December 2009 

NASA/SP–2009-6115 

Metrics for the NASA Airspace 

Systems Program 

Jeremy C. Smith and Kurt W. Neitzke 

Langley Research Center, Hampton, Virginia 
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December 2009 

NASA/SP–2009-6115 

Metrics for the NASA Airspace 

Systems Program 

Jeremy C. Smith and Kurt W. Neitzke 

Langley Research Center, Hampton, Virginia 
Available from: 

NASA Center for AeroSpace Information 

7115 Standard Drive 

Hanover, MD 21076-1320 

443-757-5802 
iii 

Abstract 

This document defines an initial set of metrics for use by the NASA Airspace Systems 

Program (ASP). 

ASP consists of the NextGen-Airspace Project and the NextGen-Airportal Project. The 

work in each project is organized along multiple, discipline-level Research Focus Areas 

(RFAs). Each RFA is developing future concept elements in support of the Next 

Generation Air Transportation System (NextGen), as defined by the Joint Planning and 

Development Office (JPDO). In addition, a single, system-level RFA is responsible for 

integrating concept elements across RFAs in both projects and for assessing system-wide 

benefits. 

The primary purpose of this document is to define a common set of metrics for measuring 

National Airspace System (NAS) performance before and after the introduction of ASPdeveloped concepts for NextGen as the system handles increasing traffic. The metrics are 

directly traceable to NextGen goals and objectives as defined by the JPDO and hence will 

be used to measure the progress of ASP research toward reaching those goals. 

The scope of this document is focused on defining a common set of metrics for 

measuring NAS capacity, efficiency, robustness, and safety at the system-level and at the 

RFA-level. Use of common metrics will focus ASP research toward achieving systemlevel performance goals and objectives and enable the discipline-level RFAs to evaluate 

the impact of their concepts at the system level. 
iv 

Acronyms 

ACES Airspace Concept Evaluation System 

ADS-B Automatic Dependent Surveillance-Broadcast 

AMI Airportal and Metroplex Integration 

ASDO Airspace Super Density Operations 

ASP NASA Airspace Systems Program 

CADOM Coordinated Arrival and Departure Operations Management 

CADRS Combined Arrival/ Departure Runway Scheduling 

CD&R Conflict Detection and Resolution 

CDA Continuous Descent Approach 

DAC Dynamic Airspace Configuration 

FMS Flight Management System 

HITL Human In The Loop 

IFR Instrument Flight Rules 

JPDO Joint Planning and Development Office 

LoS Loss of Separation 

NAS National Airspace System 

NextGen Next Generation Air Transportation System 

OPD Optimal Profile Descent 

RCM Runway Configuration Management 

RFA Research Focus Areas 

RNAV Dynamic area navigation 

RNP Required Navigation Performance 

SA Separation Assurance 

SESO Safe and Efficient Operations 

SLDAST System-Level Design, Analysis, and Simulation Tools 

SWG JPDO Safety Working Group 

TBSO Trajectory Based Surface Operations 

TCAS Traffic Alert and Collision Avoidance System 

TFM Traffic Flow Management 

VCSPRO Very Closely Spaced Parallel Runway Operations 

VFR Visual Flight Rules 
v 

Table of Contents 

1 INTRODUCTION............................................................................................................................... 1

1.1 PURPOSE OF THIS DOCUMENT ...................................................................................................... 1

1.2 USERS OF THIS DOCUMENT .......................................................................................................... 2

1.3 DOCUMENT SCOPE ....................................................................................................................... 2

1.4 DOCUMENT STRUCTURE AND CONTENT ....................................................................................... 2

2 JPDO NEXTGEN GOALS, OBJECTIVES, AND METRICS ....................................................... 3

3 DEFINING CRITERIA FOR “GOOD” METRICS ........................................................................ 6

4 DEVELOPING SYSTEM-LEVEL METRICS TO EVALUATE NASA ASP RESEARCH ....... 7

4.1 ACCEPTABLE NAS DELAY ........................................................................................................... 7

4.2 ESTIMATING NAS PERFORMANCE ............................................................................................... 7

4.3 CALCULATING METRIC VALUES AND PERFORMING STATISTICAL ANALYSIS .............................. 8

4.4 ESTIMATING NAS CAPACITY ....................................................................................................... 8

4.5 ESTIMATING NAS EFFICIENCY..................................................................................................... 9

4.6 ESTIMATING NAS ROBUSTNESS ................................................................................................. 11

4.7 ESTIMATING NAS SAFETY ......................................................................................................... 13

4.7.1 Justifying the Selection of System-Level Safety Metrics ....................................................... 14

5 EVALUATING SYSTEM-LEVEL METRICS .............................................................................. 16

5.1 DISCUSSION ............................................................................................................................... 19

6 SYSTEM-LEVEL RFA METRICS................................................................................................. 20

6.1 SEPARATION ASSURANCE (SA) .................................................................................................. 21

6.1.1 Overview ............................................................................................................................... 21

6.1.2 Airborne SA .......................................................................................................................... 21

6.1.3 Ground-based SA .................................................................................................................. 21

6.1.4 Key Functions, Features, Characteristics and Capabilities ................................................. 22

6.1.5 Expected Performance Benefits ............................................................................................ 22

6.1.6 Flight Phase .......................................................................................................................... 22

6.1.7 Contribution to JPDO Top-Level Goals and Objectives ...................................................... 22

6.1.8 Impacts to ASP System-Level Metrics .................................................................................. 22

6.2 TRAFFIC FLOW MANAGEMENT (TFM) ....................................................................................... 24

6.2.1 Overview ............................................................................................................................... 24

6.2.2 Key Functions, Features, Characteristics and Capabilities ................................................. 24

6.2.3 Expected Performance Benefits ............................................................................................ 24

6.2.4 Flight Phase .......................................................................................................................... 24

6.2.5 Contribution to JPDO NextGen Top-Level Goals and Objectives ....................................... 24

6.2.6 Impacts to ASP System-Level Metrics .................................................................................. 25

6.3 DYNAMIC AIRSPACE CONFIGURATION (DAC) ........................................................................... 26

6.3.1 Overview ............................................................................................................................... 26

6.3.2 Key Functions, Features, Characteristics and Capabilities ................................................. 27

6.3.3 Expected Performance Benefits ............................................................................................ 27

6.3.4 Flight Phase .......................................................................................................................... 27

