Explanation of Several Simulation Models
Transcript of Explanation of Several Simulation Models
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Analysis of Air Transportation Systems
Descriptions of Airport and Airspace Simulation Models
Dr. Antonio A. Trani Civil and Environmental Engineering
Virginia Polytechnic Institute and State University
Jan. 9-11, 2008
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Material Presented in this Section
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Review of current large-scale simulation models
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Review some of their strengths and weaknesses
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Provide you with some information to better understand various large-scale airport and airspace simulation models
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Basics on Airport and Airspace Simulation Models
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These models mimic the behavior of aircraft in complex airspace and airport systems
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Typically these models use a discrete event simulation approach (see another handout on this) to move aircraft among airport and airspace resources
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Airport and airspace resources are considered objects like runways, taxiways, gates and airspace links
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These models employ some sort of link-node structure to move aircraft entities between resources
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Sample Airport and Airspace Simulation Models
SIMMOD - the FAA airport and airspace simulation model
RAMS - Eurocontrol’s reorganized mathematical simulator model
TAAM - Australian developed simulation model (the Preston Group is now part of the Boeing Company)
Several in-house simulation models exist (VPI_asim)
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Common Goals of Large-Scale Airport Simulation Models
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To estimate airport measures of effectiveness to estimate delays curves for an airport subject to some airport schedule (or demand) scenario
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Delays are usually defined as the difference between unimpeded and actual travel times
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Estimate utilization of airport resources (such as gates, runways, taxiways, etc.)
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NOTE: These models do not measure capacity directly.
Capacity is a non observable variable
in an airport system (can be estimated measuring delay)
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SIMMOD
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An airspace and airfield simulation model developed by the FAA in the last two decades
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Good airfield and airspace logic
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Gate-to-Gate simulator (important for some applications)
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2D graphics (except for workstation version)
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Validated in the period 1985-1991
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Cost: $5,900 per copy for SIMMOD Plus! version 7
• Large learning curve (in general for SIMMOD)
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TAAM
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An airspace and airfield simulation model developed by the Preston Group (Australia) - a Boeing Company
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Good airfield and airspace logic
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Gate-to-Gate simulator (important for some applications)
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Excellent graphics
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Not validated although in use by many airlines and research organizations
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Cost: ~$300,000 per copy
• Large learning curve
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TAAM Model
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RAMS
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An airspace simulation model developed by Eurocontrol (equivalent of FAA ar traffic services in the US)
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Only airspace simulation
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Developed using MODSIM - a simulation language developed by CACI
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Good aircraft conflict detection and resolution
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Price varies from $7,500-$22,000 Euros (version 5.2 )
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Large learning curve
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Sample Screen of RAMS
The figure illustrates the conflict detection and resolution in RAMS
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Principles of Discrete-Event Simulation (Applies to all three models)
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The simulation moves from one scheduled event to the next one
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Keeps track of simulation events in an orderly fashion
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Many internal events are generated for each external event.
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The simulation clock is based on the current event’s scheduled initiation, not elapsed clock time
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Events that are simultaneous, I.e., events with the same initiation times, are processed sequentially but there is no time change to the simulation clock.
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Typical SIMMOD/TAAM Studies
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Runway closure impacts
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Analysis and delays of airfield ground operations
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Taxiway closures and upgrades
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Cargo and passenger terminal impact studies
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Pavement management
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Terminal traffic analysis
• Arrival/departure terminal operations
• New in-trail aircraft separation procedures
• Multi-airport interactions
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Description of SIMMOD/TAAM
SIMMOD and TAAM are computer modes used in airport operations and planning
Simulates airport airside operations (i.e., airfield and airside)
Estimates capacity, travel time, delay and fuel consumption resulting from aircraft operations
Allows the investigation of causal links between airport technological improvements, aircraft operational procedures and their effect on aircraft delay
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Justification of Large-Scale Models
Computer models are:
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Safe in ascertaining the impact of operational changes
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Inexpensive to use
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Flexible to account for special airport/airspace conditions
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Provide answers to airspace and airport operational analysts
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Help to understand complex operational phenomena
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Improve decision-making ability
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SIMMOD’s History
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Development of the Airport/Airspace Delay Model (ADM)(1978-1979)
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Development of SIMMOD fuel consumption post-processors (1983)
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Validation of the SIMMOD Simulation Model (1985-1991)
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IBM and Compatible version 1.2 available in late1992
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Virginia Tech implements runway and HS runway exit logic changes (1995)
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SIMMOD Plus! from the ATAC Corporation
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Large Scale Model as Decision Analysis Tools
Airspace and Airport Physical Layout
FlightSchedules
ATC Policiesand
Procedures
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How SIMMOD/TAAM and Work
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Builds airspace and airports from inputs that describe the physical layout.
