Airport Landside Apps

8/13/2019 Airport Landside Apps

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Virginia Polytechnic Institute and State University 1 of 77

Airport Landside Modeling

(Computer Applications and Modeling)

Dr. Antonio A. Trani

Associate Professor of Civil and Environmental Engineering

Virginia Polytechnic Institute and State University

CEE 4674 - Airport Planning and Design




Material Presented in this Section

• Brief description of airport terminal simulationlanguages

• Advantages and weaknesses

• Basic constructs

• Example of VPI Airport Terminal SimulationLibrary (EXTEND)

• Example APM simulation model (APMSIM)developed at Virginia Tech

• Future directions and impacts




Purpose of the Discussion

• Until now you understand the basics of probability,continuous, and discrete event simulation and modelingusing generic mathematical packages (such as Matlab) orspreadsheets

• Dedicated simulation languages keep track of many of

the book keeping activities required in the simulationmodel

• you can concentrate in the model with minimalimplementation burden

• productivity and model development are enhanced• Simulation tools provide rich GUI constructs to enhance

understanding from a decision maker viewpoint




Simulation Languages

Discrete Event Simulation Languages

SIMULA, STELLA, SIMSCRIPT II.5, MODSIM,

SLAM III, GPSS-H/GPSS-PC, Arena/SIMAN, EXTEND

Continuous Simulation Languages

ACSL, Simulink, STELLA, SIMSCRIPT II.5, MODSIM,

SLAM III, GPSS-H/GPSS-PC, Arena/SIMAN, EXTEND

• Many of these languages provide a hybrid modeling

capability (i.e., discrete and continuous paradigms in thesame model)




A Typical Simulation Language

• While all languages provide very similar constructs tobuild models, some have more complex Graphic UserInterfaces (GUI) than others

• The basic building blocks of a simulation model aremaintained.

• Building blocks of discrete simulation languages:

• Entities, Attributes, Resources, Queues,Accumulators, and Events

• Building blocks of continuous simulation languages:

• Integrator (reservoir, tank), generator, delaystructures, function blocks, rate variable blocks, etc.




A Typical Simulation Language (cont.)

• Modern (3rd generation simulation languages) providerich GUI interfaces to construct and prototype models(EXTEND, Stella, and Arena are good examples ofthese)

• Object-oriented data abstraction is the norm

• All simulation languages have connectivity capabilitieswith other packages (i.e., statistical, spreadsheets, wordprocessors, programming)

• ODBC/Corba support for database connectivity

• OLE support for Windows programming compatibility

• Statistical package support for regression and input/outputanalysis




Example of a Simulation Language Use andConnectivity

SimulationLanguage

Engine

Aviation Data

Statistical

Package

or Numerical

Database

Package

SimulationLanguage

Graphics

or GISDecisionSupportModel

Corba

OLE

Corba

Corba




Sample 2nd Generation Sim. Language (Simscript)

• Developed and marketed by CACI (US)

• Provides English-type constructs

• C-type model construction

• Preamble file (contains global variables, resourcedefinitions, event types, tally definitions, etc.)

• Main file (first routine to be executed: calls othersin the program)

• C-language portability (platform independence)

• Heavily used in military and aviation modeling in the1970’s and 1980’s in US

• Stable compiler (few modifications in the past five years)




Typical Organization of a Simscript II Model

Preamblevariable definitionsprocess definitionsresource definitions

Main

call process 1

local var. definitions

call process m

Process 1

code to accomplish


call process m

a task

Process m

code to accomplish


call process m

a task




Sample 2nd Generation Sim. Language (Simscript)

• Graphic capabilities were added in the 1980’s(Simgraphics)

• One of the first simulation languages used in distributedsimulation mode (i.e., running processes in differentcomputers)

• SIMMOD - the airspace and airfield simulation modeldeveloped by the FAA was developed in Simscript II.5 inthe late 1970’s

• Today, MODSIM is replacing Simscript II.5 and adding

full Object-Oriented Programming paradigm capabilities(RAMS - another airspace simulation model byEurocontrol was developed in using MODSIM)




Sample 3rd Generation Sim. Language (EXTEND)

• Developed by Bod Diamond (1987) and distributed byImagine That, Inc.