6.3.5 Contribution to JPDO Top-Level Goals and Objectives ...................................................... 27

6.3.6 Impacts to System-level Metrics ........................................................................................... 27

6.4 AIRSPACE SUPER DENSITY OPERATIONS (ASDO) ..................................................................... 28

6.4.1 Overview ............................................................................................................................... 28

6.4.2 Key Functions, Features, Characteristics and Capabilities ................................................. 29

6.4.3 Expected Performance Benefits ............................................................................................ 29

6.4.4 Flight Phase .......................................................................................................................... 29

6.4.5 Contribution to JPDO Top-Level Goals and Objectives ...................................................... 30

6.4.6 Impacts to ASP System-Level Metrics .................................................................................. 30
vi 

6.5 COORDINATED ARRIVAL AND DEPARTURE OPERATIONS MANAGEMENT (CADOM) ................ 31

6.5.1 Overview ............................................................................................................................... 31

6.5.2 Key Functions, Features, Characteristics and Capabilities ................................................. 32

6.5.3 Expected Performance Benefits ............................................................................................ 32

6.5.4 Flight Phase .......................................................................................................................... 32

6.5.5 Contribution to JPDO Top Level Goals and Objectives ....................................................... 32

6.5.6 Impacts to ASP System-Level Metrics .................................................................................. 32

6.6 SAFE AND EFFICIENT SURFACE OPERATIONS (SESO) ................................................................ 33

6.6.1 Overview ............................................................................................................................... 33

6.6.2 Key Functions, Features, Characteristics and Capabilities ................................................. 34

6.6.3 Expected Performance Benefits ............................................................................................ 34

6.6.4 Flight Phase .......................................................................................................................... 34

6.6.5 Contribution to JPDO Top Level Goals and Objectives ....................................................... 34

6.6.6 Impacts to ASP System-Level Metrics .................................................................................. 35

6.7 AIRPORTAL AND METROPLEX INTEGRATION (AMI) .................................................................. 37

6.7.1 Overview ............................................................................................................................... 37

6.7.2 Key Functions, Features, Characteristics and Capabilities ................................................. 37

6.7.3 Expected Performance Benefits ............................................................................................ 37

6.7.4 Flight Phase .......................................................................................................................... 37

6.7.5 Contribution to JPDO Top Level Goals and Objectives ....................................................... 37

6.7.6 Impacts to ASP System-Level Metrics .................................................................................. 38

7 SUMMARY AND CONCLUSIONS ............................................................................................... 39

ANNEX A: EXAMPLE VALUES FROM SIMULATION RESULTS ................................................. 40

REFERENCES ........................................................................................................................................... 45

List of Tables 

Table 1—JPDO NextGen goals and objectives within the scope of NASA ASP research 4 

Table 2—JPDO NextGen top-level metrics relevant to NASA ASP research ................... 5 

Table 3—Criteria for defining metrics used to evaluate NASA ASP research .................. 6 

Table 4—System-level capacity metrics ............................................................................ 9 

Table 5––System-level efficiency metrics ........................................................................ 10 

Table 6—System-level robustness metrics ....................................................................... 12 

Table 7—System-level safety metrics .............................................................................. 15 

Table 8—Evaluation of NASA ASP system-level metrics versus criteria ....................... 17 

Table 9—SA system-level metrics ................................................................................... 23 

Table 10—TFM system-level metrics .............................................................................. 26 

Table 11—DAC system-level metrics .............................................................................. 28 

Table 12— ASDO system-level metrics ........................................................................... 31 

Table 13—CADOM system-level metrics ........................................................................ 33 

Table 14—SESO system-level metrics ............................................................................. 35 

Table 15-–AMI system-level metrics ............................................................................... 38 

List of Figures 

Figure 1—26th Sept. 2006 Baseline Delays for All Flights ............................................. 42 

Figure 2—26th Sept. 2006 Baseline Delays for Commercial Passenger Flights ............. 42 

Figure 3—2008 TSAM Scenario Delays for All Flights .................................................. 43 

Figure 4—2008 TSAM Scenario Delays for Commercial Passenger Flights .................. 43 

Figure 5—2008 TSAM Scenario 99th-percentile of Delay for Commercial Passenger 

Flights ............................................................................................................................... 44 
1 

1 Introduction 

This document defines an initial set of metrics for common use by the NASA Airspace 

Systems Program (ASP). 

ASP research is directly addressing the fundamental research needs of the Next 

Generation Air Transportation System (NextGen) in partnership with the member 

agencies of the Joint Planning and Development Office (JPDO). [1]

ASP supports this effort through the NextGen-Airspace Project and the NextGenAirportal Project. The work in each project is organized along multiple, discipline-level 

Research Focus Areas (RFAs) and a single, system-level RFA. Each discipline-level 

RFA is responsible for developing future concept elements in support of NextGen, as 

defined by the JPDO. The system-level RFA (called System-Level Design, Analysis, and 

Simulation Tools, or SLDAST) is responsible for integrating concept elements across the 

NextGen-Airspace and NextGen-Airportal projects and for assessing system-wide 

benefits. 

To support those system-wide assessments, SLDAST is defining scenarios, assumptions, 

and metrics for common use by the NextGen-Airportal and NextGen-Airspace projects. 

This document defines those metrics. A companion document defines the scenarios and 

assumptions [2]. 

The set of metrics consists of system-level metrics and top-level metrics associated with 

discipline-level research in each RFA. 

System-level metrics measure National Airspace System (NAS) performance as a whole. 

RFA top-level metrics are specific to individual RFAs but measure characteristics that 

clearly and directly relate to the system-level metrics. RFA top-level metrics can be the 

same as the system-level metrics if applicable to the concept element. 

1.1 Purpose of This Document 

The primary purpose of this document is to define a common set of metrics for measuring 

NAS performance before and after the introduction of ASP-developed concepts and 

technologies designed to enable NextGen as the air transportation system handles 

increasing traffic. The metrics are directly traceable to JPDO NextGen goals and 

objectives [3] and will be useful for measuring the progress of ASP research in enabling 

NextGen. Monitoring this progress can then ensure that ASP research is on track toward 

achieving its targeted goals. 

Use of a common set of metrics across ASP will focus its research toward achieving 

system-level performance goals and objectives and enable the discipline-level RFAs to 

understand the impact of their concepts at the system level. 
2 

Direct traceability of system-level metrics and top-level RFA metrics to JPDO goals and 

objectives will facilitate effective and consistent performance assessments of new ASP 

concepts developed within the RFAs. 

