**Appendix 1**

![](media/image1.png){width="2.9555555555555557in" height="8.98125in"}

![](media/image2.png){width="4.590277777777778in"
height="7.998611111111111in"}**Appendix 2**

**Appendix 3**

![](media/image3.png){width="5.995138888888889in"
height="4.1194444444444445in"}

# Appendix 4

# 

# ![](media/image4.wmf){width="5.509722222222222in" height="5.893055555555556in"} 

# Appendix A FAA Radar Surveillance Costs Under ADS-B Out Scenario

# Table A.1 Legacy Sustain Costs Under ADS-B Out Scenario 2007 \$

![](media/image5.wmf){width="7.040277777777778in"
height="5.452083333333333in"}

Table A.2 Legacy Backup Cost for ADS-B Out Surface and Airborne Backup
costs (2007 K\$)

![](media/image6.wmf){width="6.45in" height="5.766666666666667in"}

**Table A.3 Radar Decommissioning Costs (2007 K\$)**

![](media/image7.wmf){width="5.422916666666667in"
height="6.404861111111111in"}

**Table A.4 Other Legacy Costs for ADS-B Out Scenario (2007 K\$)**

![](media/image8.wmf){width="5.184722222222222in"
height="6.404861111111111in"}

## Appendix B -- Description of How the ADS-B Out Benefits Were Estimated

**[B.1 Increased Capacity over the Gulf of Mexico - Benefits of Reduced
Delay]{.underline}**

Current non-radar separation in the Gulf of Mexico constrains the number
of flights that can operate in each of the Air Route Traffic Control
Center Gulf (ARTCC) sectors (ZHU79 and ZHU72). According to ZHU
controllers many flights experience ground or air delay because of this
constrained airspace. Currently delay only occurs during highly seasonal
peak traffic days.[^1] As demand increases we expect days with delays to
increase.

The first step in the analysis was to estimate the increase in capacity
that might reasonably be achieved by radar-like separation. Discussions
with representatives from Houston Center revealed that the monitor alert
parameter (MAP) value [^2] is 12 for Sector ZHU 72and 18 for sector ZHU
79, the two sectors comprising the airspace over the Gulf of Mexico. The
average time an aircraft spends in the region (both sectors) is 32
minutes. Combining these gives an hourly capacity of 56 aircraft.
Representatives from Houston center estimated that the combined MAP
value would rise to 40 after surveillance, which would give an hourly
capacity of 75 aircraft. This represents the maximum number of aircraft
per hour that Houston Center could handle with a surveillance system and
a five-mile separation given the current two sectors. While complete
surveillance coverage across the Gulf of Mexico has the potential with
the addition of a third sector, to provide much greater increases in air
traffic capacity, this initial estimate takes into account current en
route airspace constraints in Mexico as well as airport capacity
limitations at some of Mexico's most popular destinations. Our
operational experience indicates that Mexico can handle no more than 75
to 80 aircraft an hour. Therefore, in the following analysis we use 75
aircraft as the hourly capacity that could be achieved with today's
airspace design, assuming a seamless surveillance and a reduced
separation standard.

We started with actual operations over the Gulf of Mexico for each day
in 2004. [^3] This data was obtained from the FAA Air Traffic Data
Activity System (ATADS) Houston Oceanic operations counts for each day
in 2004[^4].

For example, on December 31, 2004, there were a total of 294 oceanic
operations tracked by the Houston Center (ZHU). This represents the
number of operations over the Gulf of Mexico on that day as displayed in
Table B.1 below.

Table B.1 Number of Flights over the Gulf of Mexico on 12/31/2004

**CENTERS** : Oceanic

From 12/31/2004 To 12/31/2004 :  ZHU

  -------------- -------------- ------------ -------- -------- --------- ------------- -------- -------- --------- -- --
                                DEPARTURES                               OVERFLIGHTS                                  

  **Facility**   **Date**       **AC**       **AT**   **GA**   **MIL**   **AC**        **AT**   **GA**   **MIL**      

                                                                                                                      

  ZHU            12/31/2004     0            0        0        0         226           5        22       41           

  **Total**      ** **          **0**        **0**    **0**    **0**     **226**       **5**    **22**   **41**       
  -------------- -------------- ------------ -------- -------- --------- ------------- -------- -------- --------- -- --

In this way, we collected the number of operations over the Gulf of
Mexico for every day in 2004. The second column of Table B.2
(highlighted in yellow) gives a snapshot of the number of actual
operations organized from high to low for the 31 days in 2004 with the
highest number of operations. The highest operation day in 2004 was 451.
Then we reduced the number of operations by 10% to account for
operations that only made small incursions into oceanic sectors. The
third column in Table B.2 is a snapshot of the starting point from which
we forecast operations to represent future years.

