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Unformatted text preview: Analysis of Air Transportation Systems
Airport Capacity  Introduction
Dr. Antonio A. Trani
Associate Professor of Civil and Environmental Engineering
Virginia Polytechnic Institute and State University Blacksburg, Virginia
March 2005 Virginia Tech 1 Methodologies to Assess Airport Capacity
The capacity of an airport is a complex issue. Several
elements of the airport facility have to be examined.
Namely: a) Airside and b) Landside components.
Runway
Access Road Airside Gates Taxiways Terminal Landside Runway Virginia Tech 2 Airport and Airspace Components
The following components of NAS need to be
examined:
a) Airside
 Airspace
 Runways
 Taxiways
b) Landside
 Gates
 Terminal
 Access road
Virginia Tech 3 Methodologies to Study Airport Capacity/
Delay
• Analytic models
 Easier and faster to execute
 Good for preliminary airport/airspace planning
(when demand function is uncertain)
 Results are generally less accurate but appropriate • Simulationbased models
 Require more work to execute
 Good for detailed assessment of existing facilities
 Results are more accurate and microscopic in nature Virginia Tech 4 Methodologies in Use to Study Capacity/
Delay
• Analytic models
 Timespace analysis
 Queueing models (deterministic and stochastic) • Simulationbased models
 Monte Carlo Simulation
 Continuous simulation models
 Discreteevent simulation models Virginia Tech 5 TimeSpace Analysis
• A solid and simple technique to assess runway and
airspace capacity if the headway between aircraft is
known • The basic idea is to estimate an expected headway,
E(h), and then estimate capacity as the inverse of the
expected headway 1
Capacity = E(h) (1) E ( h ) is expressed in time units (e.g., seconds)
Virginia Tech 6 TimeSpace Analysis Nomenclature δ ij is the minimum separation matrix (nm)
T ij is the headway between two successive aircraft (s)
δ is the minimum arrivaldeparture separation (nm)
ROT i is the runway occupancy time for aircraft i (s)
σ 0 is the standard deviation of the intrail delivery error
(s) V i is the speed of aircraft i (lead aircraft) in knots
Virginia Tech 7 TimeSpace Analysis Nomenclature V j is the trailing aircraft speed (knots)
γ is the common approach length (nm)
B ij is the buffer times matrix between successive aircraft
(s) q v is the value of the cumulative standard normal at
probability of violation p v
p v is the probability of violation of the minimum
separation criteria between two aircraft
Virginia Tech 8 Final Approach and Landing Processes
Space Runway ROTi TDi ROTj Tj Ti
V
i γ Time V
j Entry Gate Virginia Tech 9 Possible Outcomes of a Single Runway TimeSpace Diagram
Since aircraft approaching a runway arrive in a random
pattern we distinguish between two possible scenarios:
• Opening Case  Instance when the approach speed of
lead aircraft is higher than trailing aircraft ( V i > V j ) • Closing case  Instance when the approach of the lead
aircraft is less than that of the trailing aircraft ( V i ≤ V j ) Virginia Tech 10 Opening Case Diagram (Arrivals Only)
Space ROTi ROTj Ti Runway Tj
V
j V
i γ 1 1 Time
V >V
i
j δij
Entry Gate Virginia Tech 11 Opening Case (Equations)
Error free headway, T ij = T j – T i , (no pilot and ATC
controller error) assuming control is exercised as the
lead aircraft passes the entry gate, 11
δ ij  –  T ij =  + γ  V j V i
Vj (2) Position error buffer time (with pilot and ATC
controller error) 11  –  B ij = σ o q v – δ ij  V j V i or zero if B ij < 0. Virginia Tech (3) 12 Understanding Position Errors