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Simulates all flights plane-by-plane.
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Uses external data to initiate flights.
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Resolves all conflicts.
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Monitors time and fuel consumed along each segment.
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Generates reports of some of the following: Statistical Summaries, Graphics and Animation
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Sample Application (SIMMOD)
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Raleigh-Durham International Airport (RDU) in North Carolina represents a typical example of a medium size hub airport in the US
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Given a baseline aircraft demand during a two hour peak period you will be asked to modify the airspace and run some baseline simulations
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Graphical Description of RDU Terminal Airspace
23R05L
23L05R
Departure Track 2
Departure Track 1
Arrival Track 1
Arrival Track 2
LOM 2
LOM 1
RDU Airport
6 n.m.
10 n.m.
10 n.m.
5 n.m.
5 n.m.
6 n.m.
6 n.m. 20 Degrees
20 Degrees
30 Degrees
30 Degrees
4 n.m.
4 n.m.
4 n.m.
4 n.m.
6 n.m.
Node 294Node 295
Node 267
Node 296
Node 297
Node 271
Node 302
Node 301
Node 300
Node 299
Node 280
Node 304
Node 305
Node 307
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Graphical Depiction of RDU Airfield Configuration
Runway 5L-23R
Runway 5R-23L
RDU Airport with Supergates Shown
G 201 (American)
G 181 (Delta)
G 165 (USAir)
Proposed CargoTerminalA B
C
Gate CapacitiesConcourse A - 15 aircraft Concourse B - 10 aircraftConcourse C - 30 aircraft Cargo Complex - 30 aircraft
Node 11
Node 17
Node 16
Node 245
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RDU Baseline Input Parameters (Aircraft Demands during a Two Hour Peak Hour)
Flight Type of Aircraft Departure Time Gate(hours + decimal)
1 AA 231 B 727-200 7.15 G 2012 AA 450 B 727-200 7.20 G 2013 AA 120 B 737-200 7.23 G 2014 AA 003 F28 MK 2000 7.24 G 2015 AA 052 F28 MK 2000 7.26 G 2016 AA 2231 DC9-30-50 7.32 G 2017 AA 123 B 727-200 7.41 G 2018 DEL 200 F28 MK 4000 7.45 G 1819 USA 125 F28 MK 4000 7.50 G 16510 AA 454 B 727-200 7.52 G 20111 DEL 560 F28 MK 4000 7.55 G 18112 AA 320 B 727-200 7.58 G 20113 USA 178 F28 MK 4000 7.60 G 16514 DEL 678 F28 MK 4000 7.62 G 18715 AA 2311 DC9-30-50 7.64 G 20116 AA 2323 DC9-30-50 7.65 G 20117 AA 2345 DC9-30-50 7.66 G 20118 USA 780 B 727-200 7.70 G 16519 AA 356 DC-10-10 7.79 G 20120 AA 430 B 727-200 7.82 G 201
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Aircraft Demands during a Two Hour Peak Hour - Continuation
Flight Type of Aircraft Departure Time Gate(hours + decimal)
21 AA 579 B 727-200 7.83 G 20122 AA 122 A 300-600 7.85 G 20123 AA 065 A 300-600 7.88 G 20124 DEL 032 F 28 MK 4000 7.90 G 18125 AA 012 DC-10-10 7.93 G 20126 USA 005 F 28 MK 4000 8.00 G 16527 AA 4543 B 727-200 8.13 G 20128 DEL 563 F 28 MK 4000 8.20 G 18129 AA 3200 B 727-200 8.24 G 20130 USA 103 F 28 MK 2000 8.30 G 16531 DEL 6782 F 28 Mk 4000 8.32 G 18732 AA 2314 DC9-50 8.40 G 20133 AA 2327 DC9-50 8.43 G 20134 AA 2305 DC9-50 8.52 G 20135 USA 781 B 727-200 8.56 G 16536 AA 357 DC-10-10 8.70 G 20137 AA 5784 B 727-200 8.75 G 20138 AA 053 B 727-200 8.80 G 20139 AA 1222 A 300-600 8.84 G 20140 AA 865 A 300-600 8.92 G 201