Table B.2 Estimated Number of Flights Per Day

![](media/image9.wmf){width="6.229861111111111in"
height="4.746527777777778in"}

To forecast operations for years out to 2035, we derived total
operations for the U.S. to and from Latin America using several
forecasts from the 2006 FAA forecast. [^5] We multiplied the average
seats per aircraft (international) [^6] by the scheduled passenger
capacity and load factors [^7] to obtain passengers per aircraft. Then
we divided the total passenger traffic to and from the U.S. from Latin
America [^8] by the average passengers per aircraft[^9] to derive the
estimated total operations and computed the percent change in operations
from year to year, as presented in Table B.3.

Table B.3 Forecast of High Altitude Operations in Gulf of Mexico

![](media/image10.wmf){width="5.996527777777778in" height="5.75in"}

Using the estimated growth in operations up to year 2017 as presented in
the last column of Table B.3 and derived from the FAA forecast [^10] and
an assumed 4% growth rate after 2017, we increased each daily number of
flights starting in the third column of Table B.2 out to the year 2035.
The percent change in operations (shown in the last column of Table B.3)
is indicated on the second row of Table B.2 above (which shows a
snapshot of flights growing per year). The specific tables from which
the numbers in Table B.3 originate in the FAA forecast are indicated in
the top row of Table B.3.

Next we took our forecast (Table B.2 -- Estimated Number of Flights per
Day) and counted the number of days for each year that would experience
operations falling into various daily demand groupings. Table B.4
presents a snapshot of these groupings for selected years. For instance,
we estimate that in 2015 there will be one day during which high
altitude operations will be less than 200, 10 days with operations from
200 to 249, 45 days with 250 to 299 operations and so on.

![](media/image11.wmf){width="4.008333333333334in"
height="4.906944444444444in"}Table B.4 Unconstrained Daily Demand [^11]

In order to estimate the delay for each level of operations under the
base case scenario (no surveillance over the Gulf) and under the ADS-B
Out scenario (ADS-B surveillance over the Gulf) a queuing model
developed by MCR Federal was used.

To run the queuing model with hourly capacities, we needed an hourly
demand profile. ZHU ARTCC provided the hourly demand profile based on
the four busiest days in 2003. This profile was applied to the midpoint
of each of the daily operations groupings to derive an hourly demand
profile for each level of operations. Table B.5 displays the hourly
demand profile for a day for 675 operations (representing the midpoint
of operations from 650 to 699). For example, 12.8% of the 675 operations
or 86 operations occurs at 10 a.m.

Table B.5

![](media/image12.wmf){width="2.854861111111111in"
height="3.3465277777777778in"}Hourly Demand Profile on Peak Days

This hourly demand distribution for the midpoint of each daily demand
grouping was input into the queuing model. The queuing model was then
run for the base case capacity (56 aircraft per hour) and the
surveillance capacity (75 aircraft per hour). The queuing model produced
a probability distribution of delay minutes for each grouping.

We assume that delays beyond 20 minutes per flight are not acceptable
[^12] and we will not accommodate demand for flights if the delay is
expected to surpass 20 minutes. According to the queuing model 20-minute
delays are encountered at daily flight levels of 675 under the baseline
and 925 under the surveillance case. We therefore constrain demand at
675 for the baseline and at 925 for the surveillance case. Table B.6
shows demand constrained at 675 flights for the baseline and Table B.7
shows demand constrained at 925 flights for the surveillance case.

Table B.6 Daily Demand Constrained at 675 Flights [^13] -- Baseline

![](media/image13.wmf){width="4.008333333333334in"
height="4.888194444444444in"}

**Table B.7 Daily Demand Constrained at 925 Flights** [^14]**--
Surveillance Case**

![](media/image14.wmf){width="4.008333333333334in"
height="4.886805555555555in"}

Table B.8 shows the results of the queuing model when run for a daily
demand level of 675. The results show that at 675 daily flights there
will be 13500 minutes of total delay for the base case capacity and 512
minutes total delay for the surveillance capacity at the 50^th^
percentile. This works out to 20 minutes per flight under the base case
and .8 minutes per flight under the surveillance scenario. The daily
savings in hours between the base case and surveillance case is
estimated to be 216.5 at a daily flight level of 675 ((20-.8) x 675/60).