Distribution of
Aircraft Position
No Buffer
50% 50%
δij Real Aircraft Position Runway Distribution of
Aircraft Position
With Buffer 5%
σοqvVj δij
Virginia Tech Runway 13 Closing Case Diagram (Arrivals Only)
Space ROTi ROTj Ti Runway Tj V
i γ Time δij 1 V <V
i
j V
j
1 Entry Gate Virginia Tech 14 Closing Case (Equations)
Error free headway, T ij = T j – T i (no pilot and ATC
controller error) with the minimum separation enforced
when the lead aircraft passes the runway threshold, δ
T ij = ij
Vj (4) Position error buffer time (with pilot and ATC
controller error) is, B ij = σ o q v (5) Virginia Tech 15 Mixed Operations Diagram
Space Runway TDk ROTi Ti T1 ROTj T2 Tj Time
δ V
i γ G V
j T1 = Ti + RΟΤι
T2 = T j  δ / V j
Gap (G) exist if T2  T1 > 0 Entry Gate TDi is the departure
runway occupancy time E[Tij + Bij] = E[δ / Vj] + E[ROTi] + (n1) E(TDk) + Ε(τ)
Virginia Tech 16 Mixed Operations Notes
• The arriving aircraft leave natural gaps in the time
space diagram • When gaps (G) are sufﬁciently long, ATC controllers
can schedule one or more departures in the gap • The size of the gaps depends on:
 Runway occupancy time (for lead aircraft)
 Runway occupancy time for departing aircraft
 Minimum departuredeparture headway (seconds)
 Minimum arrivaldeparture separation (δ) Virginia Tech 17 Mixed Operations Notes
• In the U.S. the current minimum separation between
arrivals and departures (δ) is 2 nautical miles
Deﬁne: • T 1 as the time when the lead aircraft completes the
landing roll (i.e., exits the runway plane) • T 2 as the time when the following arriving aircraft is
(δ) from the runway threshold • The gap (G) is the time difference between T 2 and T 1 . G = T2 – T1 (6) Virginia Tech 18 Mixed Operations (Gap Analysis)
Mathematically, T 1 = T i + ROT i (7) and δ
T 2 = T j – Vj (8) then δ
G = T j –  – ( T i + ROT i )
Vj Virginia Tech (9) 19 Mixed Operations (Gap Analysis) δ
G = ( T j – T i ) –  – ROT i
Vj (10) Note that, ( T j – T i ) is the actual headway between the
lead and following aircraft ( T ij + B ij ). This actual
headway includes the buffer times since air trafﬁc
control will apply those buffers to each successive
arrival pair. Our analysis now concentrates in ﬁnding
suitable gaps between successive aircraft arrivals
leaving. Virginia Tech 20 Gap Analysis
Assume that we would like to ﬁnd instances such that
the gap is zero. This is the limiting case to schedule one
departure between successive arrivals. δ
0 = ( T j – T i ) –  – ROT i
Vj (11) knowing δ
0 = ( T ij + B ij ) –  – ROT i
Vj Virginia Tech (12) 21 Gap Analysis δ
( T ij + B ij ) =  + ROT i
Vj (13) For n departures in gap k the expected value of T ij + B ij
has to be longer than: δ
( T ij + B ij ) =  + ROT i + ( n – 1 ) TD k
Vj (14) where TD k is the runway occupancy time of departure
k. This expression typically applies under VFR
conditions because controllers can dispatch aircraft as Virginia Tech 22 soon as the previous departure clears the runway
end (provided that the lead aircraft turns quickly
away from runway heading).
Under IMC conditions, the runway occupancy time for a
departing aircraft TD k is smaller than the minimum
headway allowed between departures. This happens
because under IMC conditions aircraft are expected to
follow a prescribed climb procedure and usually
navigate to a departure ﬁx before changing heading.
Let ε ij be the minimum departuredeparture headway
applied by air trafﬁc control. Equation (14) can then be
modiﬁed to estimate the availability of a gap to release
n departures.
