each segment is called a server,

with each server is determined along theseparation. Separation is guaranteed by applying

the rule that only an aircraft is in the server at a

time. Follower aircraft has to wait in queue until

leader aircraft has finished service at server.Briefly, waiting in the queue is an abstraction of

vectoring and waiting time in the queue is equal to

the delay caused by vectoring [12]. Figure 1provides an overview of the position of each of

the models that have been mentioned in the

terminal airspace model spectrum.

The model built in this study is slightly different

from the mesoscopic model belongs to Monish et

al. In this study, terminal airspace landing routesare divided into several smaller

segments that each segment is the same length orsmaller / larger than separation rules. The speed

reduction is an abstraction of vectoring andadditional travel time are equal to the delaycaused by vectoring. Travel time that model of

each segment is approximated by a certainstatistical distribution to approximate the

characteristics of the actual system operation.

In Figure 2, an aircraft P1 followed by aircraft P2

on the same route. When P1 has not left SegmentA, P2 will undergo Segment B with velocity

smaller than the A1. In other words, the travel

time of P2 on the Segment B will be longer thanthe travel time of P1 on the Segment A. With this

rule, the separation between P1 and P2 will be

always assured. Model of separation is also

applied to represent the separation between thetwo aircraft that has a different flight trajectory

but in the same direction (merging). If there is P1to Segment A, then P2 will undergo Segment Cwith a speed that is smaller than P1.

Figure 1. Spectrum of Terminal Airspace Model (Adapted from [12])


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Figure 2. Model of Separation Between Aircraft

In addition, it is also necessary to model the

holding point. The actual operating of holdingpoint is usually found at the meeting point of

several flight paths and entry points. Holdingpoint is provided to maintain the separation

between aircraft, waiting for better weather (in

case of severe weather) and wait their turn to userunway. Holding point is approached using the

principles of queuing models with First Come-First Served (FCFS). Holding aircraft must

undergo a full cycle to complete before exiting theholding point.

3.2

Model Assumption

A model can not perfectly match the actualsystem. This is due to some assumptions muchsimpler than the real conditions. These

assumptions need to be taken given the limitations

of the data held and modeling capabilities of thesoftware. Here are some of the assumptions usedin the construction of terminal airspace simulation

model:

- The simulation model is discrete-timesimulation so as the time change system

conditions change.

- Modeling based on the movement of aircrafton the segment, not the propagation of the

aircraft itself so that inconsistencies can occur

at the speed of each segment.

- Modeling only within the scope of the arrival

operation only and are not affected by thedeparture operations.

- Aircraft moves according to a predeterminedarrival route.

- Aircraft Type is only divided into two,Medium and Heavy type.

- Travel time aircraft in each segment

approximated by a normal distribution.

- There is no weather disturbance duringaircraft moving on routes.

- The communication between ATC and the

pilot is not modeled.

3.3

A brief description of ARENA

ARENA is a simulation modeling softwaredeveloped by Rockwell Automation. ARENA was

first introduced by Systems Modeling Corporationin 1993 with the ability to build models in a

variety of application areas. SIMAN simulation

language became the basis of the development of

ARENA. ARENA can be used to build a model ofcontinuous or discrete [11]. ARENA simulationmodel constructed in easily with the help of

graphics modules. The model of a process flowdiagram described in the next simulated with

ARENA. It also provides a model of two-

dimensional animation. With this capability, the

process of debugging and verification of themodel will be easier. Visual models will help the

understanding of the system as a whole so that the

analysis and decision-making will be easier.

4. Process of Modeling

Construction of terminal airspace models in this

study was based on Soekarno-Hatta InternationalAirports STAR R-NAV 1. There are five entry

points; CARLI, BUNIK, DENDY, and GAPRI.Aircraft as entities will leave the system throughfour points, the threshold of Runway 07R, 07L

25R and 25L. The system also determined eightholding points consisting of four entry points plus

two other holding points that are ESALA, andNOKTA. NOKTA is also the meeting point ofseveral routes (merging point).

There are other points are called fixed points fordetermining the arrival path. All these points are

then connected by lines which became the arrivalroutes. There are 31 sections formed by thesepoints. At the end of the section point, there is a

maximum speed limit must be observed by

aircraft undergoing routes. Figure 3 shows

sections that make up the model.Accordance the concept of modeling that has been

presented in the previous section, sections then

divided into several smaller segments. Thesesegments have an average length of 5 NM. It istaken as a limit of minimal separation between

aircraft in terminal airspace. However, there are

some segments that are more/ less than 5 NM.This is because not all sections divisible by 5 NM.

If the rest of the division is less than 1 NM it will


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be added to the last segment of the section, ifmore than 1 NM then it will be segment apart.