Table B.8 Delay for Baseline (56/hour) and Surveillance (75/hour)

**Scenarios at a Daily Flight Level of 675**

![](media/image15.wmf){width="5.271527777777778in"
height="2.8465277777777778in"}

At daily demand levels beyond 675, we calculate the difference in delay
between the base case capacity and surveillance capacity for daily
flight levels up to 675. The reason we only calculate the difference in
delay for the first 675 flights is because we assume that under the base
case the maximum daily number of flights over the Gulf will be 675.

For example see Table B.9 below. For the daily demand levels of 725 the
delay savings are 203.5 calculated as follows: ((20-1.9) x 675)/60 =
203.5. The rule (surveillance case) would confer the benefit of 203.5
hours less delay compared to the base case for those days with demand
levels of 725. In addition, without the rule, for each day with a demand
level of 725, 50 flights will not be accommodated, so there is a benefit
of additional flights accommodated which would be enabled by the rule.
The number of additional flights accommodated by the rule is explained
below.

Table B.9 Results of Queuing Model for Base Case and Surveillance Case
at 725

Daily Flights

![](media/image16.wmf){width="5.311805555555556in"
height="2.8465277777777778in"}

The daily delay in minutes at each demand level for the baseline and
surveillance is presented in Table B.10. For instance, at a demand level
of 575 we estimated delays of 3.92 minutes per aircraft under the
baseline and .07 minutes with surveillance using the queuing model. We
then convert to total delay hours per year by multiplying the delay
times the midpoint of the daily operations (or demand) grouping times
the number of days that we have forecast for that grouping divided by
60. So for a demand level of 575, under the baseline, the delay is
calculated as follows: (3.92 x 575 x 62)/60 = 2,329.

For daily demand beyond the 675 (which is the level at which flights are
not accommodated under the baseline) the surveillance delay savings is
only counted for up to 675 operations. Beyond that, the benefit to the
surveillance case is counted as additional operations.

Total delay in hours under the baseline is 22,352 and under the
surveillance case it is 3,814. The surveillance case results in a
savings of 18,538 hours relative to the baseline in 2025 as illustrated
in the table below.

Table B.10 Delay Savings for Daily Flight Levels of Demand

![](media/image17.wmf){width="5.9944444444444445in"
height="5.038194444444445in"}

We assume that capacity would be phased in starting in year 2012 to
reach full compliance by 2020 as presented in Table B.11. [^15]
Therefore, the full amount of delay savings cannot be achieved until
2020. Table B.11 summarizes annual delay savings in hours and indicates
phasing in of equipment with the resulting hours that we estimate will
be saved under the surveillance case relative to the baseline. It then
computes an economic value of the delay hours saved. The factors used in
computing the economic value of delay savings benefits are in Table
B.13.

Table B.11 Annual Delay Savings in Hours, Capacity Phasing, Hours Saved
and Economic Value of Delay Savings (2007 M\$)

![](media/image18.wmf){width="7.020138888888889in"
height="4.8277777777777775in"}

Table B.12 Passenger Value of Time [^16], [^17] [^18]

![](media/image19.wmf){width="5.999305555555556in"
height="1.413888888888889in"}

**Table B.13 Factors Used to Compute Economic Value**

![](media/image20.wmf){width="7.440277777777778in"
height="1.5569444444444445in"}

The dollar value of one hour of delay savings was calculated using the
factors in Table B.13. The distribution of traffic among air carrier and
commuter, air taxi, GA and military was computed using the oceanic
overflight data from ATADS. This provided a weighting for the hourly
costs of delay.

Aircraft direct operating costs (ADOC) are estimated per hour on the
ground as \$1,957 for air carriers TAF, \$520 for air taxis and \$526
for general aviation.[^19] Average passenger value of time is derived by
multiplying passenger capacity by load factor [^20] to derive passengers
per aircraft and multiplying passengers per aircraft by the hourly value
of passenger time [^21]. These values are presented in Table B.13 Values
used to calculate hourly passenger value of time per aircraft are found
in Table B.12.

The value of \$4,165 per one hour of delay savings was derived by taking
a weighted average of the sum of ADOC ground operating costs and the
average passenger value of time for each type of operations, weighted by
the percent of traffic accounted for by each type. This value was then
multiplied by the hours saved (Table B.11) each year to derive an
economic value of delay savings. These delay savings were then
discounted at 3% and 7%. The analysis did not take into account any
downstream delay savings that might be achieved due to ADS-B. Downstream
delay occurs when an aircraft that is delayed causes further delays in
the system. Therefore, the benefits are underestimated.

We estimated the benefits of avoiding delays that would be encountered
by high altitude operations are \$1.8 billion or \$946 million
discounted at 3% and \$421 million discounted at 7%. These results are
presented in Table B.11.