Virginia Tech 23 Gap Analysis δ
( T ij + B ij ) =  + ROT i + ( n – 1 ) ε ij
Vj (15) One ﬁnal term usually added to this equation is a pilot
reaction time term to account for a possible delay time
(departing aircraft) to initiate the takeoff roll. This time
is justiﬁed because jet engines used in transport aircraft
take a few seconds to “spool up” and generate full
thrust. Let τ be the time delay (in seconds) for the
departing aircraft. Virginia Tech 24 Gap Analysis
Adding the time delay term Equation (14) becomes, δ
( T ij + B ij ) =  + ROT i + ( n – 1 ) ε ij + τ
Vj (16) Since ( T ij + B ij ) is calculated as an expected value in
the analysis for arrivals only, δ
E ( T ij + B ij ) ≥ E  + E ( ROT i ) + V j (17) ( n – 1 ) E ( ε ij ) + E ( τ ) Virginia Tech 25 Gap Analysis
The use of Equation (17) allows us to estimate whether
the natural gaps left by successive arrivals (expressed as
the expected value of ( T ij + B ij ) ) is large enough to
schedule n departures.
The practical use of Equation (17) is to compare the
actual headways between successive arrivals ( T ij + B ij )
against the sum of all four terms in the right hand side of
Equation (17). We do this for various possible departure
scenarios that include n departures (typically 1, 2, 3, ...
6 departures). Virginia Tech 26 Practical Values of ArrivalArrival
Separations (δij)
Table 1. Typical Intrail Separations (nautical miles) Near Runways at
Medium and Small Hub Airports (does not include buffers). Trailing Aircraft
Lead
Aircraft
Heavy
Large
Small Heavy Large Small 4.00
3.00
3.00 5.00
3.00
3.00 6.00
5.00
3.00 NOTE: always consult with ATC specialist to verify the validity of
these minimum separation distances. Virginia Tech 27 Practical Values of ArrivalArrival
Separations (δij)
Table 2. Typical Intrail Separations (nautical miles) Near Runways at
Large Hub Airports (does not include buffers). Trailing Aircraft
Lead
Aircraft
Heavy
Large
Small Heavy Large Small 3.00
2.50
2.50 5.00
2.50
2.50 6.00
5.00
2.50 NOTE: always consult with ATC specialist to verify the validity of
these minimum separation distances. Virginia Tech 28 DepartureDeparture Aircraft Separations
Table 3. Typical Intrail Departure Separations Near Runways in
seconds. Trailing Aircraft LEAD
ACFT.
Heavy
Large
Small Heavy Large Small 120
90
60 120
60
60 120
60
60 NOTE: always consult with ATC specialist to verify the validity of
these time. Virginia Tech 29 Example Problem (1)
Determine the saturation capacity of an airport serving
two groups of aircraft: a) heavy (30% of the population)
and b) Small (70% of the population). Assume the
common approach length γ to be 7 miles.
The aircraft performance characteristics are given in the
following table.
Table 4. Aircraft Characteristics.
Aircraft Group Runway Occupancy
Time (seconds) Approach Speed
(knots) Heavy 60 150 Small 40 100 Virginia Tech 30 Example Problem (1)
Assume radar surveillance is available with 20 seconds
for the standard deviation of intrail delivery accuracy
error and a probability of violation of 5%.
The airport is a medium hub airport. The arrivalarrival
separation matrix is shown in Table 1. The departuredeparture separation matrix is shown in Table 3. Virginia Tech 31 Determine Aircraft Mix and Probabilities
The following is a probability matrix establishing the
chance that an aircraft of type (i) follows aircraft of type
(j). We assume random arrivals.
Table 4. Probability Matrix (Pij). Aircraft (i) follows aircraft (j).