Traceability will also allow ASP to demonstrate how the detailed technical work in each 

RFA benefits system-wide performance. 

1.2 Users of This Document 

The primary users of this document are: 

• SLDAST RFA researchers and staff 

• Other discipline-level RFA researchers and staff 

SLDAST researchers and staff will use the system-level metrics to measure NAS 

performance in system-wide assessments and integration-design studies. 

Other discipline-level RFA researchers and staff will use top-level RFA metrics to 

measure the contribution of specific concepts to broader, system-level measures of NAS 

performance. They can also use this document as a guide to define detailed, lower-level 

RFA metrics to support concept development. 

1.3 Document Scope 

The scope of this document is limited to the definition of metrics for measuring NAS 

capacity, efficiency, robustness, and safety. 

1.4 Document Structure and Content 

Section 1 introduces the purpose, users, scope and structure of this document. 

Section 2 identifies JPDO NextGen goals and objectives that are within the scope of 

NASA ASP research. Section 2 also identifies top-level, JPDO NextGen metrics that are 

relevant to measuring NASA ASP research. 

Note—Definition of JPDO NextGen metrics is ongoing; the set of JPDO 

NextGen metrics currently available is incomplete. 

Section 3 presents a set of criteria for defining “good” metrics. The criteria derive from 

several sources, including a May 2008 presentation to the JPDO by the JPDO Metrics 

Team [4]. 

Note—JPDO metrics are in development; hence, a published reference 

does not exist. 

Section 4 presents system-level metrics, developed using the criteria specified in Section 

3. 
3 

Section 5 presents an analysis of system-level metrics in Section 4. This analysis consists 

of evaluating the metrics against the criteria listed in Section 3, including justification for 

metrics that do not meet specific criteria. 

Section 6 presents an initial set of top-level RFA metrics that are traceable to the systemlevel metrics presented in Section 4 and to JPDO NextGen goals and objectives within 

the scope of the NASA ASP presented in Section 2. 

Section 7 presents a summary and conclusions. 

Annex A presents example values for many of the system-level metrics calculated from 

Airspace Concept Evaluation System (ACES) simulation results [5]. 

 

2 JPDO NextGen Goals, Objectives, and Metrics 

Table 1 lists the full set of six JPDO goals, together with the corresponding objectives 

from [3]. 

Note—Not all JPDO goals and objectives fall within the scope of the 

NASA ASP. JPDO NextGen goals and objectives that are within the scope 

of ASP are shaded.

Table 2 lists only those JPDO objectives that are within the scope of the NASA ASP. 

Table 2 further examines NextGen system-level metrics obtained from [4] for relevance to 

ASP. ASP will not always adopt the same or entire metric as the JPDO and may revise 

for clarity. However, ASP will retain the essential content of the JPDO’s NextGen system 

level metrics.
4 

Table 1—JPDO NextGen goals and objectives within the scope of NASA ASP rese

JPDO NextGen Goal 1 

Retain U.S. leadership 

in global aviation. 

JPDO NextGen Goal 2 

Expand capacity. 

JPDO NextGen Goal 3

Ensure safety. 

JPDO NextGen Goal 4 

Protect the 

environment. 

JPDO Goal 5 

Ensure our national 

defense. 

Objective 1.1: Retain our role as the world leader in aviation. Objective 1.2: Reduce costs for air transportation. Objective 1.3: Enable services tailored to traveler and shipper needs. Objective 1.4: Encourage performance-based, harmonized global standards for U.S. products and services. 

Objective 2.1: Satisfy future growth in demand (up to 3 times current levels) and operational diversity. Objective 2.2: Reduce transit time and increase predictability (domestic curb-to-curb transit time cut by 30%). Objective 2.3: Minimize impact of weather and other disruptions. 

Objective 3.1: Maintain aviation’s record as safest mode of transportation. Objective 3.2: Improve level of safety of U.S. air transportation system. Objective 3.3: Increase level of safety of worldwide air transportation system. 

Objective 4.1: Reduce noise, emissions, and fuel consumption. Objective 4.2: Balance aviation’s environmental impacts with other societal objectives. 

5.1: Provide for common defense while minimizing civilian constraints. 5.2: Coordinate a national response to threats. 5.3: Ensure global access to civilian airspace. 

NOTE 1–JPDO NextGen goals and objectives supported by the NASA Airspace Systems Program are SHADED. 
5 

Table 2—JPDO NextGen top-level metrics relevant to NASA ASP research 

JPDO NextGen Goal 1 

Retain U.S. leadership in global 

aviation. 

JPDO NextGen Goal 2 

Expand capacity. 

JPDO NextGen Goal 3 

Ensure safety.

JPD

Prote

Objective 1.2: Reduce costs for air transportation. o Metric 1.2: Projected percent 

reduction in total airtransportation-system (ATS)-related costs per instrumentflight-rules (IFR)-flight where total ATS-related costs equals the sum of the following costs: ATS operations + equipage 

(annualized) + aircraft delay + Airport Improvement Program (AIP) + non-AIP airport capacity enhancements. 

Objective 2.1: Satisfy future growth in demand (up to 3 times current levels) and operational diversity. 

o Metric 2.1.a: The number of 

flights that are projected to be operated on a good-weather day. Metric 2.1.b.: The average delay for that number of flights operated over the range of weather days expected during the course of a year. 

Objective 2.2: Reduce transit time and increase predictability (domestic curbto-curb transit time cut by 30%). o Metric 2.2: Projected percentage 

reduction of average curb-to-curb travel time as affected by ASP. 

Objective 2.3: Minimize impact of weather and other disruptions. 

o Metric 2.3: The JPDO has not 

defined a metric for measuring Objective 2.3. 