**[Additional Flights Accommodated by ADS-B]{.underline}**

As mentioned above, our analysis assumes that flight delays in excess of
20 minutes will not be tolerated. Demand levels that could result in
flight delays above 20 minutes are not accommodated. The queuing
analysis indicated that without surveillance over the Gulf, delays of 20
minutes occur at a level of operations of 675 per day. If there is
surveillance, (as would be the case with ADS-B) delays would occur at
daily flight levels of 925. On days that we anticipate demand in excess
of 675, ADS-B technology would accommodate more flights. Starting in
2017 we forecast that there will be days on which daily flight demand
will exceed 675 as indicated in Table B.14. Without surveillance in the
Gulf we would not accommodate demand in excess of 675.

![](media/image21.wmf){width="7.625in"
height="4.059027777777778in"}Table B.14 Unconstrained Daily Demand

The difference in number of flights that would be accommodated given
ADS-B surveillance in the Gulf of Mexico are presented in Table B.15
below. For each demand level above 675, the difference in flights
between that level and the next (50 flights) is multiplied by the number
of days that we expect that level of demand according to our forecast.

![](media/image22.wmf){width="7.579861111111111in"
height="3.5791666666666666in"}**Table B.15 Additional Flights
Accommodated Because of Surveillance**

For instance, according to our forecast, in 2018 there would be 4 days,
during which demand would reach 725 operations per day and no days
during which it would exceed 725 operations. ADS-B technology would
allow an additional 50 flights above the level that would be tolerated
without surveillance for 4 days so the technology would allow for
accommodation of 200 more flights in 2018 than the base case.

Table B.16 summarizes the additional annual flights accommodated by
having ADS-B surveillance technology in the Gulf of Mexico. We estimate
that a total of 246,400 additional flights might be accommodated with
ADS-B beyond that which would be accommodated without surveillance in
the Gulf.

![](media/image23.wmf){width="1.8069444444444445in"
height="5.816666666666666in"}**[Table B.16 Additional Flights
Accommodated]{.underline}**

Optimal and More Direct Routing Benefits

The FAA examined the top twenty city pairs from the US to Mexico and
south whose direct paths crossed the non-radar region for April 2003 and
estimated savings in nautical miles (NM-I) that could result with
radar-like separation. Appendix C describes in more detail how these
savings were estimated. Table B.17 shows the savings in nmi for the
month of April 2003 for twenty city pairs over the Gulf that could have
been achieved with the creation of more efficient routes. [^22] There
were a total of 4,063 flights between the twenty city pairs in April
2003. Of these flights, 1105 were flights that flew through the
non-radar region that could have benefitted from optimal routing. The
table shows how much savings in nmi could be achieved through optimal
routing for flights between each city pair. For instance, direct routing
could save 18.3 nmi for flights operating between Miami (MIA) and Mexico
City (MMMX). A potential average savings of 15 nmi could be achieved by
flights that operate between the twenty city pairs.

We estimate the number of flights per year that fly through non-radar
regions and could potentially save 15 nmi per flight with more optimal
routing. In April of 2003 there were 1105 of these flights. April
represents about 9% of the yearly traffic load. We estimate that in 2003
there were about 12,266 flights through the non-radar regions that could
have derived benefit from optimal routing. We estimate that these
flights will grow at about 4% a year. Optimal routing could save each of
these flights 15 nmi each. Table B.20 below indicates the annual number
of flights and total savings each year that might be achieved from
optimal routing.

Another 756 flights avoided the non-radar region either because the
aircraft or crew may not be certified to fly that far from shore or
because of limited capacity along the non-radar routes because of
traffic. Some of these flights could be granted a more direct route with
the advent of surveillance. We estimate that flights that could benefit
from direct routing through the Gulf is equal to about 10.8% of the
number of flights through the Gulf. [^23]

Table B.17 shows how much savings in nmi could be achieved if these
flights could fly through the non-radar area. For instance, flying
through the non-radar region could save a flight between Miami (MIA) and
Mexico City (MMMX) 30.8 nmi. We estimate that direct routing could save
these flights on average 67.8 nmi per flight.