Trailing Aircraft
Lead Aircraft Heavy Small Heavy =(0.3) x (0.3) = 0.09 = (0.3) x (0.7) = 0.21 Small = (0.7) x (0.3) = 0.21 = (0.7) x (0.7) = 0.49 NOTE: verify that ∑P ij = 1.0 i, j Virginia Tech 32 Compute Headways Between Successive
Arrivals
Closing case:
Lead = small, Following = heavy aircraft TS – H 3
δS – H
=  =  = 0.02 hours
VH
150 Usually is convenient to express headway in seconds. 3
δS – H
T S – H =  =  3600 = 72 seconds 150
VH
Virginia Tech 33 Closing case (apply this case when speeds are
equal):
Lead = small, Following = small aircraft TS – S 3
δS – S  3600 = 108 seconds
=  = 100
VS Lead = heavy, Following = heavy aircraft TH – H 4
δH – H  3600 = 96 seconds
=  = 150
VH Virginia Tech 34 Opening case:
Lead = heavy, Following = small aircraft TH – S 11
δH – S  –  seconds
=  + γ V S V H
VS 6
1
1
T H – S =  3600 + 7  –  3600 = 300 100 100 150
seconds Virginia Tech 35 Arrival Aircraft Headway Table
The following table summarizes the computed
headways for all cases when an aircraft of type (i)
follows aircraft of type (j). We assume random arrivals.
Table 5. ErrorFree headways (in seconds) when aircraft (i)
follows aircraft (j).
Trailing Aircraft
Lead Aircraft Heavy Small Heavy 96 300 Small 72 108 Virginia Tech 36 Compute Expected Value of Headway
The expected value of the headway is: E ( T ij ) = ∑P T
ij ij for all i,j pairs i, j E ( T ij ) = P H – H × T H – H + P S – H × T S – H + P H – S × T H – S
+ PS – S × TS – S
E ( T ij ) = 0.09 ( 96 ) + 0.21 ( 72 ) + 0.21 ( 300 )
+ 0.49 ( 108 ) Virginia Tech 37 E ( T ij ) = 0.09 ( 96 ) + 0.21 ( 72 ) + 0.21 ( 300 )
+ 0.49 ( 108 ) E ( T ij ) = 139.7 seconds
Now compute the buffers between successive arrivals
paying close attention to closing and opening equations. Virginia Tech 38 Compute ArrivalsOnly Buffers
Opening Case: BH – S 11 σ o q v – δ H – S  –  , 0
= min V S V H 1
1
B H – S = 1.65 ( 20 ) – 6  –  3600 100 150
B H – S = min ( – 39, 0 ) = 0 seconds Virginia Tech 39 Closing Case: B ij = σ o q v
B S – S = B H – H = B S – H = σ o q v = 1.65 ( 20 ) = 33
seconds Virginia Tech 40 Arrivals Only Analysis
The following table summarizes the computed
headways (including the buffer times) for all cases when
an aircraft of type (i) follows aircraft of type (j). We
assume random arrivals.
Table 6. Actual headways (in seconds) when aircraft (i) follows
aircraft (j).
Trailing Aircraft
Lead Aircraft Heavy Small Heavy 96+33 = 129 300 + 0 = 300 Small 72+33 = 105 108+33 = 141 Virginia Tech 41 Expected Value of Headways (Including
Buffer Times)
The expected value of the actual headways E ( T ij + B ij )
is 165.8 seconds. The arrivals only capacity is, C arrivals 1
=  vehicles per second
E ( T ij + B ij ) Using more standard units of capacity (aircraft per
hour), 3600
C arrivals =  arrivals per hour
E ( T ij + B ij )
Virginia Tech 42 ArrivalsOnly Capacity
For the single runway example the arrivalsonly
capacity is, 3600
C arrivals =  = 21.8 aircraft arrivals per hour
165.8
NOTE: this value is a little low for a busy airport. At
busy airports small aircraft are generally handled at a
different runway if possible to improve the capacity of a
runway operated by heavy aircraft. Virginia Tech 43 Analysis of Runway Gaps
Gaps can be studied for all four possible instances
studied so far. For example, if a heavy aircraft is
followed by a small one, there is a headway of 300
seconds between two successive arrivals. This leaves a
large gap that be exploited by air trafﬁc controllers to
handle a few departures on the same runway. δ  + E ( ROT i ) +
E ( T ij + B ij ) ≥ E V j
( n – 1 ) E ( ε ij ) + E ( τ ) Virginia Tech 44 Computation of Minimum Gaps E ( T ij + B ij ) ≥ 64.8 + 46 + ( n – 1 ) 78 + 10 seconds
E ( T ij + B ij ) ≥ 64.8 + 46 + 10 + 78 n – 78 seconds
E ( T ij + B ij ) ≥ 42.8 + 78 n seconds
For n = 1 (one departure between arrivals) we need, E ( T ij + B ij ) n = 1 ≥ 120.8 seconds
For n = 2 (two departures between arrivals) we need, E ( T ij + B ij ) n = 2 ≥ 198.8 seconds
Virginia Tech 45 Computation of Minimum Gaps
For n = 3 (three departures between arrivals) we need, E ( T ij + B ij ) n = 3 ≥ 276.8 seconds
For n = 4 (four departures between arrivals) we need, E ( T ij + B ij ) n = 4 ≥ 354.8 seconds
and so.