• Provides a rich GUI to develop models using blocks

• Access to C-like block code (Modl language)

• Blocks are classified in terms of libraries

• Drag-and-connect approach

• Hierarchy blocks (multiple blocks working towardsa common goal)

• Some portability (WIN and Mac versions available)

• Used im manufacturing, transportation and controlengineering




Sample 3rd Generation Sim. Language (EXTEND)

Extend Libraries

Generic Library: has blocks that perform basic functions

as math, decision handling and input/output

Discrete-Event Library: contains blocks for creating

discrete-event simulation models (including activity

delays, resources, processes, etc.)

Plotter Library: contains blocks for plotting the results in

any type of simulation model

Animation Library: holds blocks that are used for

animating hierarchical blocks




Taxonomy of a Block

Blocks are complex structures in Extend that allow quickmodeling of complex processes. Shown below is a typical

block showing its components.

F

L

1

Uni-queue Line

Input Connector

Output Connector

Information Connector(Output Connector)

Information Connector(Output Connector)




Taxonomy of a Block (cont.)

Each block in Extend is extensible and modifiable to thesource code level (Modl - the language used in Extend -

blocks is an extension of the C language).

procedure getArrays()

{if (rCount != sysGlobalint2)

{

if(useString)

getPassedArray(sysGlobal5, itemArrayA);

if (costing)

getPassedArray(sysGlobal9, itemArrayC);

rCount = sysGlobalint2;




Example of Security Check Point Queue in Extend

This example replicates the seaport example discussed inhandout 7 (queuing models). The idea is to compare the

simulation results with those obtained with the analytic

queueing model.

The purpose of the example is to show you how simple amultiple server problem is constructed and solved using

various simulation packages.




Example : Level of Service at Security Checkpoints

The airport shown in the next figures has two securitycheckpoints for all passengers boarding aircraft. Eachsecurity check point has two x-ray machines. A surveyreveals that on the average a passenger takes 45 secondsto go through the system (negative exponential

distribution service time).

The arrival rate is known to be random (this equates to aPoisson distribution) with a mean arrival rate of onepassenger every 25 seconds.

In the design year (2010) the demand for services isexpected to grow by 60% compared to that today.




Relevant Operational Questions

a) What is the current utilization of the queueing system(i.e., two x-ray machines)?

b) What should be the number of x-ray machines for thedesign year of this terminal (year 2010) if the maximumtolerable waiting time in the queue is 2 minutes?

c) What is the expected number of passengers at thecheckpoint area on a typical day in the design year (year2010)?

d) What is the new utilization of the future facility?

e) What is the probability that more than 4 passengerswait for service in the design year?




Airport Terminal Layout




Security Check Point Layout




Security Check Point Model (Baseline)

Representation of the system modeled in Extend (s=2)

3V 1 2Arr. Passengers

Passengers

arrive from

ticket checkpoints

F

L W

1

Waiting Line

D

T U

Officer 1

Queue

Length

Trace

#

Exit(4)

732 Plotter

Security

Officers

count

Leave

Security

Area

D

T U

1 2 3

Rand

service time

Time in Use

and Utilization

Traces




Extend Model with Block Labels


Passengers

arrive from

ticket check

points

F

L W

1

Waiting Line

D

T U

Officer 1

Queue

Length

Trace

#

Exit

(4)732 Plotter

Security

Officers

count

Leave

Security

Area

D

T U

1 2 3

Rand

service time

Time in Useand Utilization

Traces

Generators

QueuePlotters

Activity BlockClock

Exit Block




Explanation of Extend Blocks Used

Discrete Block Generator - generatesdiscrete entities in conjunction with discrete

event models. This block has 18 different

Probability Density Functions (PDF) to

choose from, including an empirical

distribution format to enter real data without a PDF fit.