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RDU Input Aircraft Schedule (Departures During Two Hour Peak Period)
Flight Type of Aircraft Departure Time Gate(hours + decimal)
1 AA 002 B 727-200 (29) 6.68 G 2012 AA 087 B 737-200 (45) 6.73 G 2013 AA 149 B 737-200 (45) 6.76 G 2014 DEL 096 F 28 MK 2000 (38) 6.80 G 2015 AA 3290 DC9-50 (46) 6.85 G 2016 AA 4670 DC9-50 (46) 6.88 G 2017 AA 274 B 727-200 (29) 7.03 G 2018 DEL 466 F 28 MK 4000 (39) 7.11 G 1819 USA 102 F 28 MK 2000 (38) 7.27 G 16510 AA 338 B 727-200 (29) 7.45 G 201
Numbers in Parenthesis are the aircraft number according to SIMMOD
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SIMMOD Aircraft Number Equivalents Partial List
Aircraft SIMMOD Number Aircraft Engine
Airbus A 300 31 GE CF6-50CBoeing 727-200 29 PW JT8D-15QNBoeing 737-200 45 PW JT8D-9QNBoeing 747-200 2 PW JT9D-FLBoeing 747-100 1 PW JT9D-BDBoeing 757-200 52 PW 2037MD-83 50 PW JT8D-219Douglas DC9-50 46 PW JT8D-17Douglas DC10-10 19 GE CF6-6DFokker F28 MK 2000 38 RR 183-2Fokker F28 MK 4000 39 RR 183-2PSaab SF 340 72 GE CT7-5Canadair CL 600 58 ALF 502LCessna 500 57 PW JTD15-1GASEPV 74 Generic
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IFR Aircraft Intrail Separation Matrix
Leading Aircraft
Aircraft 1 2 3 4Group
1 3.0 4.0 5.0 6.0
2 3.0 3.0 4.0 5.0
3 3.0 3.0 3.0 4.0
4 3.0 3.0 3.0 3.0
σ0 = 18 Standard deviation of intrail delivery error (seconds) for manual ATC
qv = 1.65 Value of cumulative standard normal at P v = 5% (prob. of violations)
Use the following parameters to estimate actual (stochastic separations)
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Sample Results (RDU)
9 08 07 06 05 04 03 02 00
1
2
3
4
5
6
7
8
9
10
Ground DelayAir DelayTotal Delay
Average Aircraft Delay vs. Number of Operations
Aircraft Operations (Aircraft/Hour)
Av
era
ge
A
irc
raft
D
ela
y
(min
ute
s)
RDU File
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Sample Results for RDU (IFR Weather Conditions)
56 76 960
3
6
9
12
15
Air Delay (IFR)
Ground Delay (IFR)
Total Delay (IFR)
Average Aircraft Delays for RDU Under IFR Conditions
Operations per Hour
Ave
rag
e A
ircr
aft
De
lay
(m
inu
tes
)
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Sample Results for RDU (VFR Weather Conditions)
56 76 960
2
4
6
8
10
12
Air Delay (VFR)
Ground Delay (VFR)
Total Delay (VFR)
Average Aircraft Delay for RDU Under VFR Flight Conditions
Aircraft Operations per Hour
Ave
rag
e A
ircr
aft
De
lay
(m
inu
tes
)
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Sample Results for RDU and ATC Sector Study
3 4 5 60
2
4
6
8
10
Ground Delay
Air Delay
Total Delay
RDU Sector Capacity/Delay Sensitivity Study
Sector Capacity (Simultaneous Aircraft)
Ave
rag
e A
ircr
aft
De
lay
(m
inu
tes
)
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Creating Application with Multiple Airports
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All large-scale simulation models allow the creation of scenarios with multiple airports
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Discuss the implications of multiple airport analysis in airport engineering and planning
• Airport interfences
• Airspace planning studies
• Traffic issues
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México City International Airport (México)
North
RWY 23R
RWY 23L
RWY 5L
RWY 5R
RWY 33
RWY 13
250 m.