Table B.17 Optimal Route Savings Between Selected City Pairs Based on
April 2003 Flights

![](media/image24.wmf){width="7.125in" height="4.991666666666666in"}

Table B.18 Dollar Value of an Hour Saved with Optimal and More Direct
Routing

![](media/image25.wmf){width="5.995138888888889in"
height="2.6347222222222224in"}

**Table B.19 Daily Demand Constrained at 525**

![](media/image26.wmf){width="7.625in" height="3.6166666666666667in"}

**Table B.20 Value of Optimal and More Direct Routing Over the Gulf of
Mexico in (2007 \$M)**

![](media/image27.wmf){width="5.995833333333334in"
height="5.340277777777778in"}

We estimated total savings in nautical miles that could be achieved by
more direct routes through the Gulf by estimating the number of flights
that could fly these routes (10.8% of the number of flights over the
Gulf) and multiplying that by an average savings of 67.8 nmi per flight.
Because adding these additional flights into the Gulf would also add
demand to the High Altitude capacity model, we decided that the benefit
would only accrue on days where there is little to no delay and we chose
days with a demand level of less than 525. To estimate number of flights
per year we multiplied the number of days that we expected demand to be
at various levels times that level and times 10.8% and summed all the
levels. Table B.19 shows number of days each year that we would
encounter various daily demand levels. For instance, in 2015 we expect
there will be 45 days when there will be an average demand for 275
flights. We multiply 45 by 275 and by 10.8% and do this for all the
daily demand bins up to, but not including 525, and sum the results for
an estimated 12,957 flights that could benefit from more direct routes
over the Gulf. We then multiply the number of flights by the average
savings per flight (12,957 x 67.8) to derive a total potential savings
for 2015 of 879,800 nmi. These benefits are achieved as capacity is
phased in, beginning in 2012, based on industry equipage. We computed
total savings (nmi) (529,705 for 2015) by adding potential optimal
non-radar route savings (294,574 for 2015) to potential additional
flight savings (879,801 for 2015) and multiplying the sum by the percent
capacity phasing (45.11% for 2015)

.

To monetize these savings we converted total savings into hours saved by
dividing total savings (nmi) by an average airspeed of 390 (529,761
divided by 390 for 2015). We then converted hours saved to value of
hours saved by multiplying hours saved by the sum of the weighted
averages of aircraft direct operating costs airborne and the passenger
value of time as presented in Table B.18 (\$5,714). Refer to Table B.20
for potential savings in miles, savings converted to total savings in
miles, total savings in hours and total savings converted to dollars
(benefits) and discounted benefits.

The result was an estimated benefit of \$257 million dollars (\$154
million discounted by 3% and \$83 million discounted by 7%) as indicated
in Table B.20.

The table below summarizes the quantified benefits from reduced delay
with the increased capacity that ADS-B may help achieve during high
altitude operations over the Gulf of Mexico and optimal and more direct
routing over the Gulf of Mexico that might be achieved with ADS-B Out
services in the Gulf, calculated as described above.

![](media/image28.wmf){width="6.970138888888889in"
height="2.161111111111111in"}

# Table B.21 Unconstrained Daily Demand

![](media/image29.wmf){width="8.5in" height="3.4916666666666667in"}

# 

# 

# 

#  

# Table B.22 Daily Demand Constrained at 675 Flights

# ![](media/image30.wmf){width="9.375in" height="3.8354166666666667in"} 

![](media/image31.wmf){width="9.125in"
height="3.7333333333333334in"}**Table B.23 Daily Demand Constrained at
925 Flights**

![](media/image32.wmf){width="10.25in"
height="4.808333333333334in"}**Table B.24 Additional Flights**

[Appendix C Details on Optimal Routing of High Altitude Operations Over
the Gulf of Mexico]{.underline}

Optimal Routing Of High Altitude Operations Over the Gulf of Mexico

[Problem:]{.underline} Due to the lack of surveillance and limited
communication at high altitudes, non-radar separation procedures
restrict aircraft to the available airways.

[Capability/Direct Impact:]{.underline} By providing surveillance and
improved communication services, radar-like separation can be applied
which enables enhanced dynamic air traffic management. Enhanced dynamic
traffic management can, in effect, increase the number of available
airways by granting directs.

[Outcome/Benefit]{.underline}: Enhanced dynamic air traffic management
increases direct routing, which should lead to a reduction in flight
distances and times. Reduction in flight distances and times translates
to savings in aircraft direct operating costs and passenger value of
time.

[Baseline Information]{.underline}: Current flights between Gulf city
pairs either file one of the 10 available non-radar routes, or file a
path that avoids the non-radar region completely. Figure 1 displays the
non-radar region, the non-radar routes, and two important SUAs that
constrict traffic.