Queue FIFO - this structure keeps track of

physical queues where the first entity arriving

is the first one to be processed. Parameters L

and W represent the queue length andwaiting time, respectively. Output F is a binary function

that takes values of 1 when the queue is full (zero

otherwise)


Passengersarrive from

ticket check

points

F

L W

1

Uni-queue Line





Activity Delay - holds an entity for aspecified amount of time. Input parameter is

D, the delay inside the activity block. Output

parameters are T and U which represent the

time in use and the utilization of the block,

respectively.

Random Number - generates a random

number according to a pre-specified

distribution. Input parameter are 1,2, and 3

which represent 3 input arguments used todefine user-defined distributions. The single

output parameter is a random number. This block is

typically used in conjunction with activity delays.

D

T U

Officer 1

1 2 3

Rand

service time





Exit Block (4) - destroys up to 4 items fromfurther consideration in the simulation. The

total number of entities absorbed by the

block are reported. The output # connector

reports the number of entities exiting the

simulation.

Plotter, Discrete Event - This block plots up

to 4 parameters in the same figure. Note four

input parameters in the left hand side of the

block.

#

Exit(4)

473

Leave

Security

Area

Queue

Length

Trace

Plotter





Executive Block - controls the duration ofthe simulation. This can be done either by

count (i.e., a prespecified number of units or

by time duration).

Note: this block has to be present in all discretesimulation models in Extend. Its location must be at the

left most position in the simulation model (a necessary

convention in Extend).

count




Sample Results for Two-Server System (BaselineConditions)

The results below show the utilization and waiting time

instances for the security checkpoint area

0 100 200 300 400 500 6000

0.4499084

0.8998168

1.349725

1.799634

2.249542

2.69945

3.149359

3.599267

4.049176

4.499084

4.948993

5.398901

Time

Time in Use (min)Plott er, Discrete Event

0

0.07840131

0.1568026

0.2352039

0.3136052

0.3920065

0.4704079

0.5488092

0.6272105

0.7056118

0.7840131

0.8624144

0.9408157

Utilization

Waiting Time S1 Y2 Utilization S1 Wait Time S2 Y2 Utilization S2




Uni-Queue Length Trace

The following diagram depicts the uni-queue length

0 100 200 300 400 500 6000

119.3333

238.6667

358

477.3333

596.6667

716

835.3333

954.6667

1074

1193.333

1312.667

1432

Time (minutes)

Leaving Security AreaSecurity Area

0

1.666667

3.333333

5

6.666667

8.333333

10

11.66667

13.33333

15

16.66667

18.33333

20

In line

1

1

1

1

1

1

1

1

11

1

1

1

1

1 Leaving Y2 In Queue Line




Uni-Queue Waiting Time Trace

The following diagram depicts the waiting times vs time

0 100 200 300 400 500 6000

0.8333333

1.666667

2.5

3.333333

4.166667

5

5.833333

6.666667

7.5

8.333333

9.166667

10

Time

Waiting Time (minutes)Queue Waiting Time Trace

Waiting Time




Distribution of Interarrival Times

The following plot of interarrival times was generatedusing Extend in the security check point model

0.000102552 0.4863909 0.9726792 1.458968 1.945256 2.431544 2.9178320

1.232143

2.464286

3.696429

4.928571

6.160714

7.392857

8.625

9.857143

11.08929

12.32143

13.55357

14.78571

Interarrival Time (minutes)

Percent ItemsItems' Distribution

% Items




Discussion of Results (Baseline Year)

The following table shows typical results for the baselineyear and compares them with those of the analytic model

Table 1. Results for Security Check Point System.

ParameterAnalytic Queueing

Model (s = 2)Simulation Model

(2 servers)

Utilization ( ) 0.900 0.916 / 0.886 a

a.Two values are reported because Extend keeps independent statistics for

each server.