3,050 m.
3,400 m.
1,950 m.
350 m.
350 m.
350 m.
200 m.
440 m.
500 m.
530 m.
440 m.
440 m.
500 m.
Airline Terminal
Supergate for 50 aircraft
183 m.
183 m.
183 m.
General Aviation Terminal
0 500 1000
Scale in meters
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Acapulco International Airport (Mexico)
RWY 24
RWY 06
RWY 28
550 m.
Airline Terminal
Supergate for 50 aircraft122 m.
0 500 1000
Scale in meters
183 m.
GA Terminal
RWY 10550 m.
350 m.
600 m.
400 m.
400 m.
200 m.3,050 m.
250 m.
1,600 m.
250 m.
250 m.
North
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México City Terminal Area (Sketch)
TEQ VOR
Departure Track
6 nm
5 nm8 nm
10 nm
7 nm
5 nm
5 nm
5 nm
4 nm 10 nm DME Arc
MEX VOR
30 nm
4 nm
6 nm
LOM
12 nm
Arrival Tracks to MEX
1A
2A
3A
4A
1D
2D
Departure Track
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Acapulco Airport Terminal Area (Sketch)
VOR Departure Track onV-15
4 nm
5 nm ACA VOR
6 nm
1A 2A1D
Departure Track
to TEQ VORTo INT SFC
4 nm
4 nm
4 nmArrival Track
From INT CAN
4 nm
4 nm
4 nm
5 nm
8 nm
4 nm
3 nm
3 nm
Arrival Track
4 nm
4 nm2D
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MEX-ACA Airway System
MEX VOR
ACA VOR
J-21 E(Arrival ACA Route)
J-21 W(ACA Departure Route)
V-15
Arrival Tracksto RWY 5R or 5L
Departure Trackson RWY 5R and 5L
TEQ VOR
INT SFC INT CAN
Air Distance betweenACA and MEX VOR'svia J-21 E or J-21 Wis 189.0 nm.
40.0 nm
89.0 nm
60.0 nm
89.0 nm
60.0 nm
North
Air Distance betweenINT CAN and SFC is20.0 nm
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Large-Scale Model Inputs (Typical)
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Airspace files (link and node structures)
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Airfield files (link and node structures)
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Aircraft file (demand or schedule files)
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Ancilliary files (for other tasks like fuel consumption etc.)
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Large-Scale Model Outputs (Typical)
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Aircraft delays (in the airfield and in the airspace)
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Fuel consumption (TAAM and RAMS)
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Arrivals vs. Departures
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Runway utilization patterns
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Travel times and delays (air and ground)
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Hourly delay metrics
• Animation of aircraft operations (a selling point to show decision makers what will happen)
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Use of Animation in Airport Modeling and Simulation
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Serves to identify potential airspace/runway logic problems
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Analysts can examine the simulation in real time or faster
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Identifies visually potential queueing problems at various airfield spots
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Helps non-technical people to understand airport operations (specially good for airport facilities with community complaints)
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Aircraft Move Checks (Ground and Airspace)
Scheduled at a node by the following:
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Aircraft arriving at current node
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Aircraft departing from current node
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Estimated release time for aircraft in holding queue at the current node
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Aircraft departing from an approaching node to current node
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Aircraft leaving holding queue from an approaching node to current node
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Sample SIMMOD Airspace Logic
3
4 2 1
5
Link 3 Link 1
Link 4
Link 2
Route 1
Route 2
Route 3
AirportInterface
Node
Node 2Holding Queue
Description:
Aircraft holding at node 2.
Aircraft at node 3 must hold until node 2 hasan empty holding queue.
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Order of Actions to Impose Delays (SIMMOD)
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Reduce aircraft speed based on node strategy (i.e., ATC speed change request)
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Vectors where wake turbulence on link is not a consideration..