Flights crossing the non-radar region of the Gulf of Mexico are
constrained to routes with set entry and exit fixes. Because of spacing
restrictions and arrival/departure stream flows, controllers sometimes
treat routes as one-way corridors. The addition of surveillance across
the gulf would provide more optimal routing to flights that already file
within the non-radar region in two ways. First, it would allow new
entry/exit locations on the U.S. side of the region. The ability to
allow new entry/exit points on the U.S. side should allow controllers to
grant more directs from U.S. locations to the fixes on the Mexican
airspace border. Second, it would allow controllers to build additional
routes. Although the necessity of creating one-way routes for separating
arrival and departure streams will still exist, controllers can build
these routes much closer together than is possible with the current
non-radar separation standards. This addition of airways should allow
both inbound and outbound traffic to fly paths closer to the optimal
route.

Flights that avoid the non-radar region may do so because the aircraft
or crew may not be certified to fly that far away from shore, or the
capacity along the non-radar routes may be limited due to traffic or
weather \[flights that are constrained due to capacity or lack of
surveillance accounts for 10.8% of flights between city pairs\]. Added
surveillance should increase capacity along current non-radar routes and
allow addition of available routes, and assumed equipage of these
flights should allow them to take the more optimal routes through the
Gulf.

![](media/image33.png){width="4.763194444444444in"
height="4.157638888888889in"}

Figure 1. Airspace and routes in the Gulf of Mexico. The green region is
the non-radar airspace. The orange regions are two of the largest
SUA[^24]s. The blue lines are the current routes through the region.

We first examined the top twenty city pairs from the US to Mexico and
south whose direct paths crossed the non-radar region for April 2003. We
chose to examine savings in terms of excess distance between current
routes and more direct routes, where possible. The data source was
ETMS-recorded filed flight paths. We considered savings from the two
general categories mentioned in the previous paragraphs.

For flights that already fly through the non-radar region, we explored
if the current route was the most optimum of the available routes to the
destination. If not, we assumed the addition of surveillance would
increase capacity along the more optimum route and additional parallel
routes would be created to allow more direct paths between entry and
exit points. In some situations, we also examined if directs from US
locations to current fixes on the Mexican border would save distance.

We also investigated savings for flights that avoided the non-radar
region. An attempt was made to determine if these circuitous routes
around the region were weather-related. If a route between city pairs
was filed more than 20% of the time, we assumed that it was not
weather-related, but involved either capacity or equipage
considerations. We removed routes filed less than 20% of the time from
the analysis. Savings for this second category focused on allowing these
flights to take the more optimal non-radar region route.

Table 1 displays the excess distance data for the top twenty city pairs.
The savings are broken into columns for flights that took routes through
the non-radar regions and those who avoided the non-radar region.

Table 1. Optimal route savings between selected city pairs

  -------- -------- ------- --------- ----------- ----------- --------- ----------- ---------
  City 1   City2    Count   Count of  Count of    Savings per Count of  Savings per Total
                            flights   current     flight      flights   flight that savings
                            already   non-radar   current     that      avoided     (nmi)
                            on        flights     non-radar   avoided   non-radar   
                            optimal               routes      region    route (nmi) 
                            route or              (nmi)                             
                            weather                                                 
                            related                                                 

  MIA      MMMX     574     0         292         18.3        282       30.8        14,030

  IAH      MMUN     338     183       155         12.4                              1922

  DFW      MMUN     292     156       136         2.6                               354

  ATL      MMMX     271     152       119         5.8                               690

  MSP      MMUN     215     215                                                     0

  ATL      MMUN     219     122       97          30.6                              2968

  ORD      MMUN     212     169       10          30.6        33        82.3        3022

  JFK      MMMX     217     118       99          5.8                               574

  CLT      MMUN     198     6         8           30.6        184       82.3        15,388

  EWR      MMUN     185     73        61          30.6        51        82.3        6064

  IAH      MZBZ     183     183                                                     0

  DTW      MMUN     168     117       17          30.6        34        82.3        3318

  EWR      MMMX     158     91        67          5.8                               389

  DEN      MMUN     137     66                                71        133.3       9464

  IAH      MROC     126     126                                                     0

  STL      MMUN     130     130                                                     0

  IAH      MGGT     116     111                                                     0

  IAH      MSLP     111     111                                                     0

  PHL      MMUN     108     24        9           30.6        75        82.3        6448

  JFK      MMUN     105     44        35          30.6        26        82.3        3211

  TOTAL             4063                                                            67,842
  -------- -------- ------- --------- ----------- ----------- --------- ----------- ---------

Using only these twenty city pairs and the fact that April represents 9%
of the yearly traffic load, we estimate savings of approximately 750,000
nmi a year from more optimal routes.