Objective 3.1: Maintain aviation’s record as safest mode of transportation. o Metric 3.1: The JPDO has not 

defined a metric for Objective 3.1. The JPDO Metrics Working Group is developing a safety risk metric that takes into account the severity and likelihood of occurrence of identified hazards. See “A proposal for the Integration Of Safety in NextGen Planning.” [11] 

Objective 3.2: Improve level of safety of U.S. air transportation system. o Metric 3.2: The JPDO has not 

defined a metric for Objective 3.2. The JPDO Metrics Working Group is developing a safety risk metric that takes into account the severity and likelihood of occurrence of identified hazards. See “A Proposal for the Integration of Safety in NextGen Planning.” [11] 

Objective 4.1emissions, ano Metric 4

reductioexposed Night Avat top 96 

Objective 4.2environmentsocietal objeo Metric 4

improveof the aiconsumeflown fo

NOTE 1–JPDO NextGen goals, objectives, and metrics supported by the NASA Airspace Systems Program are SHADED. 
6 

3 Defining Criteria for “Good” Metrics 

This section defines the criteria for developing “good” metrics based on a survey of metricsrelated literature. Most of the criteria were adapted from a May 2008 presentation to the JPDO 

[4]. This follows from the alignment of NASA ASP research with JPDO NextGen goals and 

objectives. Other sources include [5], [6], [7], [8], and [9]. 

The criteria provide the basis for the selection of system-level metrics (presented in Section 4) 

and for the RFA metrics (presented in Section 6) that will be used to evaluate ASP research. 

Table 3 lists the criteria for defining the metrics. 

Table 3—Criteria for defining metrics used to evaluate NASA ASP research 

No. Criteria Definition 

1 The metric provides a direct indicator of progress toward one of more strategic goals and objectives 

cited in “Section 2: JPDO NextGen Goals, Objectives, and Metrics” of this document. 

2 The metric is clear and unambiguous. 

3 The description includes measurement assumptions and definitions. 

4 The metric is sensitive to changes introduced by implementing NextGen. 

5 The metric is easily measured in simulation with valid and consistent results. 

6 The metric can be used to measure the performance of future concepts and compared with current 

performance. 

7 The metric is robust, so it is applicable over a range of assumptions and different future scenarios. 

8 The metric is convertible to dollars, so that related costs or benefits can be readily valued in 

monetary terms. 

9 The metric must not drive the system toward optimizing something other than the relevant strategic 

goals. 

10 The metric provides useful information for influencing NextGen concept development decisions at 

the system level. 

11 The metric is consistent within the set of metrics; no metric shall conflict with any other metric. 

(Metrics that measures progress towards the same goal must indicate the same outcome.) 

Because not all JPDO goals and objectives are within the scope of NASA’s ASP research agenda 

(as shown previously in Table 1), not all JPDO NextGen criteria are relevant to the selection of 

metrics that will used to evaluate NASA ASP research. 

RFA metrics are traceable to the system-level metrics and meet the same criteria as the 

corresponding system-level metric. Not all metrics need to meet all criteria—e.g., not all metrics 

convert impacts to a dollar value. A discussion of exceptions is in Section 5. 
7 

4 Developing System-Level Metrics to Evaluate NASA ASP 

Research 

This section discusses general principles for analyzing the value of metrics for evaluating NASA 

ASP research. 

4.1 Acceptable NAS Delay 

The JPDO Metrics Working Group introduced the idea of a “system operating point” for the 

NAS [4], beyond which delays become unacceptable. Capacity, therefore, becomes a function of 

throughput and delay. 

Determining how much delay is acceptable is a subjective process. One approach is to apply 

current practice. Today, the FAA defines a flight as late if it arrives more than 15 minutes after 

the scheduled arrival time [10]. However, the airlines’ practice of including extra time in their 

published schedules to accommodate anticipated delays is a complicating factor. 

NASA’s Advanced Concepts Evaluation System (ACES) [5] determines delay by comparing a 

simulated NAS trajectory with an unimpeded ideal trajectory. Thus, using the 15-minute value 

for an ACES simulation is not strictly justified. However, it is necessary to define acceptable 

delay. Therefore, the NASA ASP uses the following definition: 

• Mean delay must not exceed 15 minutes during any 1-hour period. 

• The 95th-percentile of delay must not exceed 30 minutes during any 1-hour period. 

• The 99th-percentile of delay must not exceed 60 minutes during any 1-hour period. 

In addition, it is important to distinguish between delay to commercial flights and delay to all 

flights. Including non-commercial flights in the calculation of mean delay can be misleading, 

since many non-commercial flights use small, uncongested airports and will bias the delay 

results toward lower values. Therefore, it is necessary to calculate delay to all flights and to 

commercial passenger flights only; both must satisfy all conditions. 

Accordingly, capacity metrics must be based on a common definition of acceptable delay that 

defines the system operating point. This will allow for consistent representation of future 

capacity impacts of new ASP concepts. 

Simulation of any concept or synthesis of concepts using the ASP common scenarios [2] enables 

the system operating point to be determined. The common scenarios demand increments are 

0.5X of the baseline demand; a curve fit between 0.5X increments should be sufficiently accurate 

to determine the operating point at which delay becomes unacceptable. 

4.2 Estimating NAS Performance 

ACES is the primary NAS-wide simulation used to estimate both current and future NAS 

performance. Currently, ACES simulates instrument flight rules (IFR) flights only, including 

general aviation flights that fly IFR. 

Comparison of performance metrics with future concepts to a reference day’s performance 

representative of the current system without future concepts allows assessment of benefits. The 
8 

reference day is a good weather, high-volume day without any key system failures as defined in 

[2]. 

4.3 Calculating Metric Values and Performing Statistical Analysis 

Most non-commercial flights fly into small, uncongested airports and, thus, experience lowaverage delays. Commercial flights, on the other hand, can experience much higher average 

delays. Therefore, to avoid a biased result, it is necessary to calculate metric values for all flights 

separately from metric values for commercial passenger flights. 

Calculation of the total, mean, 95th, and 99th-percentile values of metrics is required for metrics 

that have a distribution of values. 

Reporting the corresponding values for metrics stated as a ratio or percentage is required for 

context. It is insufficient to report a factor or percentage improvement only, because this gives no 

indication of the significance of the improvement. 

Metrics values will be calculated and annualized over the range of weather days (good to bad) 

and traffic volumes (low to high) expected during the course of a year as defined in [2]. 

4.4 Estimating NAS Capacity 

Throughput is defined as the number of flights or the number of passengers or tonnage of freight 

transported during the time of interest to the researchers––i.e., typically a year of NAS 

operations or day of simulated operations. 

The scope of NASA ASP research is gate-to-gate. Therefore, NASA ASP research focuses on 

aircraft-related concepts. In contrast, the JPDO NextGen scope of research is curb-to-curb. 

Therefore, some JPDO metrics measure benefits related to airport-side improvements. One such 

metric is JPDO Metric 2.2: “Projected percent reduction of average curb-to-curb travel time.” 

