airport landside modeling (computer applications and modeling)

Virginia Polytechnic Institute and State University 1 of 77

Airport Landside Modeling(Computer Applications and Modeling)

Dr. Antonio A. TraniProfessor 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 ofthe 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 enhanceunderstanding 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 modelingcapability (i.e., discrete and continuous paradigms in thesame model)


A Typical Simulation Language

• While all languages provide very similar constructs to build models, some have more complex Graphic User Interfaces (GUI) than others

• The basic building blocks of a simulation model are maintained.

• Building blocks of discrete simulation languages:

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

• Building blocks of continuous simulation languages:

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


A Typical Simulation Language (cont.)

• Modern (3rd generation simulation languages) provide rich GUI interfaces to construct and prototype models (EXTEND, Stella, and Arena are good examples of these)

• Object-oriented data abstraction is the norm

• All simulation languages have connectivity capabilities with other packages (i.e., statistical, spreadsheets, word processors, programming)

• ODBC/Corba support for database connectivity

• OLE support for Windows programming compatibility

• Statistical package support for regression and input/output analysis


Example of a Simulation Language Use and Connectivity

SimulationLanguageEngine

Aviation Data

Statistical

Packageor Numerical

Database

Package

SimulationLanguageGraphics

or GISDecisionSupportModel

Corba

OLECorba

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

Preamble

variable definitionsprocess definitionsresource definitions

Main

call process 1local var. definitions

call process m

Process 1

code to accomplish

local var. definitions

call process ma task

Process m

code to accomplish

local var. definitions

call process ma task


Sample 2nd Generation Sim. Language (Simscript)

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

• One of the first simulation languages used in distributed simulation mode (i.e., running processes in different computers)

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

• Today, MODSIM is replacing Simscript II.5 and adding full Object-Oriented Programming paradigm capabilities (RAMS - another airspace simulation model by Eurocontrol was developed in using MODSIM)


Sample 3rd Generation Sim. Language (EXTEND)

• Developed by Bod Diamond (1987) and distributed by Imagine 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 towards a common goal)

• Some portability (WIN and Mac versions available)

• Used im manufacturing, transportation and control engineering


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 quick modeling of complex processes. Shown below is a typical block showing its components.

F

L W

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 the source 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 in handout 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 a multiple 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 security checkpoints for all passengers boarding aircraft. Each security check point has two x-ray machines. A survey reveals that on the average a passenger takes 45 seconds to go through the system (negative exponential distribution service time).

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

In the design year (2010) the demand for services is expected 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 the design year of this terminal (year 2010) if the maximum tolerable waiting time in the queue is 2 minutes?

c) What is the expected number of passengers at the checkpoint area on a typical day in the design year (year 2010)?

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

e) What is the probability that more than 4 passengers wait 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 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 Use and Utilization Traces


Extend Model with Block Labels



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 Use and Utilization Traces

Generators

QueuePlotters

Activity BlockClock

Exit Block


Explanation of Extend Blocks Used

Discrete Block Generator - generates discrete 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 and

waiting time, respectively. Output F is a binary function that takes values of 1 when the queue is full (zero otherwise)



points

F

L W

1

Uni-queue Line



Activity Delay - holds an entity for a specified 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 to define 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 from further 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 of the 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 discrete simulation 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 (Baseline Conditions)

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)Plotter, 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

11

1

11

1

11

1

11

11

11

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 generated using 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 baseline year and compares them with those of the analytic model

Table 1. Results for Security Check Point System.

Parameter Analytic 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 to guarantee 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 of each server in the baseline year is around 90%

• This result is consistent with that obtained with the analytic queueing model (i.e., multi-server and infinite source)


Part (b) Horizon Year Operation

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

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

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

• As a matter of simulation practice you should always check the results of your simulation models with close form solutions before developing large and more complex models


Horizon Year Extend Model (4 servers)



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 time function 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 in demand (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

11

1

11

11

1

1

11

11

11



Discussion of Results (Horizon Year)

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

Table 2. Results for Security Check Point System.

Parameter Analytic Queueing Model (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 statistics for each server.

