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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
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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
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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
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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)
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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.
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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
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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
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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)
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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
local var. definitions
call process m
a task
Process m
code to accomplish
local var. definitions
call process m
a task
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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)
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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
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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
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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)
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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;
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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.
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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.
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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?
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Airport Terminal Layout
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Security Check Point Layout
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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
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Extend Model with Block Labels
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 Useand Utilization
Traces
Generators
QueuePlotters
Activity BlockClock
Exit Block
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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)
3V 1 2Arr. Passengers
Passengersarrive from
ticket check
points
F
L W
1
Uni-queue Line
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Explanation of Extend Blocks Used
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
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Explanation of Extend Blocks Used
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
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Explanation of Extend Blocks Used
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
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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
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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
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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
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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
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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
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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
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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)
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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
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Horizon Year Extend Model (4 servers)
3V 1 2Arr. Passengers
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
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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
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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
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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)
Leaving Security AreaSecurity Area
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
1 Leaving Y2 In Queue Line
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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
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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)
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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)
Leaving Security AreaSecurity Area
0
1.25
2.5
3.75
5
6.25
7.5
8.75
10
11.2512.5
13.75
15
In line
1 Leaving Y2 In Queue Line
Lq
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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)
Leaving Security AreaSecurity Area
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
1 Leaving Y2 In Queue Line
Area of Interest
Lq > 4
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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
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Computation of in Extend
A decision block and an integrator compute the new busyfunction, , defined before in Equation (1)
Lq 4>
B t ( )
3V 1 2Arr. Passengers
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)
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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
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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
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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
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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
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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.
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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
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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
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APM Requirements Analysis
Level of Service Analysis
APM Demand Analysis
Capacity Analysis
Flow Analysis
Energy Consumption Analysis
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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
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APM Simulation Model Description
•
APM Simulation Model• APM station model
• APM guideway model
• Energy consumption model
• Simulation Model Logic
• Passenger flows
• Vehicular flows
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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
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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
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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)
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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
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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
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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
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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
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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
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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
Simulation Time (seconds)
Passengers (passengers)No of Passengers on the Platform
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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
Simulation Time (seconds)
Area per Passenger (m^2/ pax)LOS on the Platform
LOS Equivalent LOS
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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
Simulation Time (seconds)
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
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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
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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)
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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.
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