Expected Queue

Length ( ) - per

7.60 6.96

Exp. Waiting Time in

Queue ( ) - min

3.20 2.85

ρ

Lq

W q




Simulation Length and Stability of Results

Simulations results require careful interpretation toguarantee stable solutions as exemplified below

0.2745101 33.56209 66.84967 100.1373 133.4248 166.7124 2000

0.3728427

0.7456853

1.118528

1.491371

1.864213

2.237056

2.609899

2.982741

3.355584

3.728427

4.101269

4.474112

Time

Time in Use (min)Plotter, Discrete Event

0

0.08134968

0.1626994

0.244049

0.3253987

0.4067484

0.4880981

0.5694477

0.6507974

0.7321471

0.8134968

0.8948464

0.9761961

Utilization

Steady-StateTransientRegion Region




Part (a) Baseline Utilization Results

• From the previous graphs is evident that the utilization ofeach server in the baseline year is around 90%

• This result is consistent with that obtained with theanalytic queueing model (i.e., multi-server and infinitesource)




Part (b) Horizon Year Operation

• Recalling from the analytic model that 4 x-ray machineswere needed to provide levels of service below 2 minutes(in the queue)

• The corresponding Extend model and results andillustrated in the following pages

• Clearly the results of the simulation model are in closeagreement with those of the analytic model presented inthe queueing models section

• As a matter of simulation practice you should always

check the results of your simulation models with closeform solutions before developing large and morecomplex models




Horizon Year Extend Model (4 servers)


Passengers

arrive from

ticket check

points

F

L W

0

Uni-queue Line

D

T U

Officer 1

Queue

Length

Trace

#

Exit

(4)1547 Plotter

Security

Officers

count

Leave

Security

Area

D

T U

Officer 2

1 2 3

Rand

service time

Utilization

Traces

Waiting Time

Trace

D

T U

D

T U




Horizon Year Model Utilization

The average utilization is reduced with 4 servers and 60%increase in the demand functions (400 minute simulation)

0.2212978 66.85108 133.4809 200.1106 266.7404 333.3702 4000

0.07387553

0.1477511

0.2216266

0.2955021

0.3693777

0.4432532

0.5171287

0.5910043

0.6648798

0.7387553

0.8126309

0.8865064

Time

UtilizationPlotter, Discrete Event




Horizon Year Results ( )

The following plot shows the behavior of the waiting timefunction during a 400 minute simulation

W q

0 66.66667 133.3333 200 266.6667 333.3333 4000

0.25

0.5

0.75

1

1.25

1.5

1.75

2

2.25

2.5

2.75

3

Time

Waiting Time (minutes)Queue Waiting Time Trace

Waiting Time




Horizon Year Queueing Characteristics

Four servers are capable of coping with 60% increase indemand (400 minute simulation)

0 66.66667 133.3333 200 266.6667 333.3333 4000

128.9167

257.8333

386.75

515.6667

644.5833

773.5

902.4167

1031.333

1160.25

1289.167

1418.083

1547

Time (minutes)


0

1.25

2.5

3.75

5

6.25

7.5

8.75

10

11.25

12.5

13.75

15

In line

1

1

1

1

1

1

1

1

1

1

1

1

1

1





Discussion of Results (Horizon Year)

The following table shows typical results for the horizonyear and compares them with those of the analytic model

Table 2. Results for Security Check Point System.

ParameterAnalytic QueueingModel (4 servers)

Horizon Year(4 servers)

Utilization ( ) 0.72 0.82 / 0.75 / 0.66 / 0.60 a

average = 0.71

a. Four values are reported because Extend keeps independent statisticsfor each server.