This metric can include the value of improvements to security procedures for example that 

decrease passenger curb-to-gate times. 

Even though NASA ASP research focuses on aircraft-related concepts, the NAS exists to 

transport passengers and cargo, not merely to move aircraft around, per se. 

There are different viable scenarios that can transport the demand of passengers and cargo in the 

NAS. Alternate future scenarios can use different numbers and sizes of aircraft to transport the 

same future demand. Therefore, it is important to have capacity metrics that track passengers and 

cargo in addition to metrics for tracking aircraft. Scenarios that use larger aircraft and more 

routes that are direct may allow transport of more passengers from origin-to-destination for the 

same system operating point. 

Table 4 lists system-level capacity metrics defined in terms of throughput within the acceptable 

delay limits specified in Section 4.1 “Acceptable NAS Delay.” The metrics relate directly to the 

following JPDO NextGen goal and objectives: 

• Goal 2: Expand capacity.
9 

o Objective 2.1: Satisfy future growth in demand (up to 3 times current levels) and 

operational diversity. 

Table 4—System-level capacity metrics 

NASA ASP 

No. Metric 

JPDO NextGen 

Goal/objective Metric 

Comment/how calculated 

ASP-C1 The number of flights that 

can be operated on a 

good-weather day within 

the delay limits specified 

in Section 4.1. 

Goal 2: Expand 

capacity. 

Objective 2.1: 

Satisfy future 

growth in demand 

(up to 3 times 

current levels) and 

operational 

diversity. 

Metric 2.1.a: 

The number of 

flights that are 

projected to be 

operated on a 

good-weather 

day. 

Same metric as JPDO NextGen. 

Obtained from ACES output, 

using common scenarios in 

0.5X demand increments. 

ASP-C2 The number of passenger 

origin-to-destination trips 

that can be operated on a 

good-weather day within 

the delay limits specified 

in Section 4.1. 

Goal 2: Expand 

capacity. 

Objective 2.1: 

Satisfy future 

growth in demand 

(up to 3 times 

current levels) and 

operational 

diversity. 

N/A Metric ASP-C2 is not redundant 

to ASP-C1 since it depends on 

fleet mix and route structure. 

Obtained from demand analysis. 

ASP-C3 The freight tonnage that 

can be transported on a 

good-weather day within 

the delay limits specified 

in Section 4.1. 

Goal 2: Expand 

capacity. 

Objective 2.1: 

Satisfy future 

growth in demand 

(up to 3 times 

current levels) and 

operational 

diversity. 

N/A Obtained from demand analysis. 

4.5 Estimating NAS Efficiency 

Table 5 defines system-level efficiency metrics in terms of reduction in trip times and fuel usage 

attributable to NextGen improvements. The efficiency metrics relate directly to the following 

JPDO NextGen goals and objectives: 

• Goal 1: Retain U.S. leadership in global aviation. 

o Objective 1.2 Reduce costs for air transportation. 

• Goal 2: Expand capacity. 

o Objective 2.2: Reduce transit time and increase predictability (domestic curb-to-curb 

transit time cut by 30%). 

• Goal 4: Protect the environment. 

o Objective4.1: Reduce noise, emissions and fuel consumption. 
10 

Table 5––System-level efficiency metrics 

NASA ASP 

No. Metric 

JPDO NextGen 

Goal/objective Metric 

Comment/how calculated 

ASP-E1 The reduction in 

gate-to-gate 

aircraft transit 

time. 

Goal 1: Retain U.S. 

leadership in global 

aviation. 

o Objective 1.2 Reduce 

costs for air 

transportation. 

Goal 2: Expand capacity. 

o Objective 2.2: Reduce 

transit time and 

increase predictability 

(domestic curb-tocurb transit time cut 

by 30%) 

Metric 2.2: Projected 

percent reduction of 

average curb-to-curb 

travel time as affected 

by NASA ASP. 

Concurrent with capacity 

increase. 

Obtained directly from 

ACES output. 

ASP-E2 The reduction in 

gate-to-gate 

passenger originto-destination 

trip time. 

Goal 1: Retain U.S. 

leadership in global 

aviation. 

o Objective 1.2 Reduce 

costs for air 

transportation. 

Goal 2: Expand capacity. 

o Objective 2.2: Reduce 

transit time and 

increase predictability 

(domestic curb-tocurb transit time cut 

by 30%). 

Metric 2.2: Projected 

percent reduction of 

average curb-to-curb 

travel time as affected 

by NASA ASP. 

Measures the time saved 

by more direct routing. 

Obtained from ACES 

output combined with 

demand analysis. 

ASP-E3 The reduction in 

fuel usage of the 

aircraft fleet in 

terms of fuel 

consumed per 

aircraft nautical 

mile. 

Goal 1: Retain U.S. 

leadership in global 

aviation. 

o Objective 1.2 Reduce 

costs for air 

transportation. 

Goal 4: Protect the 

environment. 

o Objective 4.1: Reduce 

noise, emissions, and 

fuel consumption. 

Metric 4.1: Projected 

percent improvement 

in fuel efficiency of 

the aircraft fleet in 

terms of fuel 

consumed per 

aircraft-kilometerflown for all IFR 

operations. 

Obtained directly from 

ACES output. 
11 

NASA ASP 

No. Metric 

JPDO NextGen 

Goal/objective Metric 

Comment/how calculated 

ASP-E4 The reduction in 

fuel usage of the 

aircraft fleet in 

terms of fuel 

consumed per 

passenger 

nautical mile 

flown 

Goal 1: Retain U.S. 

leadership in global 

aviation. 

o Objective 1.2 Reduce 

costs for air 

transportation. 

Goal 4: Protect the 

environment. 

o Objective 4.1: Reduce 

noise, emissions, and 

fuel consumption. 

N/A Measures the benefit of 

using larger aircraft that 

are generally more efficient 

per passenger mile. 

Obtained from ACES 

output combined with 

demand analysis. 

ASP-E5 The reduction in 

fuel usage of the 

aircraft fleet in 

terms of fuel 

consumed per 

passenger originto-destination 

great-circle 

nautical miles. 

Goal 1: Retain U.S. 

leadership in global 

aviation. 

o Objective 1.2 Reduce 

costs for air 

transportation. 

Goal 4: Protect the 

environment. 

o Objective 4.1: Reduce 

noise, emissions, and 

fuel consumption. 

