Explanation of Several Simulation Models

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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1
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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2
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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3
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 postprocessors (1983)
•
Validation of the SIMMOD Simulation Model (19851991)
•
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
Flight
Schedules
ATC Policies
and
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
Node 295
Node
296
4 n.m.
Node 294
Arrival Track 1
30 Degrees
10 n.m.
4 n.m.
RDU Airport
Node
305
5 n.m.
Departure Track 1
Node
297
LOM 1
Node 267
05L
6 n.m.
Node
304
23R
20 Degrees
6 n.m.
Node 271
LOM 2
6 n.m.
05R
23L
6 n.m.
Node
302
5 n.m.
Node
280
Departure Track 2
4 n.m.
Node
301
20 Degrees
Node
307
4 n.m.
10 n.m.
Node 300
Node 299
Arrival Track 2
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30 Degrees
19
Graphical Depiction of RDU Airfield
Configuration
Node 17
G 201 (American)
G 165
(USAir)
Runway 5L-23R
Node 16
C
A
B
Proposed Cargo
Terminal
Runway 5R-23L
Node 245
Node 11
G 181 (Delta)
RDU Airport with Supergates Shown
Concourse A
Concourse C
Gate Capacities
- 15 aircraft
Concourse B
- 30 aircraft
Cargo Complex
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- 10 aircraft
- 30 aircraft
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RDU Baseline Input Parameters (Aircraft
Demands during a Two Hour Peak Hour)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Flight
Type of Aircraft
Departure Time
(hours + decimal)
Gate
AA 231
AA 450
AA 120
AA 003
AA 052
AA 2231
AA 123
DEL 200
USA 125
AA 454
DEL 560
AA 320
USA 178
DEL 678
AA 2311
AA 2323
AA 2345
USA 780
AA 356
AA 430
B 727-200
B 727-200
B 737-200
F28 MK 2000
F28 MK 2000
DC9-30-50
B 727-200
F28 MK 4000
F28 MK 4000
B 727-200
F28 MK 4000
B 727-200
F28 MK 4000
F28 MK 4000
DC9-30-50
DC9-30-50
DC9-30-50
B 727-200
DC-10-10
B 727-200
7.15
7.20
7.23
7.24
7.26
7.32
7.41
7.45
7.50
7.52
7.55
7.58
7.60
7.62
7.64
7.65
7.66
7.70
7.79
7.82
G 201
G 201
G 201
G 201
G 201
G 201
G 201
G 181
G 165
G 201
G 181
G 201
G 165
G 187
G 201
G 201
G 201
G 165
G 201
G 201
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Aircraft Demands during a Two Hour Peak
Hour - Continuation
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
Flight
Type of Aircraft
Departure Time
(hours + decimal)
Gate
AA 579
AA 122
AA 065
DEL 032
AA 012
USA 005
AA 4543
DEL 563
AA 3200
USA 103
DEL 6782
AA 2314
AA 2327
AA 2305
USA 781
AA 357
AA 5784
AA 053
AA 1222
AA 865
B 727-200
A 300-600
A 300-600
F 28 MK 4000
DC-10-10
F 28 MK 4000
B 727-200
F 28 MK 4000
B 727-200
F 28 MK 2000
F 28 Mk 4000
DC9-50
DC9-50
DC9-50
B 727-200
DC-10-10
B 727-200
B 727-200
A 300-600
A 300-600
7.83
7.85
7.88
7.90
7.93
8.00
8.13
8.20
8.24
8.30
8.32
8.40
8.43
8.52
8.56
8.70
8.75
8.80
8.84
8.92
G 201
G 201
G 201
G 181
G 201
G 165
G 201
G 181
G 201
G 165
G 187
G 201
G 201
G 201
G 165
G 201
G 201
G 201
G 201
G 201
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RDU Input Aircraft Schedule (Departures