Figure 3. Terminal Airspace Model

Several sections in the model deserve attentionbecause it has a different separation rule. Others

different section separation are BINAM-

THRESHOLD 07L, SURYA-THRESHOLD 07R,LOMBA-THRESHOLD 25L and PILAR -

THRESHOLD 25R. They are within range of ILS

localizer, so must comply with the rules 6 NMseparation between entities on the same Runway

and 3 NM separation for aircrafts on parallel ILS

approach [13]. The section will be formed with

the length of each segment is 3 NM.

Like the section established by those segments,the maximum speed is also determined for each of

the segments. Coverage speed segment will be thebasis in determining the speed as the model

parameters.

5. Implementation of The

Simulation Model

5.1

Measuring Capacity

Knowing the capacity of terminal airspace is quite

important, especially in the face of the continued

increase in demand for the services of Air TrafficControl (ATC). Currently, the sector capacity isusually expressed as the instantaneous maximum

number of aircraft in a sector. The maximumcapacity varies between sectors and in different

traffic situations [14]. The maximum capacity is

obtained by running the simulation for an extremeinter-arrival time. From the simulation, we canalso obtain the maximum number of aircraft reach

runway threshold to determine runway capacityfor the arrival operation only.

5.2Measuring Dynamic Complexity

In general, based on several studies that have beendone, there are many factors that determine the

complexity of air traffic [15]. In this study, not allof those factors used in determining thecomplexity. Only the dynamic parts get attention.

Dynamic complexity with the assumption of good

meteorological conditions and no human factorsinfluence (air traffic controller) can be expressedas follows [16]:

=+ (1)

DC - Dynamic Complexity

TD - Traffic Density

C - Complexity FactorWd - Factor Weight

Specified air traffic complexity factor has the

same weight as well as the traffic density :

=1, (2)

The dynamic complexity becomes :

=+ (3)

Dynamic complexity in this study is solely

determined by factors such as described :

Traffic density TD, number of aircraft are

already inside the system. Aircraft areconsidered to be in the system from the

moment they appear at the entry point to themoment arriving at the runway threshold.

Number of vectoring Nv, number of

vectoring aircraft in order to avoid a violation

of the separation.

Number of holding Nh, number of holding

aircraft for sequencing and spacing.

Then the terminal airspace dynamic complexity

will be calculated in the following way :

= + + (4)

5.3

Simulation Experiments

In order to determine the capacity and dynamiccomplexity of terminal airspace, we performedseveral simulations by setting the parameters

considered will affect the complexity.

1.Runway in use : 07R-07L, 25R-25L.


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2.

Inter-arrival time (uniform with 10%deviation, in minutes) : 0.5, 1.0, 1.5, 2.0.

3.

Ratio possibilities on which entry pointaircraft will arrive (BUNIK : DENDY :CARLI : GASPA : INDRAMAYU) : 1:1:1:3,

2:2:2:3, 1:1:1:6.

Aircraft mix is set to 90% Medium type and 10%Heavy type for all simulations.

6. Results and Analysis

The model was run for 10 statistically

independent replications for a set simulation. Eachreplication has a length of 100 minutes with a

warm-up period 40 minutes. Simulation resultswill also be shown to see the comparison betweenthe different inter-arrival time.

A. Runway in use : 07R-07L

From simulation in under extreme condition (IAT: 0.5 minutes), we get result that terminal airspace

capacity for arrival operation is 66 aircrafts.Figure 4 shows values of terminal airspace

dynamic complexity for typical inter-arrival time.

Figure 4. Dynamic Complexity for Typical Inter-

Arrival Time, Runway in Use : 07R-07L

Table 1 summarizes the simulation results from

different inter-arrival time and ratio possibilities

on which entry point aircraft will arrive.

Inter-Arrival

Time(

minutes,

1

0%)

Ratio Possibilities on Which Entry

Point Aircraft Will Arrival

1:1:1:3 2:2:2:3 1:1:1:6

Min.

Avg.

Max.

Min.

Avg.

Max.

Min.

Avg.

Max.

0.5 78 104 122 89 107 121 68 94 118

1.0 50 73 93 49 70 94 42 61 83

1.5 27 39 50 27 42 60 29 41 55

2.0 16 23 32 16 25 36 17 24 34

Table 1.Dynamic Complexity, Runway in Use :

07R-07L

B. Runway in use : 25R-25L

For the runway in use : 25R-25L, we get result

that terminal airspace capacity for arrival

operation is 56 aircrafts. Figure 5 shows terminalairspace dynamic complexity for typical inter-

arrival time.

