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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

•

Review of current large-scale simulation models

•

Review some of their strengths and weaknesses

•

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

•

These models mimic the behavior of aircraft in complex airspace and airport systems

•

Typically these models use a discrete event simulation approach (see another handout on this) to move aircraft among airport and airspace resources

•

Airport and airspace resources are considered objects like runways, taxiways, gates and airspace links

•

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

•

To estimate airport measures of effectiveness to estimate delays curves for an airport subject to some airport schedule (or demand) scenario

•

Delays are usually defined as the difference between unimpeded and actual travel times

•

Estimate utilization of airport resources (such as gates, runways, taxiways, etc.)

•

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

•

An airspace and airfield simulation model developed by the FAA in the last two decades

•

Good airfield and airspace logic

•

Gate-to-Gate simulator (important for some applications)

•

2D graphics (except for workstation version)

•

Validated in the period 1985-1991

•

Cost: $5,900 per copy for SIMMOD Plus! version 7

• Large learning curve (in general for SIMMOD)

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TAAM

•

An airspace and airfield simulation model developed by the Preston Group (Australia) - a Boeing Company

•

Good airfield and airspace logic

•

Gate-to-Gate simulator (important for some applications)

•

Excellent graphics

•

Not validated although in use by many airlines and research organizations

•

Cost: ~$300,000 per copy

• Large learning curve

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TAAM Model

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RAMS

•

An airspace simulation model developed by Eurocontrol (equivalent of FAA ar traffic services in the US)

•

Only airspace simulation

•

Developed using MODSIM - a simulation language developed by CACI

•

Good aircraft conflict detection and resolution

•

Price varies from $7,500-$22,000 Euros (version 5.2 )

•

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)

•

The simulation moves from one scheduled event to the next one

•

Keeps track of simulation events in an orderly fashion

•

Many internal events are generated for each external event.

•

The simulation clock is based on the current event’s scheduled initiation, not elapsed clock time

•

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

•

Runway closure impacts

•

Analysis and delays of airfield ground operations

•

Taxiway closures and upgrades

•

Cargo and passenger terminal impact studies

•

Pavement management

•

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:

•

Safe in ascertaining the impact of operational changes

•

Inexpensive to use

•

Flexible to account for special airport/airspace conditions

•

Provide answers to airspace and airport operational analysts

•

Help to understand complex operational phenomena

•

Improve decision-making ability

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SIMMOD’s History

•

Development of the Airport/Airspace Delay Model (ADM)(1978-1979)

•

Development of SIMMOD fuel consumption post-processors (1983)

•

Validation of the SIMMOD Simulation Model (1985-1991)

•

IBM and Compatible version 1.2 available in late1992

•

Virginia Tech implements runway and HS runway exit logic changes (1995)

•

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

•

Builds airspace and airports from inputs that describe the physical layout.

•

Simulates all flights plane-by-plane.

•

Uses external data to initiate flights.

•

Resolves all conflicts.

•

Monitors time and fuel consumed along each segment.

•

Generates reports of some of the following: Statistical Summaries, Graphics and Animation

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Sample Application (SIMMOD)

•

Raleigh-Durham International Airport (RDU) in North Carolina represents a typical example of a medium size hub airport in the US

•

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

•

All large-scale simulation models allow the creation of scenarios with multiple airports

•

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)

•

Airspace files (link and node structures)

•

Airfield files (link and node structures)

•

Aircraft file (demand or schedule files)

•

Ancilliary files (for other tasks like fuel consumption etc.)

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Large-Scale Model Outputs (Typical)

•

Aircraft delays (in the airfield and in the airspace)

•

Fuel consumption (TAAM and RAMS)

•

Arrivals vs. Departures

•

Runway utilization patterns

•

Travel times and delays (air and ground)

•

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

•

Serves to identify potential airspace/runway logic problems

•

Analysts can examine the simulation in real time or faster

•

Identifies visually potential queueing problems at various airfield spots

•

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:

•

Aircraft arriving at current node

•

Aircraft departing from current node

•

Estimated release time for aircraft in holding queue at the current node

•

Aircraft departing from an approaching node to current node

•

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)

•

Reduce aircraft speed based on node strategy (i.e., ATC speed change request)

•

Vectors where wake turbulence on link is not a consideration..

•

System cannot track wake during vectoring (ATC responsibility)

•

Vector time must be specified for each link

•

Hold at node

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New SIMMOD Interface (SIMMOD Plus 7.0)

•

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)

•

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

•

An airspace and airfield simulation model developed by the Preston Group (Australia) - a Boeing Company

•

Good airfield and airspace logic

•

Gate-to-Gate simulator (important for some applications)

•

Excellent graphics

•

Large learning curve

•

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

•

TAAM uses table functions to approximate the performance of aircraft in the airspace and on the ground

•

Currently, 60 aircraft are included in the TAAM database (version 1.2 under Solaris 2.8)

•

Transport aircraft and GA vehicles are included in the database

•

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

•

Berlin multi-airport and airspace simulation

•

Delta Airlines Atlanta simulation

•

FEDEX cargo hub modeling

•

FAA ARTCC modeling (Kansas City)

•

FAA Super TRACON modeling (Potomac metroplex study)

•

NASA Ames studies of advanced ATM concepts

• VPI SATS enroute analysis

• GMU SATS enroute analysis

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DFS Case Study

•

Optimization of a complex airspace structure and arrival/departure procedures for the approach control unit serving the three airports of Berlin (Germany)

•

Developed a new airspace sectorization scheme with departure routes representing more optimal flight profiles

•

This resulted in a reduction of the controllers' coordination “workload” by almost 35%

•

Shorter arrival routes and optimized descent profiles

•

Reduced fuel burn (due to shorter flying time).

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FEDEX Use of TAAM

•

Construction work at Memphis (FEDEX Hub) runways required a change of operating procedures and forced the use of an alternate runway.

•

TAAM simulation showed that a 30% delay reduction could be achieved through the use of a new parking plan and departure order

•

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

•

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

•

SATS = Small Aircraft Transportation System (a NASA langley initiative to bring General Aviation aircraft to the masses)

•

Limited study of baseline conditions (no SATS), 5% and 10% enplanements in NAS shifting mode to SATS. (Performed by Baik, Farrell, Trani and Koelling)

•

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

•

Hybrid simulation model

•

Microscopic in nature (second-by-second output if required)

•

Models aircraft operations around the airport terminal area (includes sequencing)

•

Models ATC-pilot interactions explicitly (voice and datalink)

•

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:

•

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:

•

Two types of controllers (i.e., local and ground controllers),

•

Two types of flights (i.e., departing and arriving flights), and

•

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

•

Delay analysis. There are two types of delay:

•

Traffic delay due to the traffic congestion on taxiway/runway

•

Communication delay due to the controller/data link communications

•

Dynamic aircraft-following logic

•

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

5

6

7

8

9

10

11

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

2

3

4

5

6

7

8

9

10

11

122013

2006

321

33

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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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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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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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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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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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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Sample Aircraft Delays Curves

Voice channel - three assignment techniques studied

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Sample Delay Curves (datalink analysis)

Datalink active - three assignment techniques studied