During Two Hour Peak Period)
1
2
3
4
5
6
7
8
9
10
Flight
Type of Aircraft
AA 002
AA 087
AA 149
DEL 096
AA 3290
AA 4670
AA 274
DEL 466
USA 102
AA 338
B 727-200 (29)
B 737-200 (45)
B 737-200 (45)
F 28 MK 2000 (38)
DC9-50 (46)
DC9-50 (46)
B 727-200 (29)
F 28 MK 4000 (39)
F 28 MK 2000 (38)
B 727-200 (29)
Departure Time
(hours + decimal)
Gate
6.68
6.73
6.76
6.80
6.85
6.88
7.03
7.11
7.27
7.45
G 201
G 201
G 201
G 201
G 201
G 201
G 201
G 181
G 165
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
Boeing 727-200
Boeing 737-200
Boeing 747-200
Boeing 747-100
Boeing 757-200
MD-83
Douglas DC9-50
Douglas DC10-10
Fokker F28 MK 2000
Fokker F28 MK 4000
Saab SF 340
Canadair CL 600
Cessna 500
GASEPV
31
29
45
2
1
52
50
46
19
38
39
72
58
57
74
GE CF6-50C
PW JT8D-15QN
PW JT8D-9QN
PW JT9D-FL
PW JT9D-BD
PW 2037
PW JT8D-219
PW JT8D-17
GE CF6-6D
RR 183-2
RR 183-2P
GE CT7-5
ALF 502L
PW JTD15-1
Generic
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IFR Aircraft Intrail Separation Matrix
Leading Aircraft
Aircraft
Group
1
2
3
4
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
Use the following parameters to estimate actual (stochastic separations)
σ0 = 18
Standard deviation of intrail delivery error (seconds) for manual ATC
qv = 1.65
Value of cumulative standard normal at P
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v = 5% (prob. of violations)
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Sample Results (RDU)
Average Aircraft Delay vs. Number of Operations
10
RDU File
Average Aircraft
Delay (minutes)
9
8
7
6
Ground Delay
Air Delay
Total Delay
5
4
3
2
1
0
20
30
40
Aircraft
50
Operations
60
70
80
90
(Aircraft/Hour)
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Sample Results for RDU (IFR Weather
Conditions)
Average Aircraft Delays for RDU Under IFR Conditions
15
Average Aircraft
Delay (minutes)
12
Air Delay (IFR)
9
Ground Delay (IFR)
Total Delay (IFR)
6
3
0
56
76
96
Operations per Hour
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Sample Results for RDU (VFR Weather
Conditions)
Average Aircraft Delay for RDU Under VFR Flight Conditions
12
Average Aircraft
Delay (minutes)
10
8
Air Delay (VFR)
Ground Delay (VFR)
Total Delay (VFR)
6
4
2
0
56
76
96
Aircraft Operations per Hour
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Sample Results for RDU and ATC Sector
Study
RDU Sector Capacity/Delay Sensitivity Study
10
Average Aircraft
Delay (minutes)
8
6
Ground Delay
Air Delay
Total Delay
4
2
0
3
4
5
Sector Capacity
(Simultaneous Aircraft)
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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)
RWY 23R
500 m.
RWY 23L
Supergate for 50 aircraft
440 m.
0
500
RWY 13
1000
Scale in meters
440 m.
North
Airline Terminal
3,400 m.
530 m.
183 m.
3,050 m.
1,950 m.
500 m.
183 m.
350 m.
440 m.
350 m.
200 m.
RWY 33
350 m.
RWY 5L
General Aviation
Terminal
183 m.
RWY 5R
250 m.
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Acapulco International Airport (Mexico)
Airline Terminal
GA Terminal
400 m.
RWY 10
RWY 24
North
183 m.
400 m.
550 m.
550 m.
250 m.
350 m.
250 m.
600 m.
250 m.
1,600 m.
122 m.
3,050 m.
200 m.