Expected Queue

Length ( ) - per

1.18 0.97

Exp. Waiting Time inQueue ( ) - min

0.30 0.26

ρ

Lq

W q




Answers to Parts (b) - (d)

b) The number of x-ray machines should be 4 to provide alevel of service below 2 minutes

c) The the expected number of passengers at the

checkpoint area on typical day in the design year

should be around 0.97 persons (quite small for thistype of facility but the result of the 2-min waiting time

limit)

d) The utilization of the future facility should be around

71% (average of four servers)




Answer to Part (e)

The Queueing Length ( ) versus time plot provides

insight on this. The red area corresponds to > 4. Lq

0 66.66667 133.3333 200 266.6667 333.3333 4000

128.9167

257.8333

386.75

515.6667

644.5833

773.5

902.4167

1031.333

1160.251289.167

1418.083

1547

Time (minutes)


0

1.25

2.5

3.75

5

6.25

7.5

8.75

10

11.2512.5

13.75

15

In line


Lq




Answer to Part (e) - continuation

Isolating a small region of the queue length function wecan see more clearly the function to be integrated

82.86402 83.93834 85.01266 86.08699 87.16131 88.23563 89.30995187.7423

235.4808

283.2192

330.9577

378.6961

426.4345

474.173

521.9114

569.6498

617.3883

665.1267

712.8651

760.6036

Time (minutes)


0.07905244

0.5421237

1.005195

1.468266

1.931337

2.394409

2.85748

3.320551

3.783622

4.246694

4.709765

5.172836

5.635907

In line


Area of Interest

Lq > 4




Answer to Part (e) - continuation

• The diagram is useful to guesstimate the probability of> 4. However if we need and accurate answer we

should extract the numerical values of from theExtend output

•

An equivalent way to execute this analysis is to define anew busy function in Extend such that,

= (1)

then integrate this function over time to obtain the number

the percent of instances where > 4.

Lq

Lq

B t ( ) 1 if system if Lq 4>0 if system if Lq 4

≤

Lq




Computation of in Extend

A decision block and an integrator compute the new busyfunction, , defined before in Equation (1)

Lq 4>

B t ( )


points

F

L W

0

Uni-queue Line

D

T U

Officer 1

S

D

T U

Officer 2

1 2 3

Rand

service time

Utilization

Traces

N

B

A

Y a>b

SR

Waiting Time

Trace

D

T U

D

T U

Integrator Block

Decision Block

Plot of theIntegral of B(t)




Graphical Result of the Integral of

The result below shows the integral of over time

B t ( )

B t ( )

0 75 150 225 3000

2.304406

4.608813

6.913219

9.217626

11.52203

13.82644

16.13085

18.43525

Time

Integral of B(t)Plotter, Discrete Event

Solid Blue GrayPat Red GrayPat Green ltGrayPat Black




Avoiding Clutter with Hierarchical Blocks

•

Hierarchical blocks are complex structures that contain aseries of related Extend blocks

• Their main purpose is to avoid clutter in a complexmodel and, at the same time, provide a quick way toabstract complex behavior with minimum effort

• In simple terms, hierarchical blocks provide a way tocreate templates of behavior through the combination ofmultiple Extend blocks

• An example of a hierarchical block from a prototype

landside simulation model developed at Virginia Tech isshown on the next page




Hierarchical Blocks of a Simple Airport TerminalModel (ALPS - Airport Landside Performance

System)

These are some of the hierarchical blocks available in

ALPS (an Extend library developed by Kulkarni and

Trani, 1994)

Immigration

a

Heavy Acft Gate

Heavy Acft Gate

Heavy Acft Gate

Heavy Acft Gate

Heavy Acft Gate

CustomsCUSTOMSBaggage Clai

Entrance to

Landside

facilities

Help

Hierarchical Blocks




Taxonomy of a Hierarchical Block

Hierarchical blocks contain one of more common Extendblocks as shown below

CD L W

F U

0

A

∆

Get A

b?

a

select 1 2 3

Rand

b?