MIA-MMMX

While most routes across the non-radar region are already direct, there
is one notable exception. The most travel in the non-radar region takes
place between Miami and Mexico City (574 flights in April 2003). The
northern route (292 flights April 2003) between Miami and Mexico City
has a turn in the gulf designed to avoid an SUA just west of the Florida
Keys. With better surveillance, a new route, or a direct could be
granted that would cut 18 nmi off the current route and still avoid the
SUA. This new route would also be 31 nmi less than the current southern
route (282 flights April 2003) that does not penetrate the non-radar
region. See Figure 2.

![](media/image34.png){width="5.413194444444445in"
height="4.370833333333334in"}

Figure 2: Routes between Miami and MMMX. The green region is the
non-radar region and the orange regions are two major SUAs. Solid blue
lines are common northern and southern routes. Dashed blue line is
proposed shorter route.

IAH-MMUN

The current route structure includes non-radar routes that are close to
the optimum path between major city pairs (e.g. IAH-CUN, IAH- MID).
However, many times one of these routes is used as a one-way corridor
causing flights in the opposite direction to use a non-optimum route.
Although the necessity of creating one-way routes for separating arrival
and departure streams may still exist, controllers can build these
routes much closer together than is possible with the current non-radar
separation standards. This addition of airways should allow both inbound
and outbound traffic to fly a route closer to the optimal route.

During April 2003 there were 338 flights between IAH and MMUN. Half the
flights (nearly all from IAH to MMUN) flew on the solid path flown in
Figure 3. If we assume the same departure path (which ends on the MUSYL
fix), we find that the dotted path (not a current route) is 12 nm
shorter than the flown route. Most of the flights from MMUN to IAH
already go on a very direct route. There were very few flights that did
not cross the non-radar region between these cities, and their path may
be weather related.

![](media/image35.png){width="5.166666666666667in"
height="4.499305555555556in"}

Figure3: Route from IAH to MMUN. Solid line is most common route April
2003; dashed line is shorter route by 12 miles. The green region is the
non-radar region.

Northeast-MMUN

Most of the flights during April 2003 from CLT, JFK, and EWR to MMUN and
back generally followed one of the three paths seen in Figure 4. Most
flights from ATL to MMUN and some from both DTW and ORD also took
similar routes to MMUN with some variation at the extreme North of
Figure 4. Between all of these city pairs there are more routes that
avoid the non-radar region (Florida route) than can be explained because
of weather.

If capacity were to increase because of surveillance, then most of these
flights could take a route close to the optimum middle route (dashed
path in Figure 4) saving 30.6 nmi over the western case and 80.2 nmi
over the Florida route.

![](media/image36.png){width="5.995833333333334in"
height="5.221527777777778in"}

Figure 4. Routes between Northeast and MMUN. All lines are common routes
(April 2003). Dashed line is most optimum route. The green region is the
non-radar region and the orange regions are two major SUAs.

East-MMMX

Flights from JFK, EWR, and ATL to MMMX during April 2003 generally took
one of the two routes shown in Figure 5. The southern route is more
direct by 5.8 nmi. If capacity were to increase, more of these flights
could take a route closer to the southern route. A few of the recorded
flights took routes that did not penetrate the non-radar region. There
were so few of these that we assume these deviations were weather
related.

![](media/image37.png){width="5.301388888888889in"
height="4.616666666666666in"}

Figure 5. Routes between East and MMMX. Solid lines are common routes.
The green region is the non-radar region and the orange regions are two
major SUAs.

DEN-MMUN

Flights from DEN to MMUN in April 2003 took one of two routes. Over half
filed a southern route that entered Mexico near McAllen, Texas and
hugged the coast before flying to MMUN (dotted path in Figure 6). The
more direct filed route through the non-radar region is over 400 nmi
shorter than the southern route.

Over half the return flights from MMUN to DEN have filed routes that
start with a NAVAID directly across from McAllen, Texas. We suspect that
these flights also followed a route similar to those on the trip from
DEN to MMUN and ETMS truncates the data when the flight is passed to the
US. However, to be conservative, we use the direct path from MMUN to
McAllen to estimate all the southern routes between DEN and MMUN. Using
this route we find that flights that use the non-radar region route save
133 nmi each.

![](media/image38.png){width="5.211805555555555in"
height="4.528472222222222in"}

Figure 6. Routes between DEN and MMUN. Lines are common routes. The
green region is the non-radar region.

Other flights:

Midwest-MMUN: Flights between STL and MSP in April 2003 generally took
one of two routes across the non-radar region. The flights picked up
both of these routes near New Orleans. The distance difference was
negligible. Most flights from ORD and half the flights from DTW also
take these paths. The other ORD and DTW flights take an eastern route
that coincides with the traffic from the Northeast to MMUN.