RWY 06
RWY 28
Supergate for 50 aircraft
0
500
1000
Scale in meters
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México City Terminal Area (Sketch)
1A
Departure Track
5 nm
2A
1D
12 nm
10 nm
5 nm
4 nm
10 nm DME Arc
5 nm
5 nm
8 nm
6 nm
7 nm
6 nm
MEX VOR
3A
LOM
2D
4 nm
Arrival Tracks to MEX
Departure Track
30 nm
4A
TEQ VOR
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Acapulco Airport Terminal Area (Sketch)
To INT SFC
Departure
Track
4 nm
From INT CAN
to TEQ VOR
VOR
Departure
Track on
V-15
1D
4 nm
Arrival
Track
Arrival
Track
2A
1A
2D
4 nm
4 nm
4 nm
4 nm
4 nm
4 nm
6 nm
5 nm
5 nm
ACA VOR
4 nm
3 nm
8 nm
3 nm
4 nm
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MEX-ACA Airway System
MEX VOR
Departure Tracks
on RWY 5R and 5L
40.0 nm
Arrival Tracks
to RWY 5R or 5L
J-21 W
(ACA Departure Route)
TEQ VOR
89.0 nm
89.0 nm
V-15
INT SFC
Air Distance between
ACA and MEX VOR's
via J-21 E or J-21 W
is 189.0 nm.
INT CAN
60.0 nm
60.0 nm
North
J-21 E
(Arrival ACA Route)
Air Distance between
INT CAN and SFC is
20.0 nm
ACA VOR
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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
Description:
Aircraft holding at node 2.
Aircraft at node 3 must hold until node 2 has
an empty holding queue.
3
Route 1
Link 2
4
Node 2
Holding Queue
Airport
Interface
Node
Link 1
Link 3
2
Route 2
1
Link 4
5
Route 3
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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)
Node delays shown
Source:ATAC Corporation
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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
ATL
Airport
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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
Climb.IAS(kt) Mach, Fuel C.
030 110 160 210 0.0 0.0 0.0
050 110 160 200 0.0 0.0 0.0
100 110 160 195 0.0 0.0 0.0
150 110 140 190 0.0 0.0 0.0
190 110 140 190 0.0 0.0 0.0
230 110 130 180 0.0 0.0 0.0
260 110 130 160 0.0 0.0 0.0
310 110 120 140 0.0 0.0 0.0
350 110 120 120 0.0 0.0 0.0
12 # Below level... Min,Norm,Max
12
12
12
12
12
12
12
10
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)
Number of Daily Conflicts
Region of Interest = Size of ZDC ARTCC
18000
Current NAS
(baseline)
TAAM simulations
Analytical Results (AEM model)
16000
14000
12000
10000
8000
6000
10 nm
Numbers indicate minimum
separation criteria
5 nm
Current Traffic Level
NAS
with SATS
3 nm
4000
2 nm
2000
0
0.6
0.8
1
1.2
1.4
1.6
1.8
2
2.2
4
x 10
Number of Daily Flights (all types)
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Other Results (Baik/Trani, 2002)
Conflicts (100-120% Required Separation)
Number of Conflicts
12000
10106
10000
8931
8000
5962
6000
4000
2000
5035
4483
3200
1255
1167
936
0
2000
2010
Year
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2015
0% SATS
5% SATS
10% 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 VTASIM
Nominal Schedule
for Arrivals
Separation
Rule
Nominal Schedule
for Departures
Aircraft Sequencing Problem
(ASP)
Optimal sequence and schedule
Time-dependent O-D
(between gates and runways)
Taxiing Network
Configuration
Network Assignment Problem
(NAP)
Simulation
(VTASM)
Optimal taxiing routes
for arrivals and departures
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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
•
•
A discrete-time simulation model
•
•
Represents a system by changing the system status at the
moments when an event occurs
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)
Start Communication
Put this flight strip to
progressing box.
Is controller busy?
No
Sending
Request
(t1)
Waiting
Command
(t2)
Receiving
Command
(t3)
Sendin
Confirm
(t4)
Yes
WaitNext
Comm.
(t0)
End Communication
Ready to
comm.
Sendin g
Confirm.