N/A Measures the fuel savings 

of more direct routing, 

compared to using 

connections. 

Obtained from ACES 

output combined with 

demand analysis. 

ASP-E6 The reduction in 

fuel usage of the 

aircraft fleet in 

terms of fuel 

consumed per 

freight ton 

nautical mile 

flown. 

Goal 4: Protect the 

environment. 

o Objective 4.1: Reduce 

noise, emissions, and 

fuel consumption. 

N/A Obtained from ACES 

output combined with 

demand analysis. 

4.6 Estimating NAS Robustness 

Table 6 presents system-level robustness metrics defined in terms that compare system 

performance under good weather and nominal operating conditions with system performance 

under less-than-nominal conditions. These conditions include extreme weather (e.g., hurricanes), 

system-level failures and emergencies, and a range of weather conditions routinely encountered 

throughout the year. 

System robustness is not an explicit goal of the JPDO. However, the efficiency metrics defined 

here directly relate to the following JPDO NextGen goal and objectives: 

• Goal 2: Expand capacity. 

o Objective 2.2: Reduce transit time and increase predictability (domestic curb-tocurb transit time cut by 30%). 

o Objective 2.3: Minimize impact of weather and other operations. 
12 

Both objectives address robust system operation. Robustness is also a vital characteristic for 

achieving other JPDO NextGen goals: 

• Goal 1: Retain U.S. leadership in global aviation. 

• Goal 3: Ensure safety. 

• Goal 4: Protect the environment. 

Consequently, SLDAST will measure changes to certain robustness metrics between a baseline 

system state, and the projected future state employing new, ASP-developed concepts. 

Table 6—System-level robustness metrics 

NASA ASP 

No. Metric 

JPDO NextGen 

Goal/objective Metric 

Comment/how calculated 

ASP-R1 The ratio of ASP 

capacity metrics for 

poor weather and/or 

system failure 

scenarios, within 

the delay limits 

specified in Section 

4.1 compared with 

the good weather, 

nominal operating 

conditions scenario. 

Goal 2: Expand capacity. 

o Objective 2.2: Reduce 

transit time and 

increase predictability 

(domestic curb-to-curb 

transit time cut by 

30%). 

o Objective 2.3: 

Minimize impact of 

weather and other 

disruptions. 

N/A Concepts that show less 

capacity reduction are more 

robust. 

Obtained from ACES output, 

using common scenarios in 

0.5X demand increments. 

ASP-R2 The mean gate-togate delay for the 

good-weather 

number of flights, 

flown over the 

range of weather 

days expected in the 

course of a year. 

Goal 2: Expand capacity. 

o Objective 2.1: Satisfy 

future growth in 

demand (up to 3 times 

current levels) and 

operational diversity. 

o Objective 2.3: 

Minimize impact of 

weather and other 

disruptions. 

Metric 2.1.b: The 

average delay for 

that number of 

flights operated 

over the range of 

weather days 

expected during 

the course of a 

year. 

Same metric as JPDO. 

Obtained from ACES output 

using the five common 

weather day scenarios. 
13 

NASA ASP 

No. Metric 

JPDO NextGen 

Goal/objective Metric 

Comment/how calculated 

ASP-R3 The variance of 

gate-to-gate delay 

for the goodweather number of 

flights, for the poor 

weather and/or 

system failure 

scenarios, compared 

with the good 

weather, normal 

operating conditions 

scenario. 

Goal 2: Expand capacity. 

o Objective 2.2: Reduce 

transit time and 

increase predictability 

(domestic curb-to-curb 

transit time cut by 

30%). 

N/A Measures predictability of 

travel times and how this 

degrades. 

Obtained from ACES output. 

4.7 Estimating NAS Safety 

The JPDO Safety Working Group (SWG) has proposed an approach for integrating safety into 

NextGen planning. The approach is described in the document, A Safety Working Group 

Proposal for the Integration of Safety in NextGen Planning [11]. Key points include: 

• Safety must be accounted for when estimating NextGen benefits. 

• Two safety assessment tiers are proposed. They are: 

o Focus on operational hazards by performing a concept hazard assessment for each 

proposed operational improvement. 

o Perform a capability safety assessment that will focus integrating a set of 

operational improvements within a larger operational context. 

• Data-driven, quantitative assessments will be made where possible. When this is not 

possible, qualitative assessments will be made based on subject matter expertise. 

Risk analysis is a common technique for examining safety issues for airspace systems. Safety 

issues undergoing research in the NASA ASP relate to risks associated with aircraft collisions 

with other aircraft, birds, terrain, and static objects as well as to risks associated with aircraft 

encounters with severe weather and atmospheric/wake turbulence. 

FAA Order 8040.4 defines a hazard as “a condition, event, or circumstance that could lead to or 

contribute to an unplanned or undesirable event.” 
14 

The notion of risk is a product of the likelihood of the event and the severity of the 

consequences. Combining the severity of consequence with the likelihood of occurrence in a 

matrix gives an assessment of risk. The FAA defines five categories of “consequence” as the 

following: 

1. Catastrophic 

2. Hazardous 

3. Major 

4. Minor 

5. No safety effect 

The FAA also defines four categories of “likelihood.” Each has an associated probability of 

occurrence per-operational-hour. The four categories of likelihood are the following: 

1. Probable 

2. Remote 

3. Extremely remote 

4. Extremely improbable 

Although the assessment of consequence is subjective, the FAA gives clear guidelines. For 

example, the FAA defines “catastrophic” as “results in multiple fatalities and/or loss of the 

system.” The likelihood of a hazardous event for future concepts is usually difficult to quantify 

due to lack of data. However, it may be possible to estimate the probable range of the likelihood 

value based on simulation data that is validated using similar, current operational concept data. If 

such data are not available, a qualitative assessment based on subject mater expertise must be 

made. The JPDO SWG allows either, with an obvious preference for the data-driven approach. 

Validation of simulation results is often problematic, and the use of subject matter opinion 

becomes unreliable when the future concept differs from current concepts. Therefore, it is 

important to use caution when estimating the likelihood of a hazardous event. Careful attention 

to the development process and use of advanced techniques (such as formal analysis) contribute 

to a safe design and should be used to the fullest extent possible. Thus, for these reasons, 

defining safety metrics that meet the criteria in Section 3 is not a straightforward process.