We need to compare the values stated in Table 6
( T ij + B ij ) against the gaps needed to schedule n
departures per arrival gap instance.
Virginia Tech 46 Gap Analysis
The following table summarizes the number of
departures possible when an aircraft of type (i) follows
aircraft of type (j). We assume random arrivals.
Table 7. Number of departures per arrival gap when aircraft (i)
follows aircraft (j).
Trailing Aircraft
Lead Aircraft Heavy Small Heavy 1 3 Small 0 1 Virginia Tech 47 Interpretation of Table 7
One departure (on the average) can be scheduled
between a heavy aircraft followed by another heavy
aircraft. Note that a heavy  heavy arrival sequence
implies an average of 129 seconds between successive
arrivals.
Since 121 seconds are needed to schedule a departure
(expected value for all types of operations), we conclude
that one departure can occur anytime two heavy aircraft
follow each other.
Other cells are computed in a similar fashion. Virginia Tech 48 Analysis of Arrival Gaps
The ﬁnal question that needs to be answered is: how
many times each gap happens during the period of
interest?
From our analysis of arrivals only, we determined that
on the average hour 21.8 arrivals could be processed at
the runway. Since two successive arrivals are needed to
form a gap, we can infer that an average of 20.8 gaps are
present in one hour.
The probability of each one of the four arrival instances
is known and has been calculated in Table 4. Thus using Virginia Tech 49 these two pieces of information we estimate the
number of times gaps will occur during one hour.
Consider the instance of a heavy aircraft leading another
heavy aircraft. Nine percent of the time this instance
occurs at the airport. Thus for 20.8 gaps per hour this
represents an equivalent number of hourly departures
per arrival instance ( E D H – H ), ED H – H = TG ( P H – H ) ( DG H – H )
where: TG is the total number of gaps per hour, P H – H is
the probability that a heavy aircraft follows another
heavy, and DG H – H is the number of departures per gap
for each instance (numbers in Table 7).
Virginia Tech 50 ED H – H = 20.8 ( 0.09 ) ( 1 ) = 1.87
equivalent departures per hour
Similarly, ED H – S = 20.8 ( 0.21 ) ( 3 ) = 13.10
ED S – H = 20.8 ( 0.21 ) ( 0 ) = 0
ED S – S = 20.8 ( 0.49 ) ( 1 ) = 10.19
equivalent departures per hour per instance Virginia Tech 51 Departures with Arrival Priority
Table 8 summarizes the number of departures per hour
per instance.
Table 8. Equivalent departures per hour per arrival instance
when aircraft (i) follows aircraft (j).
Trailing Aircraft
Lead Aircraft Heavy Small Heavy 1.87 13.10 Small 0.00 10.19 Total departures per hour = 25.2 departures per hour Virginia Tech 52 Recapitulation of Results so Far C arrivals 3600
=  = 21.8 arrivals per hour
165.8 C departures = 25.2 departures per hour
These results indicate that a single runway can process
21.8 arrivals per hour and during the same period
process 25.2 departures per hour using the gaps formed
by the arrivals.