(t4)
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Receiving
Command
(t3)
Wait
Contact
from
Controller
Received clea
No
(i.e., Delayed)
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State Diagram for Controller’s Data Strips
Completed
Flight Strips
Completed
Flight Strips
Processing
Flight Strips
Processing
Flight Strips
Pending
Flight Strips
Pending
Flight Strips
Ground controller’s
flight strip organization
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Local controller’s
flight strip organization
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Algorithm: Dynamic Taxiing Route Plan
n=1
Find TDSP
for the n th aircraft
(by using
TDSP algorithm)
Considers time-dependent
network loading
Employs an incremental
time-dependent
network assignment strategy
Based on time-dependent
shortest path algorithm
Assign the nth
aircraft on the
links involved in
the TDSP over
time intervals
Update
link travel times
n = last vehicle?
No
n = n+1
Yes
End
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Algorithm: Dynamic Taxiing Route Plan
1006
1006
1008
1007
1008
1007
2005
2005
21
2006
1010
21
1009
1
1009
1013
1011
2009
2
1012
1012
3
2007
1015
4
2011
1017
2011
5
1017
2010
2012
1021
2013
2014
11
1026
33
9
11
2016
1028
2020
3
33
1026
1025
1027
2016
1030
1029
1031
2017
2018
2014
10
1031
1033
2015
1023
1024
1027
2012
1021
2013
12
1025
1030
1029
1032
1020
8
10
1024
1028
7
2015
1023
9
1019
6
8
12
2010
1018
1019
1020
2007
1016
1018
7
2008
1015
1016
6
2009
2
2008
1014
3
4
1013
1011
1014
5
2006
1010
1
2017
1032
2020
2021
3
01
Statically assigned path
2018
1033
2019
2019
2021
01
Time-dependent assigned path
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Aircraft Following Model
Ht
vt+dt
Basic equations of motion to characterize the aircraft
taxiing following model
vtd+ ∆t = v f (1 −
Hj
Ht
)
ant ++∆1 t = (vtd+1 − vt ) / ∆t
Speed equation of motion
Acceleration equation of motio
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Conflict Detection and Resolution Model
Second or later flights on this link
(do follow the leading flight
by car-following logic)
First flight on this link
(Need to check
the potential collision)
Conflicting flights coming
toward thecommon intersection
15.6(sec)
20.8(sec)
F1
Start point
of Intersection
Legend :
30.7(sec)
the current operation direction
F2
Current position
of flight
F3
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Expected arrival time to
the common intersction
(ET i)
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Four Phases of the Landing Procedure
Exit point
Touchdown Point
Air Speed
Altitude
Disatan
Runw
CO
Exit
BR
FR
FL
FL : Flare
FR: Free-rolling
BR: Braking
CO: Coasting
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Example of Output File (1): Log File
Second-by-second statistics can be obtained in VTASIM
Time = 320.000
DEP_1 (4.27860, 7.23847)
readyToCommunicate
clearToTakeOff rolling
228.557 5.65931 2006 -> 2005
347.582 322.875 8907.85
Aircraft ID and Positi
Acft. COMM State
Acft. Permission
Acft. speed, accel. an
link information
DEP_2 (3.44770, 3.71363)
readyToCommunicate
clearToTaxi
taxiingToDepQue
27.3409 0.000000 1031 -> 2018
782.058 727.237 3832.22
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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 - 217
Taxiing Delay
: 2.22827
Nominal Takeoff Time (= NTOT)
: 186
Sequenced Takeoff Time (= STOT)
: 268
Actual Takeoff Time (= ATOT)
: 289
Runway Occupancy Time (= ROT)
: 289 - 328
Sequenced Delay
(= ATOT - STOT) : 21
Runway Delay
(= ATOT - NTOT) : 103
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Local Controller Workload Metric
Local controller’s workload (2)
(Utilization factor = 0.607)
10
Aircraft Under Control
No. of aircraft contacting controller
9
8
7
6
5
4
3
2
1
0
0
200
400
600
800
1000
1200
1400
1600
1800
1800
(second)
TimeTime
(seconds)
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Delay Curves for Mixed Runway Operations
Average Delay per Aircraft (seconds)
Average Aircraft Delay (seconds)
900
800
Simulation +
Average
o
700
600
500
400
300
TextEnd
200
100
0
10
15
20
25
30
35
40
Number of Operations
45
50
55
60
Aircraft Operations per Hour
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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
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