a

select

CD L W

F U

0

1 2 3

Rand

CD L W

F U

0

1 2 3

Rand

F U

apaxIn

CD L W

F U

0

1 2 3

Rand

F

L W

0

Help

These two blocks

represent the circulation

in the facility

Queueing

Block

Collects attributes ofall passengers and

sends a message to the

following blocks

These blocks send the

passengers to the

respective baggage

carousels for

clooection of baggage

Baggage Clai

Equivalent to




The Importance of Attributes in Simulation

•

Attributes: characteristics of entities used to differentiatebehavior in the simulation process (i.e.,transfer vsterminating passengers)

• Attributes move with the entity and thus can be“retrieved” in simulation blocks to perform various

processes based on the attribute of an entity at time t

• Practically all simulation languages support attributes

• For example, suppose we are simulating domesticpassenger baggage claim procedures. Some passengers

carry carry-on luggage, others carry full baggage thatneeds to be retrieved from a direct feed baggage system.




Attributes (Illustration)

•

If the percentages of passengers carrying full bags isknown a random number generator and an attribute blockcan be used to model both types of passengers

• In this example an attributes block is used to assigneither carry-on or full bags to each passenger

start

VF

L W

550

D

T U

CD L W

F U

10 paxout

A

Set A

Help

Program

Block

Attributes

Block

Queueing

Block

Activity Delay

Block AnimationBlock

Multiple ActivityBlock




APM Simulation Model (Lin and Trani, 1995)

Purpose of research model• To model individual passenger and APM vehicle

movement at airports

• To determine the sensitivity of system performance

• To estimate the APM vehicle energy consumption

• To examine the flexibility of an APM system

Generically we call this model APMSIM




APM Requirements Analysis

Level of Service Analysis

APM Demand Analysis

Capacity Analysis

Flow Analysis

Energy Consumption Analysis




Methodology

Developed a hybrid simulation model in ExtendTM

time

Process

Event 1 Event 2 Event 3 Event 4 Event 5

entityarrival

servicebegun ontask 1

servicebegun ontask 2

serviceended ontask 1

serviceended ontask 2

Activity 1

Activity 2




APM Simulation Model Description

•

APM Simulation Model• APM station model

• APM guideway model

• Energy consumption model

• Simulation Model Logic

• Passenger flows

• Vehicular flows




Sample Schematic of Station Model

Veh02_ In

Veh12_ Ou

Veh02_ Ou

Veh12_ In

Veh11_ Ou

Veh01_ In

Veh11_ In

Veh01_ OuD B1

B1D B2

B2

Transit Uni

D B1

B1D B2

B2

Transit Uni

Escalato

B

D

B

D

Escalato

B

D

B

D

Stairway

D

BB

D

Elevato

B

D

B

D

D

B

D

B

Circulation

PassagewayD B1

D B1 B2

B2

D

BPlatform

(1) Pax.Schedule

Info.

Boarding Passenger Flow Directio

Deboarding Passenger Flow Directio





Virginia Polytechnic Institute and State University56 of 77

APM Energy Consumption Model

The purpose of this submodel is to estimate the following:• Speed

• Acceleration

• Travel Time

• Travel Distance

• Power Requirements

• Energy Consumption

• LOS (Level of Service)

• Occupancy and Load Factor




Energy Modeling

•

David’s Equation, Power required and energy consumed• Integrate these relationships over time (use Extend’s RK-

4 integration algorithms

R (a+ r) = K0

+

K1

w + B(V) +

CAV2

wn

E = 3.6 ×10-6

Pdt0

t

∫

P =

T(V)




Typical Station Results

•

Travel time for an individual passenger• Average waiting time for a facility or a TU

• Total number of passengers arriving at or leaving astation or a facility

• Queue length at a facility

• LOS of a facility in terms of area per passenger




Typical Guideway Results

For a single guideway section or for the completeguideway network the model estimates:

• Travel time of an individual TU or passenger

• Occupancy and load factors of a TU

• TU speed, acceleration, deceleration, and travel distance

• TU power requirements and energy consumption

• Number of TUs in a guideway or a system

•

LOS in a vehicle




Sample Application of the APMSIM Model

We modeled Atlanta Hartsfield Intl. Airport in the US

Concourse A

InternationalConcourse

Concourse B Concourse C Concourse D

Ticketing

APM Station

Station Doors

305 m 305 m305 m278 m

North

Guideway

SouthGuideway

52 m 76 m 52 m

41 m

88 m 130 m 346 m

305 m

305 m 305 m

52 m305 m

305 m

305 m221 m Bypass

Baggage

368 m

Concourse E

242 m 69 m 38 m

Maintenance

305 m




61 of 77

Atlanta APMSIM Guideway Model

A

B

C

count

B

AC

B

A C A

B

C

B

A

B

A

Station

Station

Station

Station

Station

Station

B

A

VehicleSchedul

Bypass

Bypass

Station

Turnaround

Baggage Station Ticketing Statio Concourse A

Concourse B

Concourse C Concourse D Concourse E

North Guideway

South Guideway

South Guideway

North Guideway

North Guideway

South Guideway




62 of 77

Atlanta APMSIM Station Results

Passengers entering and leaving a station

600 666.6667 733.3333 800 866.6667 933.3333 10000

32

64

96

128

160

192

224

256

288

320

352

384

Simulation Time (seconds)

Total No. of PAX (passengers)PAX Entering/ Leaving a Platfrom

Pax Entering Pax Leaving




63 of 77

ATL APMSIM Simulation Results

Passengers waiting at a station (on the APM platformarea)

600 666.6667 733.3333 800 866.6667 933.3333 10000

10

20

30

40

50

60

70

80

90

100

110

120


Passengers (passengers)No of Passengers on the Platform




64 of 77

ATL APMSIM Results

Level of service at the APM platform

600 666.6667 733.3333 800 866.6667 933.3333 10000

2.5

5

7.5

10

12.5

15

17.5

20

22.5

25

27.5

30


Area per Passenger (m^2/ pax)LOS on the Platform

LOS Equivalent LOS




ATL APMSIM Simulation Results

Number of Passengers Entering a Concourse

600 666.6667 733.3333 800 866.6667 933.3333 10000

20.83333

41.66667

62.5

83.33333

104.1667

125

145.8333

166.6667

187.5

208.3333

229.1667

250


Accum. PAX Flow (passengers)Passengers Entering the Concours

0

0.75

1.5

2.25

3

3.75

4.5

5.25

6

6.75

7.5

8.25

9

PAX Flow

Accum. Pax Flow Y2 Pax Flow




ATL APMSIM Development Conclusions

These are conclusions of this model development effort:• Able to model various types of APM system

configurations

• Useful to estimate the effects of changes in the system by

modifying the input data• Easy to model alternative APM service concepts

• Capable of simulating an APM system for differentscenarios




APM or Other Transport Modes in TerminalAirport Models

• As demonstrated in the APMSIM model averagestatistics about LOS can be obtained in discrete andhybrid simulations

• These statistics can, in the end, be used to compare the

static and macroscopic values typically used in airportterminal design

• As engineers and analysts we have to get the point todecision makers that airport terminals cannot be viewedand designed using static metrics

• It is more relevant to know how queues form anddissipate at processing facilities than computing a“perfect-gas molecular” equivalent (area per passenger)




References

1) Lin,Y. A Simulation Model of an Automated People Mover at Airports. M.S. Thesis. Virginia Polytechnic

Institute and State University, Blacksburg, VA 24061.

2) Kulkarni, M. Development of a Landside Terminal

Simulation Model. M.S. Thesis. Virginia Polytechnic

Institute and State University, Blacksburg, VA 24061.

3) Fruin, J.J. Designing for Pedestrians. in Public

Transportation Systems. Hoel and Gray: Editors. John

Wiley and Sons, New York, 1993.

4) IATA. Airport Development Reference Manual: 8th

Edition. International Airline Transport Association,

Montreal, 1995.



























Documents

Airport Landside Apps