Total operations = 47 aircraft per hour Virginia Tech 53 Final Note
If only departures are processed at this runway (no
arrivals), the departures only capacity is the reciprocal
of the departure headway (78 seconds), 3600
C dep – NA =  = 46.2 departures per hour
78
Airport engineers use a capacity diagram illustrated in
the ﬁgure to display all three hourly capacity results in a
single diagram. These diagrams represent a Pareto
frontier of arrivals and departures. The airport can be
operated inside the Pareto boundary. Virginia Tech 54 ArrivalDeparture Capacity Diagram
B (25.2,21.8) Arrivals per Hour A (0,21.8) 20 10
C (46.2,0) 0
10 20 30 40 Departures per Hour Virginia Tech 55 Review of Runway Capacity Excel Program
• The Excel template provided in class attempts to
illustrate how the timespace diagram technique can be
“programmed” in a standard spreadsheet • You can extend the analysis provided in the basic
template to more complex airport conﬁgurations • The program, as it stands now, can only estimate the
saturation capacity of a single runway. The program
provides a simple graphical representation of the arrival
departure saturation diagram (sometimes called
capacity Pareto frontier in the literature) • The following pages illustrate the use of the program
using the values of the previous runway example.
Virginia Tech 56 Excel Template Flowchart
1 Enter runway operation technical parameters
 Arrival minimum separation matrix (δij)
 Departuredeparture separation matrix (εij)
 Arrivaldeparture minimum separation (δ)
 Common approach length (γ)
 Runway occupancy times (ROTi)
 Runway departure times (td)
 Aircraft mix (Pi)
 Standard deviation of intrail delivery error (so)
 Probability of separation violations (Pv) 2 Compute Expected value of ROT times (E(ROT))
 E(ROTi) 3 Estimate the “ErrorFree” separation matrix
 Tij values using opening and closing cases Compute expected value
of the errorfree matrix
E(Tij) 4 Estimate the “Buffer” separation matrix
 Bij values using opening and closing cases Compute expected value
of the buffer matrix
E(Bij) Virginia Tech 57 Excel Template Flowchart (continuation)
5 Compute augmented separation matrix
 Aij = Tij + Bij (errorfree + buffer) 6 Compute the probability matrix (i follows j)
 Pij 7 Compute expected value of Aij matrix
 E(Aij) = E(Tij + Bij) Compute arrivalsonly
runway saturation capacity
Carr 8 Compute expected value of departuredeparture matrix  E(εij) Compute departuresonly
runway saturation capacity
Cdep 9 Compute gaps for n departures (n=1,2,...,5)
 E(Gn) 10 Compute feasible departures per arrival gap
(implemented as an Excel Macro) Virginia Tech 58 Excel Template Flowchart (continuation)
11 Compute number of departures per gap
if arrivals have priority 12 Departure capacity
with arrival priority
Cdeparrpriority Draw the arrivaldeparture diagram using
points:
Carr
Cdep
Cdeparrpriority End Virginia Tech 59 Computer Program Screen 1 1 2 1 Virginia Tech 60 Computer Program (Screen 2) 3 6 4 Virginia Tech 61 Computer Program (Screen 3)
5 1 8 9 Virginia Tech 62 Computer Program (Screen 4)
10 11 Virginia Tech 63 Computer Program (Screen 5) 12 Virginia Tech 64 Estimating Runway Saturation Capacity for
Complex Airport Conﬁgurations
• The methodology explained in the previous handout
addresses a simple TimeSpace diagram technique to
estimate the runway saturation capacity • The timespace approach can also be used to estimate
the saturation capacity of more complex runway
conﬁgurations where interactions occur between
runways • Example problems taken from the FAA Airport
Capacity benchmark document will be used to illustrate
the points made Virginia Tech 65 Methodology
• Understand the runway use according to ATC
operations • Select a primary runway as the basis for your analysis • Estimate the saturation capacity characteristics of the
primary runway using the known timespace method • Examine gaps in the runway operations at the primary
runway. These gaps might exist naturally (i.e., large
arrivalarrival separations) or might be forced by ATC
controllers by imposing large intrail separations
allowing operations at other runways Virginia Tech 66 • If runway operations are independent you can
estimate arrival and departure saturation
capacities for each runway independently • If the operations on runways are dependent estimate the
runway occupancy times (both for arrivals and
departures) very carefully and establish a logical order f
operations on the runways. Virginia Tech 67 Example 2  CharlotteDouglas Intl. Airport
Departures Operational Conditions 18R 1) Runways 18R/36L and 18L/36R
are used in mixed operations mode
2) Runway 5/23 is inactive
3) Parallel runway separation > 4,3000 ft.