4.7.1 Justifying the Selection of System-Level Safety Metrics 

This section presents a justification for the system-level safety metrics in Table 7. 

The paradigm for addressing uncertainty related to collision risk is to utilize protection zones 

around aircraft and exclusion zones around weather, which are sized appropriately to allow for 

uncertainty. Intrusion into a zone constitutes a loss of separation (LoS). 

Such LoS then, by definition, constitutes a safety incident without necessarily resulting in an 

actual collision. Therefore, LoS, by definition, serves as a surrogate for a safety metric, although 

it does not measure safety directly. The direct measure would be the number of collisions. 

However, it is not possible for ASP to measure collision metrics in simulation. Therefore, safety 

metrics S1–S3 in Table 7 are, by necessity, only precursors and represent necessary-butinsufficient criteria for an accident to occur. 
15 

The problem with defining safety metrics in this way is that there is no operational data available 

for future concepts. Therefore, it is not possible to correlate precursors to actual accidents. 

Furthermore, obtaining safety precursor data from simulation is difficult. System-level 

simulations, such as ACES, can collect these metrics. However, validation requires high fidelity 

simulation, which is likely to be computationally intensive, and because LoS are rare, require 

large amounts of simulation time to collect. 

The metrics ASP-S4, “Consequence of hazardous event,” and ASP- S5, “Likelihood of 

hazardous event,” are included in accordance with the FAA and JPDO approach to risk analysis, 

although it may be difficult for the NASA ASP to obtain accurate estimates of likelihood. 

Table 7—System-level safety metrics 

NASA ASP 

No. Metric 

JPDO NextGen 

Goal/objective Metric 

Comment/how calculated 

ASP-S1 Number of 

losses of 

separation 

with traffic or 

weather. 

Goal 3: Ensure safety. 

o Objective 3.1: Maintain 

aviation’s record as safest 

mode of transportation. 

o Objective 3.2: Improve level 

of safety of U.S. air 

transportation system. 

N/A This is a hazardous event. 

Measure initially using ACES, 

then verify in high fidelity airtraffic operations laboratory. 

ASP-S2 Time to 

predicted loss 

of separation 

from time of 

conflict 

detection. 

Goal 3: Ensure safety. 

o Objective 3.1: Maintain 

aviation’s record as safest 

mode of transportation. 

o Objective 3.2: Improve level 

of safety of U.S. air 

transportation system. 

N/A This is an indicator of the 

probability of a missed 

detection. 

Measure initially using ACES, 

then verify in high fidelity airtraffic operations laboratory. 

ASP-S3 Variance of 

closest point 

of approach 

for resolved 

conflicts. 

Goal 3: Ensure safety. 

o Objective 3.1: Maintain 

aviation’s record as safest 

mode of transportation. 

o Objective 3.2: Improve level 

of safety of U.S. air 

transportation system. 

N/A The closest point-of-approach 

is not a good indicator of 

safety, since an efficient 

system will minimize 

separations. Higher variance 

may indicate lower reliability 

of the conflict resolutions. 

Measure initially using ACES, 

then verify in high fidelity airtraffic operations laboratory. 
16 

NASA ASP 

No. Metric 

JPDO NextGen 

Goal/objective Metric 

Comment/how calculated 

ASP-S4 Consequence 

of hazardous 

event. 

Goal 3: Ensure safety. 

o Objective 3.1: Maintain 

aviation’s record as safest 

mode of transportation. 

o Objective 3.2: Improve level 

of safety of U.S. air 

transportation system. 

This is 

consistent 

with JPDO 

approach [11]. 

Based on FAA consequence 

categories. 

Determine from safety 

analysis. 

ASP-S5 Likelihood of 

hazardous 

event. 

Goal 3: Ensure safety. 

Objective 3.1: Maintain aviation’s 

record as safest mode of 

transportation. 

Objective 3.2: Improve level of 

safety of U.S. air transportation 

system. 

This is 

consistent 

with JPDO 

approach [11]. 

Based on similar current 

operational concept data, 

simulation data or use subject 

matter expert opinion. 

Determine from safety 

analysis. 

 

5 Evaluating System-Level Metrics 

This section evaluates system-level metrics for measuring capacity, efficiency, robustness, and 

safety (which were discussed in Section 4) using the criteria for “good” metrics (which were 

defined in Section 3). The evaluation is presented in Table 8 and in subsequent comments that 

reference the table. The system-level metrics need not meet all criteria to be useful. In addition, 

some criteria may not be relevant to a particular system-level metric. If a system-level metric 

does not meet the criteria for a good metric, an explanation is given. 
17 

Table 8—Evaluation of NASA ASP system-level metrics versus criteria 

No. ASP capacity, efficiency, robustness, and safety metrics 

ASP-C1 ASP-C2 ASP-C3 ASP-E1 ASP-E2 ASP-E3 ASP-E4 ASP-E5 ASP-E6 ASP-R1 ASP-R2 ASP-R3 ASP-S1 

1 2 3 4 G 5 H 6 7 I 8 A C D E F 9 B 10 11 Fully Meets Partly Meets Does not meet Not Re

 NOTE 1—The notations ASP-C1–ASP-S5 in the white row reference capacity, efficiency, robustness, and safety metrics defined in Section 4 

o Table 4—System-level capacity metrics (ASP-C1, ASP-C2, ASP-C3) o Table 5—System-level efficiency metrics (ASP-E1, ASP-E2, ASP-E3, ASP-E4, ASP-E5, ASP-E6) 

o Table 6—System-level robustness metrics (ASP-R1, ASP-R2, ASP-R3) 

o Table 7—System-level safety metrics (ASP-S1, ASP-S2, ASP-S3, ASP-S4, ASP-S5) 

NOTE 2—The notations A-T in the gold and red cells reference comments in Section 5.1 (which follows). 

 NOTE 3—The notations 1-11 in the pink column reference criteria 1–11 (“No.) described in Table 3: Criteria for Defining Metrics Used to Ev
18 

The following comments reference notations A–T in the yellow and red cells of Table 8. 

A. Capacity metric ASP-C1 does not fully meet Criteria No. 8 because it is not easy 

to obtain a monetary value for a flight. The increase in capacity expected to derive 

from NextGen in terms of number of additional flights accommodated is 

financially beneficial, but it would be difficult to quantify. 