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System cannot track wake during vectoring (ATC responsibility)
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Vector time must be specified for each link
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Hold at node
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New SIMMOD Interface (SIMMOD Plus 7.0)
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Two version of SIMMOD have been developed by the ATAC Corporation (SIMMOD systems integrator for FAA):
• SIMMOD Plus! 7.0
• SIMMOD Pro (based on work done for the Navy)
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The new version of SIMMOD Plus! 7.0 has a very detailed Java-based interface
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Sample SIMMOD Plus! (Builder GUI)Source:ATAC Corporation
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Sample SIMMOD Plus! Interface
Source:ATAC Corporation
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Sample SIMMOD Plus! (Animation)
Source:ATAC Corporation
Node delays shown
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SIMMOD Plus! Aircraft Monitor
Source:ATAC Corporation
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Sample Airspace Study in RAMS (CSSI)
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RAMS Atlanta Airspace Study
ATLAirport
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TAAM
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An airspace and airfield simulation model developed by the Preston Group (Australia) - a Boeing Company
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Good airfield and airspace logic
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Gate-to-Gate simulator (important for some applications)
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Excellent graphics
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Large learning curve
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Limited stochastic behavior (only the aircraft performance is somewhat stochastic in this model)
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TAAM Data Directory Organization
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TAAM Relation to Aircraft Performance
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TAAM uses table functions to approximate the performance of aircraft in the airspace and on the ground
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Currently, 60 aircraft are included in the TAAM database (version 1.2 under Solaris 2.8)
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Transport aircraft and GA vehicles are included in the database
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Technically, it is not difficult to add an aircraft to the TAAM aircraft definition file
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Sample TAAM Aircraft Data
57 # SUPER KING AIR -SHORT/LONG - BE20 S 4 4 M M # Type, Haul, Wake Turb.Cat., Classif., Performance Cat (SID, STAR)030 280 350 # Preferable levels (Low, High), Ceiling (FL)
015 104 114 126 0.0 0.0 0.0 12 # Below level... Min,Norm,Max Climb.IAS(kt) Mach, Fuel C.030 110 160 210 0.0 0.0 0.0 12050 110 160 200 0.0 0.0 0.0 12100 110 160 195 0.0 0.0 0.0 12150 110 140 190 0.0 0.0 0.0 12190 110 140 190 0.0 0.0 0.0 12230 110 130 180 0.0 0.0 0.0 12260 110 130 160 0.0 0.0 0.0 12310 110 120 140 0.0 0.0 0.0 10350 110 120 120 0.0 0.0 0.0 10
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TAAM Studies
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Berlin multi-airport and airspace simulation
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Delta Airlines Atlanta simulation
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FEDEX cargo hub modeling
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FAA ARTCC modeling (Kansas City)
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FAA Super TRACON modeling (Potomac metroplex study)
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NASA Ames studies of advanced ATM concepts
• VPI SATS enroute analysis
• GMU SATS enroute analysis
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DFS Case Study
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Optimization of a complex airspace structure and arrival/departure procedures for the approach control unit serving the three airports of Berlin (Germany)
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Developed a new airspace sectorization scheme with departure routes representing more optimal flight profiles
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This resulted in a reduction of the controllers' coordination “workload” by almost 35%
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Shorter arrival routes and optimized descent profiles
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Reduced fuel burn (due to shorter flying time).
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FEDEX Use of TAAM
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Construction work at Memphis (FEDEX Hub) runways required a change of operating procedures and forced the use of an alternate runway.
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TAAM simulation showed that a 30% delay reduction could be achieved through the use of a new parking plan and departure order
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Revised departure plan produced estimated annual savings in fuel costs of $5 - $10 million for two projects.