4) ASR9 airport surveillance radar
(scan time 4.8 seconds)
5) Aircraft mix
a) Heavy  20%
b) Large  30%
c) Small  50%
6) Approach speeds
a) Heavy  150 knots
b) Large  140 knots
c) Small  110 knots
7) Runway occupancy times
N
a) Heavy  57 s.
b) Large  52 s.
c) Small  49 s.
8) Common approach length  7 nm
9) Intrail delivery error standard deviation 18 s.
10) Large hub separation criteria (2.5/4/5/6 nm)
11) IMC weather conditions 18L
Control
Tower
23 Terminal 5 1,00 0 500 Virginia Tech 5 , 0 0 ft
36R
3,000 36L Arrivals 68 Some Intermediate Results DepartureDeparture
Separation Matrix Virginia Tech 69 Results of CLT Analysis Arrivals (per hour) Single runway analysis  mixed operations 30
25
20
15
10
5
0
0 10 20 30 40 50 Departures (per hour)) Virginia Tech 70 Results of CLT Analysis Arrivals per Hour Twoparallel runway analysis  mixed operations
50% arrivals
50% departures 54 0
95 23
Departures per Hour Virginia Tech 71 Capacity Benchmark Results
The FAA capacity benchmarks offer an assessment of
the estimated capacity by the FAA Reduced capacity = IMC conditions Virginia Tech 72 FAA Benchmark Results vs. Our Analysis Variations occur
because the
assumptions made
in our example
are not necessarily
the same as those
made by FAA Virginia Tech 73 Example 3  CharlotteDouglas Intl. Airport
Departures Operational Conditions 18R 1) Runway 18R/36L for departures
Runway 18L/36R for arrivals
2) Runway 5/23 is inactive
3) Parallel runway separation > 4,3000 ft.
4) ASR9 airport surveillance radar
(scan time 4.8 seconds)
5) Aircraft mix
a) Heavy  20%
b) Large  30%
c) Small  50%
6) Approach speeds
a) Heavy  150 knots
b) Large  140 knots
c) Small  110 knots
7) Runway occupancy times
N
a) Heavy  57 s.
b) Large  52 s.
c) Small  49 s.
8) Common approach length  7 nm
9) Intrail delivery error standard deviation 18 s.
10) Large hub separation criteria (2.5/4/5/6 nm)
11) IMC weather conditions 18L
Control
Tower
23 Terminal 5 1,00 0 500 Virginia Tech 5 , 0 0 ft
36R
3,000 36L Arrivals 74 Results of CLT Analysis
Twoparallel runway analysis  segregated operations Arrivals per Hour Original Runway Configuration 54
New Runway Configuration 27 0
23 95
47
Departures per Hour Virginia Tech 75 Example 4  CharlotteDouglas Intl. Airport
Departures Operational Conditions 18R 1) Runways 18R/36L and 18L/36R
are used in mixed operations mode
2) Runway 5/23 is inactive
3) Parallel runway separation > 4,3000 ft.
4) ASR9 airport surveillance radar
(scan time 4.8 seconds)
5) Aircraft mix
a) Heavy  20%
b) Large  30%
c) Small  50%
6) Approach speeds
a) Heavy  150 knots
b) Large  140 knots
c) Small  110 knots
7) Runway occupancy times
N
a) Heavy  57 s.
b) Large  52 s.
c) Small  49 s.
8) Common approach length  7 nm
9) Intrail delivery error standard deviation 18 s.