Expected Queue Length ( ) - per

1.18 0.97

Exp. Waiting Time in Queue ( ) - 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 a level 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 this type 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

1160.25

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


Lq


Answer to Part (e) - continuation

Isolating a small region of the queue length function we can 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 the Extend output

• An equivalent way to execute this analysis is to define a new 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 busy

function, , defined before in Equation (1)

Lq 4>

B t( )


points

F

L W

0

Uni-queue Line

D

T U

Officer 1

Officers

Security

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 a series of related Extend blocks

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

• In simple terms, hierarchical blocks provide a way to create templates of behavior through the combination of multiple Extend blocks

• An example of a hierarchical block from a prototype landside simulation model developed at Virginia Tech is shown on the next page


Hierarchical Blocks of a Simple Airport Terminal Model (ALPS - Airport Landside Performance

System)

These are some of the hierarchical blocks available in ALPS (an Extend library developed by Kulkarni and Trani, 1994)

count

Immigration

a

Heavy Acft Gate

Heavy Acft Gate

Heavy Acft Gate

Heavy Acft Gate

Heavy Acft Gate

CustomsCUSTOMSBaggage Claim

Entrance to Landside facilities

Help

Hierarchical Blocks


Taxonomy of a Hierarchical Block

Hierarchical blocks contain one of more common Extend blocks 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

0

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 of all passengers and sends a message to the following blocks

These blocks send the passengers to the respective baggage carousels for clooection of baggage

Baggage ClaimEquivalent to


The Importance of Attributes in Simulation

• Attributes: characteristics of entities used to differentiate behavior in the simulation process (i.e.,transfer vs terminating 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 domestic passenger baggage claim procedures. Some passengers carry carry-on luggage, others carry full baggage that needs to be retrieved from a direct feed baggage system.


Attributes (Illustration)

• If the percentages of passengers carrying full bags is known a random number generator and an attribute block can be used to model both types of passengers

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

start

VF

L W

550

D

T U

CD L W

F U

10 paxoutA

Set A

Help

Program Block

Attributes Block

Queueing Block

Activity Delay Block Animation

BlockMultiple Activity Block


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 5entityarrival

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_Out

Veh02_Out

Veh12_In

Veh11_Out

Veh01_In

Veh11_In

Veh01_OutD B1

B1D B2

B2Transit Unit

D B1

B1D B2

B2Transit Unit

EscalatorB

D

B

D

EscalatorB

D

B

D

StairwayD

BB

D

ElevatorB

D

B

D

D

B

D

B

CirculationPassageway

D B1

D B1 B2

B2

D

B

Platform

(1) Pax.Schedule

Info.

Boarding Passenger Flow Direction

Deboarding Passenger Flow Direction


Sample APM Guideway Model

Double-Lane Loop

Pinched Loop with Turnbacks

A

B

A

B

B

A

B

A

StationStation

StationStation

BA

BA

StationStation Station


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 a station 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 complete guideway 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

NorthGuideway

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


Atlanta APMSIM Guideway Model

ABC

count

BAC

BA C A

B

C

BA

BA

Station

Station

Station

Station

Station

Station

BA

VehicleSchedule

Bypass

Bypass

Station

Turnaround

Baggage Station Ticketing Station Concourse A

Concourse B

Concourse C Concourse D Concourse E

North Guideway

South Guideway

South Guideway

North Guideway

North Guideway

South Guideway


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


ATL APMSIM Simulation Results

Passengers waiting at a station (on the APM platform area)

600 666.6667 733.3333 800 866.6667 933.3333 10000

10

20

30

40

50

60

70

80

90

100

110

120


No. of Passengers (passengers)No of Passengers on the Platform


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 different scenarios


APM or Other Transport Modes in Terminal Airport Models

• As demonstrated in the APMSIM model average statistics about LOS can be obtained in discrete and hybrid simulations

• These statistics can, in the end, be used to compare the static and macroscopic values typically used in airport terminal design

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

• It is more relevant to know how queues form and dissipate 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.

airport landside modeling (computer applications and modeling)

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