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TAAM Study of SATS Enroute Traffic
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A non-funded study was performed at Virginia Tech to study the impacts of SATS traffic in the enroute airspace above the State of Virginia Boundaries
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SATS = Small Aircraft Transportation System (a NASA langley initiative to bring General Aviation aircraft to the masses)
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Limited study of baseline conditions (no SATS), 5% and 10% enplanements in NAS shifting mode to SATS. (Performed by Baik, Farrell, Trani and Koelling)
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Another more comprehensive study being done by George Mason for the Virginia SATS Alliance with inputs from LMI and Virginia Tech
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Scenario Modeled
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Statistics of Scenario Modeled
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Modeled Scenario (Part of ZDC)
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Results of Study (Baik/Trani, 2002)
0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2x 104
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
10 nm
5 nm
3 nm
2 nm
Numbers indicate minimum criteriaseparation
Number of Daily Flights (all types)
Num
ber
of D
aily
Con
flic
ts
Region of Interest = Size of ZDC ARTCC
TAAM simulationsAnalytical Results (AEM model)
Current NASCurrent NAS(baseline)(baseline)
NASNASwith SATSwith SATSCurrent Traffic LevelCurrent Traffic Level
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Other Results (Baik/Trani, 2002)
Conflicts (100-120% Required Separation)
936 1167 1255
3200
44835035
5962
8931
10106
0
2000
4000
6000
8000
10000
12000
2000 2010 2015Year
Num
ber
of C
onfl
icts
0% SATS5% SATS10% SATS
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The Virginia Tech Airport Simulation Model
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Hybrid simulation model
•
Microscopic in nature (second-by-second output if required)
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Models aircraft operations around the airport terminal area (includes sequencing)
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Models ATC-pilot interactions explicitly (voice and datalink)
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Dynamic taxiing plans (true dynamic traffic assignment)
•
Developed under the auspices of the FAA NEXTOR basic research funding (ATM agenda)
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Framework for VTASIMSeparation
RuleNominal Schedule
for ArrivalsNominal Schedule
for Departures
Network Assignment Problem(NAP)
Optimal sequence and schedule
Taxiing NetworkConfiguration
Time-dependent O-D(between gates and runways)
Aircraft Sequencing Problem(ASP)
Optimal taxiing routesfor arrivals and departures
Simulation(VTASM)
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Development of a Simulation Model: VTASIM
•
Existing microscopic simulation models for airport studies:
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SIMMOD, TAAM (airfield and airspace analyses)
•
Airport Machine (airfield analysis)
•
RAMS (airspace analysis)
•
These models are:
•
discrete-event simulation models,
• less accurate in describing the aircraft movement,
• do not describe communication process (ATC-pilot).
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VTASIM is a Hybrid-type Simulation Model
•
A discrete-event simulation model
•
Represents a system by changing the system status at the moments when an event occurs
•
A discrete-time simulation model
•
Represents a system checking and changing the system status at every step size (dt).
•
VTASIM is a hybrid-type simulation model
•
Movement: represented by discrete-time simulation model
• Communication: represented by discrete-event simulation model
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Entities and State Variables in VTASIM
Entities:
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Two types of controllers (i.e., local and ground controllers),
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Two types of flights (i.e., departing and arriving flights), and
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Facilities including gates, taxiways, runways, etc.
State Variables:
•
Controllers: controlling state, next communication time,
•
Flights: communication state, next communication time, movement state, next movement time, speed, acceleration, position, etc.,
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•
Gates, taxiways, runways: current flight(s).
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Ground Control Model Features
•
Communication interactions between ATC controllers/data link and each aircraft is explicitly modeled
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Delay analysis. There are two types of delay:
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Traffic delay due to the traffic congestion on taxiway/runway
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Communication delay due to the controller/data link communications
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Dynamic aircraft-following logic
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Static and dynamic route guidance for taxing
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State Diagram for COM (Voice Channel)
SendingRequest
(t1)Is controller busy?
WaitingCommand
(t2)
ReceivingCommand
(t3)
SendinConfirm
(t4)
Received clea
WaitContact
fromController
Ready tocomm.
Put this flight strip toprogressing box.
End Communication
No
Yes
Start Communication
ReceivingCommand
(t3)
SendingConfirm.
(t4) No(i.e., Delayed)
WaitNextComm.
(t0)
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State Diagram for Controller’s Data Strips
CompletedFlight Strips
PendingFlight Strips
ProcessingFlight Strips
CompletedFlight Strips
PendingFlight Strips
ProcessingFlight Strips
Ground controller’sflight strip organization
Local controller’sflight strip organization
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Algorithm: Dynamic Taxiing Route Plann = 1
Assign the nth
aircraft on thelinks involved inthe TDSP overtime intervals
Updatelink travel times
n = last vehicle?