Figure 5. Dynamic Complexity for Typical Inter-

Arrival Time, Runway in Use : 25R-25L

Table 2 summarizes the simulation results fromdifferent inter-arrival time and ratio possibilities

on which entry point aircraft will arrive.

Inter-Arrival

Time(minutes,

10%)

Ratio Possibilities on Which EntryPoint Aircraft Will Arrival

1:1:1:3 2:2:2:3 1:1:1:6

Min.

Avg.

Max.

Min.

Avg.

Max.

Min.

Avg.

Max.

0.5 70 88 105 76 92 104 53 80 100

1.0 49 68 95 45 64 90 39 52 72

1.5 22 31 41 24 36 52 24 36 492.0 13 19 25 13 22 33 12 20 30

Table 2.Dynamic Complexity, Runway in Use :

25R-25L

The highest number of aircrafts that can reach therunway threshold in period of one hour for both

configuration runway in use (07R-07L and 25R-

25L) is 44 aircrafts. From this fact, then we canapproximate the runway capacity for landing

operation only is 44 aircraft per hour for aircraft

mix 90% Medium type and 10% Heavy type. Thisresult must be confirmed with the operation on the

runway in order to obtain precise results.

The simulation results show that terminal airspacecapacity for the runway in use 07R-07L is higherthan 25R-25L. One reason is because the route of

arrival to 07R-07L is longer. It is also one of the

causes why the terminal airspace dynamiccomplexity while use runway 07R-07L is higher.

Therefore, controller will get a higher workload

when handling arrival traffic that using runway07R-07L.


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Further, when we consider the effect of inter-arrival time we find that increment of inter-arrival

time has a significant effect for values incrementof dynamic complexity. On the other hand,variation of ratio possibilities on which entry

point aircraft will arrival are not too significant

for increment of dynamic complexity.

7. Conclusion and Further

Works

We have developed terminal airspace model using

discrete event-based simulation in order todetermine the capacity and complexity of terminal

airspace. This animated simulation is a powerfuland effective modelling methodology forrepresenting and analyzing complex systems like

terminal airspace operations. Several simulation

scenarios implemented on Soekarno-Hatta

International Airport based on different averageinter-arrival time and ratio on which entry points

aircraft will arrive were examined. The modelingmethod then can be applied for another airport.

However further works are required to investigate

other terminal airspace complexity factor whichhas not been considered in this research. Studiesto analyze the others terminal airspace operations

(e.g., delays, the average holding time, runway

configuration optimization) can use this terminalairspace simulation model. This model can also be

further developed to include a wider airport

operations such as for the runway taxiway andapron operations. The results of simulationanalysis then can be used as consideration in

airport management planning to improve aviation

safety and airport capacity.

References

[1] Ministry of Transportation, Transportation

Statistics 2010, Indonesia, 2011.

[2] H. Baik and A. A. Trani, Framework of a

time-based simulation model for theanalysis of airfield operations, Journal of

Transportation Engineering, October, 2008.

[3] A. R. Odoni, J. Bowman, J. J. Deyst, E.Feron, et al., Existing and Required

Modeling Capabilities for Evaluating ATM

Systems and Concepts, 1997.[4] N. Pujet, B. Delcaire, and E. Feron,

INPUT-OUTPUT MODELING AND

CONTROL OF THE DEPARTURE

PROCESS OF CONGESTEDAIRPORTS,AIAA, pp. 118, 1999.

[5] F. R. Carr, Robust Decision-Support Toolsfor Airport Surface Traffic,Massachusetts

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[7] M. Janic, Air Transport System Analysis

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[8] F. Netjasov, Terminal Airspace TrafficComplexity, In Proceeding of 1stInternational Conference on Research in

Air Transportation (ICRAT), Zilina,

Slovakia, 2004, pp. 261-268.[9] F. Netjasov, M. Janic, V. Tosic, The

Future Air Transport System: Looking forGeneric Metrics of Complexity for

Terminal Airspace, TRB 2009 AnnualMeeting CD-ROM, 2009.

[10] P. Kopardekar, J. Rhodes, A. Schwartz, etal., Relationship Of MaximumManageable Air Traffic Control Complexityand Sector Capacity, 26th International

Congress Of The Aeronautical Sciences,2008.

[11] W. David Kelton, P. Sadowski, A.

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[14] M. Histon, R. Hansmann, et al.,Introducing Structural Considerations intoComplexity Metrics, Air Traffic Control

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[15] M. Pfleiderer, A. Manning, M. Goldman,

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With Operational Errors, Office ofAerospace Medicine, Washington, May2007.

[16] T. Krstic Simic, V. Tosic, "Airfield Traffic

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Isi Analysis Of Arrival Procedure On Terminal Airspace Using Simulation ModelRcmeaeok