10) Large hub separation criteria (2/3/4/5 nm)
11)VMC weather conditions 18L
Control
Tower
23 Terminal 5 1,00 0 500 Virginia Tech 5 , 0 0 ft
36R
3,000 36L Arrivals 76 Results for CLT VMC Scenario Arrivals (per hour) Single runway analysis  mixed operations 40
30
20
10
0
0 20 40 60 80 Departures (per hour) ) Virginia Tech 77 Results of CLT VMC Analysis Arrivals per Hour Twoparallel runway analysis  mixed operations
63
54
VMC
IMC 0
23 95 26 118 Departures per Hour
Virginia Tech 78 Airport Capacity Model (ACM)
• Model developed by FAA to expedite computations of
runway saturation capacity • Later modiﬁed by MITRE to be more user friendly • Inputs and output of the model are similar to those
included in the spreadsheet shown in class • Provides 79 data points to plot the arrivalcapacity
saturation capacity envelope (Pareto frontier) Virginia Tech 79 Sample Enhanced ACM Results Virginia Tech 80 Example 5  Nontowered Aiport Capacity
Using Advanced HighVolume Operations
Technologies (SATS program)
• Existing airports without a control tower have very
small runway saturation capacities (45 arrivals per
hour) • These airports require large headways (1012 minutes)
between aircraft because ATC cannot see the aircraft in
radar (ATC applies procedural separations) • NASA is developing technologies to help pilots space
themselves at these nontowered airports (using an
airport sequence manager and Automated Depedence
Surveyance mode B  ADSB)
Virginia Tech 81 HVO Scenario (Uncontrolled Airport) Critical
Area of Study Virginia Tech 82 Example 5  HVO Airport
Operational Conditions Departures 1) Single runway
used in mixed operations mode
2) HVO technology with Airport Manager and ADSB technology
3) With and without parallel runway
4) No radar
5) Aircraft mix
a) TERP A  60%
b) TERP B  40%
c) No TERP C
6) Approach speeds
a) TERP A  90 knots
b) TERP B  110 knots
c) No TERP C
7) Runway occupancy times
Variable with availability of runway taxiways and parallel taxiway (use REDIM model)
8) Common approach length  10 nm
9) Intrail delivery error standard deviation 30 s.
10) Arrivalarrival separation criteria (5 nm)
11) IMC weather conditions Key change from procedural
seperation Virginia Tech 83 Computer Program Screen 1 1 2 1 Virginia Tech 84 Computer Program (Screen 2) 3 6 4 Virginia Tech 85 Computer Program (Screen 3)
5 1
8 9 Virginia Tech 86 Computer Program (Screen 4) 10 11 Virginia Tech 87 HVO Single Runway Airport Capacity (no
parallel taxiway) Virginia Tech 88 HVO Single Runway Airport Capacity (with
parallel taxiway) Virginia Tech 89 Validation of Results (using FAA Aairport
Capacity Model) Virginia Tech 90 Summary of Results
• The saturation capacity of an airport with HVO (ADSB)
technology depends on the safety buffers allowed and the
delivery accuracy of pilots/AMM system • The variation in technical parameters such as γ and δ
affects the results of saturation capacity • The estimation of departures with 100% arrival priority in
our analysis seems consistent with analyses done by
TSAA in 2003 (Milsaps, 2003) • The results compare well with those obtained using the
FAA Airport Capacity Model • The availability of a parallel taxiway has a large inﬂuence
in the mixed mode saturation capacities
Virginia Tech 91 Recapitulation
• The saturation capacity of an airport depends on the
runway conﬁguration used • The saturation capacity during VMC conditions is higher
than during IMC conditions (due to shorter separation
minima) • The variation in technical parameters such as γ and δ
affects the results of saturation capacity • The estimation of departures with 100% arrival priority in
our analysis seems very conservative • The timespace analysis does not provide with delay
results (use deterministic queueing theory or FAA AC
150/5060 to estimate delay)
Virginia Tech 92 ...
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 Fall '11
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