Find TDSPfor the nth aircraft
(by usingTDSP algorithm)
n = n+1
End
No
Yes
Considers time-dependent
Employs an incremental
Based on time-dependent
network assignment strategy
network loading
time-dependent
shortest path algorithm
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Algorithm: Dynamic Taxiing Route Plan
1028
2009
2012
2010
2011
2007
2005
2014
2015
2016
20182019
2020 2021
2008
1006
1007 1008
1009 1010
1012
1 1011
1014
1013
10151016
1017
10181019
1020 1021
1023
1024 1025
10271026
1031
10301029
1032
1033
2017
2
3
4
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8
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122013
2006
321
33
01
1028
2009
2012
2010
2011
2007
2005
2014
2015
2016
20182019
2020 2021
2008
1006
1007 1008
1009 1010
1012
1 1011
1014
1013
10151016
1017
10181019
1020 1021
1023
1024 1025
10271026
1031
10301029
1032
1033
2017
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01 Statically assigned path Time-dependent assigned path
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Aircraft Following Model
Basic equations of motion to characterize the aircraft taxiing following model
vt+dt
Ht
)1(t
jfdtt H
Hvv −=∆+ Speed equation of motion
tvva tdt
ttn ∆−= +
∆++ /)( 11 Acceleration equation of motio
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74
Conflict Detection and Resolution Model
15.6(sec)
the current operation direction
20.8(sec)
30.7(sec)
Start pointof Intersection
Current positionof flight
Expected arrival time tothe common intersction
(ETi)F2
Legend :
First flight on this link(Need to check
the potential collision) Conflicting flights comingtoward thecommon intersection
Second or later flights on this link(do follow the leading flight
by car-following logic)
F1 F3
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75
Four Phases of the Landing Procedure
FL : FlareFR: Free-rollingBR: BrakingCO: Coasting
Runw
Exit
FL
Disatan
Air SpeedAltitude
FR
BR
CO
Touchdown Point
Exit point
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76
Example of Output File (1): Log File
Second-by-second statistics can be obtained in VTASIM
Time = 320.000DEP_1 (4.27860, 7.23847) readyToCommunicate clearToTakeOff rolling 228.557 5.65931 2006 -> 2005 347.582 322.875 8907.85 DEP_2 (3.44770, 3.71363) readyToCommunicate clearToTaxi taxiingToDepQue 27.3409 0.000000 1031 -> 2018 782.058 727.237 3832.22
Aircraft ID and PositiAcft. COMM StateAcft. Permission Acft. speed, accel. anlink information
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Example of Output File (2): Summary File
------------------------------- SUMMARY -------------------------------
Flight (Departure DEP_1, B727-100, Gate 1, Runway 36)Enters into the simulation at : 1 sec. Taxiing Duration : 73 - 217Taxiing Delay : 2.22827Nominal Takeoff Time (= NTOT) : 186Sequenced Takeoff Time (= STOT) : 268Actual Takeoff Time (= ATOT) : 289Runway Occupancy Time (= ROT) : 289 - 328Sequenced Delay (= ATOT - STOT) : 21Runway Delay (= ATOT - NTOT) : 103
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78
Local Controller Workload Metric
Local controller’s workload (2)(Utilization factor = 0.607)
0
1
2
3
4
5
6
7
8
9
10
0 200 400 600 800 1000 1200 1400 1600 1800
Time (second)
No
. of
airc
raft
co
nta
ctin
g c
on
tro
ller
Time (seconds)
Air
craf
t Und
er C
ontr
ol
1800
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79
Delay Curves for Mixed Runway Operations
10 15 20 25 30 35 40 45 50 55 600
100
200
300
400
500
600
700
800
900
Number of Operations
Ave
rage
Del
ay p
er A
ircra
ft (s
econ
ds)
TextEnd
Aircraft Operations per Hour
Ave
rage
Air
craf
t Del
ay (
seco
nds)
Simulation +Average o
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80
Sample Aircraft Delays Curves
Voice channel - three assignment techniques studied
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81
Sample Delay Curves (datalink analysis)
Datalink active - three assignment techniques studied