IAH-other cities: Nearly all the flights from IAH to Central or South
American cities in the top twenty follow a single path that already
exits at the most direct point available on the Mexican border (fix
MARTE).

DFW-MMUN: These flights are already generally direct. We could calculate
a slight savings of 2.6 nmi a flight by sending planes going from DFW to
MMUN direct from the end of the departure path from DFW (BILEE fix) to
the exit point on the Mexican border (KEHLI fix) instead of going first
to the entry point on the U.S. side of the current non-radar area (KLAMS
fix). The return flights (also on A766) were already optimum. This gives
a total savings in April 2003 of 354 nmi.

[^1]: ATO En Route Services, Surveillance and Broadcast Services
    Benefits Basis of Estimate, June 2006, FAA ATO, pg. 41.

[^2]: The MAP value is the maximum number of aircraft that can be
    managed by a sector simultaneously with the exception that the MAP
    value can be exceeded for brief intervals.

[^3]: 2004 was chosen because 2005 was not a representative year, as
    traffic had decreased in the last quarter of 2005 due to hurricanes
    and 2006 data was not complete at the time of the analysis.

[^4]: Available on the FAA Office of Policy and Plans (APO) website:
    http://www.apo.data.faa.gov/

[^5]: FAA Aerospace Forecasts, Fiscal Years 2006-2017, U.S. Department
    of Transportation, Federal Aviation Administration, Office of Policy
    and Plans.

[^6]: Table 9 FAA Aerospace Forecasts, Fiscal Years 2006-2017

[^7]: Table 13 FAA Aerospace Forecasts, Fiscal Years 2006-2017

[^8]: Table 8 FAA Aerospace Forecasts, Fiscal Years 2006-2017

[^9]: Table 9 FAA Aerospace Forecasts, Fiscal Years 2006--2017

[^10]: FAA Aerospace Forecasts Fiscal Years 2006-2017, U.S. Department
    of Transportation, Federal Aviation Administration Office of Policy
    & Plans.

[^11]: A complete table of unconstrained daily demand showing number of
    days for each year from 2007 to 2035 in each daily demand group can
    be found in Table B.14 below.

[^12]: "approximately 20 minutes represents the highest level of average
    delay realized in actual practice even at highly congested airports,
    FAA Airport Benefit Cost Analysis Guidance, page 16, FAA Airport
    Benefit Cost Analysis
    http://www.faa.gov/regulations_policies/policy_guidance/benefit_cost/media/faabca.pdf

[^13]: A complete table of daily demand constrained at 675 flights
    showing number of days for each year from 2007 to 2035 in each daily
    demand group up to and including 675 can be found in Table B.22
    below.

[^14]: ^?^ A complete table of daily demand constrained at 925 flights
    showing number of days for each year from 2007 to 2035 in each daily
    demand group up to and including 925 can be found in Table B.23
    below.

[^15]: The phasing depends on estimated Gulf equipage and allocation of
    ADS-B separation to more altitudes over time.

[^16]: Source for Passenger Capacity and Load Factor is "Supporting
    Documentation for the Economic Factors Used in Investment Analysis,"
    pg. 17, Federal Aviation Administration, December 7, 2006, Final.

[^17]: Source for Hourly Values of Time 2000 U.S. \$ Per Passenger is
    "Economic Values for FAA Investment and Regulatory Decisions, A
    Guide, Draft final Report, December 31, 2004, Prepared for FAA
    Office of Aviation Policy and Plans.

[^18]: GDP Chained Price Index, Budget of the United States Government,
    Fiscal Year 2007, Table 10.1---Gross Domestic Product and Deflators
    Used in the Historical Tables: 1940-2011.

[^19]: Supporting Documentation for the Economic Factors Used in
    Investment Analysis, pg. 12, Federal Aviation Administration,
    December 7, 2006, Final.

[^20]: Supporting Documentation for the Economic Factors Used in
    Investment Analysis, pg. 17, Federal Aviation Administration,
    December 7, 2006, Final.

[^21]: Source for Hourly Values of Time 2000 U.S. \$ Per Passenger is
    "Economic Values for FAA Investment and Regulatory Decisions, A
    Guide, Draft Final Report, December 31, 2004, Prepared for FAA
    Office of Aviation Policy and Plans.

[^22]: ATO En Route Services, Surveillance and Broadcast Services
    Benefits Basis of Estimate, June 2006, Federal Aviation
    Administration, Air Traffic Organization, pg. 53

[^23]: These flights that currently avoid the non-radar regions of the
    Gulf are not included in the count of flights that fly through the
    Gulf.

[^24]: Special Use Airspace.