B. Capacity metric ASP-C1 does not fully meet Criteria No. 9because it measures 

capacity in units of the number of aircraft. This potentially drives the system 

toward maximizing the number of aircraft rather than maximizing the transport of 

the passengers and cargo. 

C. Capacity metric ASP-C2 does not fully meet Criteria No. 8 because it is not easy 

to obtain the average monetary value of a passenger trip. 

D. Capacity metric ASP-C2 does not fully meet Criteria No. 8 because it is not easy 

to obtain the average monetary value of a ton of freight. 

E. Robustness metric ASP-R1 does not fully meet Criteria No. 8. The same problems 

apply as for capacity metrics, with respect to the difficulty of obtaining a 

monetary value. See “A,” above. 

F. Robustness metric ASP-R3 does not meet Criteria No. 8. It is not possible to 

convert variance in gate-to-gate time to a monetary value. 

G. Safety metric ASP-S1 does not meet Criteria No. 4. Loss-of-separation occurs 

infrequently for any reasonable concept, so this metric is not very sensitive in that 

it will be difficult to obtain statistically meaningful results to compare concepts. 

H. Safety metric ASP-S1 does not meet Criteria No. 5. It requires high fidelity, 

computationally intensive simulation with numerous runs to provide valid and 

consistent results. It is therefore not easy to measure. 

I. Safety metric ASP-S1 does not fully meet Criteria No. 7. Losses-of-separation are 

likely to be sensitive to modeling assumptions. Lower fidelity simulation may 

lead to more (or possibly fewer) losses-of-separation than would be the case in 

high-fidelity simulation or in actual service. 

J. Safety metric ASP-S2 does not fully meet Criteria No. 5. Conflicts occur much 

more frequently than losses-of-separation, so this metric is easier to measure than 

S1. However, it still requires high-fidelity simulation to ensure validity. 

K. Safety metric ASP-S2 does not fully meet Criteria No. 6. It may not be possible to 

compare this metric with current system performance. 

L. Safety metric ASP-S2 does not fully meet Criteria No. 10. It may not be of 

interest at the system level. 
19 

M. Safety metric ASP-S3 does not fully meet Criteria No. 5 because it requires highfidelity simulation to ensure validity. 

N. Safety metric ASP-S2 does not fully meet Criteria No. 6. It may not be possible to 

compare this metric with current system performance. 

O. Safety metric ASP-S3 does not fully meet Criteria No. 9. This metric could 

induce a false goal, because reducing the variance of closest point-of-approach 

may not be an indicator of improved safety. 

P. Safety metric ASP-S3 does not fully meet Criteria No. 10. It may not be of 

interest at the system level. 

Q. Safety metric ASP-S5 does not fully meet Criteria No. 4. It may be difficult to 

measure likelihood, so may not be sensitive to changes to current system 

operations. 

R. Safety metric ASP-S5 does not fully meet Criteria No. 5. It may not be possible to 

use simulation to measure reliably the likelihood of a hazardous event associated 

with a future concept. 

S. Safety metric ASP-S5 does not fully meet Criteria No. 6. It may be difficult to 

measure likelihood, so it may not be possible to compare this metric with current 

system performance. 

T. Safety metric ASP-S5 does not meet Criteria No. 7. The estimate of likelihood 

could be very sensitive to assumptions. 

5.1 Discussion 

The capacity, efficiency, and robustness metrics meet the majority of the criteria defined 

in Section 3. However, capacity metrics are not easy to convert to a monetary value, 

although this may be of more importance to the JPDO than to the NASA ASP. 

Capacity metric ASP-C1: The number of flights that can be operated on a good weather 

day within the delay limits specified in Section 4.1 can drive the system toward a false 

goal if used unwisely. However, with sensible interpretation, it can be a useful metric. (A 

hypothetical example of using this metric unwisely might be to rate a concept highly 

because it made use of a large number of small aircraft to increase airport capacity. Small 

aircraft require less wake separation from other small aircraft, and thus they increase 

airport throughput in terms of operations. However, they are not likely to increase 

passenger throughput when compared to using a smaller number of larger aircraft.) 

The safety metrics are more problematic; they fail several criteria. The best safety metric 

is ASP-S1: Number of losses of separation with traffic or weather. A concept that results 
20 

in a significant number of losses of separation is unsafe by definition, because safety 

concerns directly drive separation standards. 

However, loss-of-separation must occur infrequently if the concept is useful, so it is 

difficult to measure with statistical significance. The validity of this metric is dependent 

on the availability of high-fidelity simulation. 

Safety metric ASP-S2: Time to predicted loss of separation from time of conflict 

detection and safety metric ASP-S3: Variance of closest point of approach for resolved 

conflicts are easier to measure, because conflicts occur much more frequently than 

losses-of-separation. However, it is difficult to establish a direct link between these 

metrics and safety. Even so, these metrics may be the best that we can obtain from 

simulation and they relate to collisions in a logical and direct way. 

Safety metric ASP-S4: Consequence of hazardous event is a useful metric to evaluate for 

any concept. The FAA consequence categories in Order 8040.4 include clear guidelines 

on how to categorize events. However, it may require extensive analysis to determine the 

set of possible hazardous events for a NextGen concept. 

Safety metric ASP-S5: Likelihood of a hazardous event is the companion to ASP-S4: 

Consequence of hazardous event and is much more difficult to estimate. It may prove 

impossible for NASA ASP to evaluate this metric reliably for some or all of the NextGen 

concepts. 

Even so, the metric ASP-S4: Consequence of hazardous event alone is a useful indicator. 

If a concept has potential for one or more hazardous events in the catastrophic category, 

this indicates that the concept requires scrutiny. 

6 System-Level RFA Metrics 

This section presents top-level RFA metrics for the NextGen-Airspace and NextGenAirportal projects. Top-level RFA metrics shall be an indicator of system-level 

performance and shall address one or all of capacity, efficiency, robustness and safety. 

The RFAs likely need lower level concept-specific metrics to evaluate concept 

development but these are not the subject of this document. 

NextGen-Airspace Project RFAs are the following: 

• Dynamic Airspace Configuration (DAC) 

• Traffic Flow Management (TFM) 

• Separation Assurance (SA) 

• Airspace Super Density Operations (ASDO) 

• System-Level Design, Analysis, and Simulation Tools (SLDAST) 

NextGen-Airportal Project RFAs are the following: 

• Safe and Efficient Operations (SESO) 

• Coordinated Arrival and Departure Operations Management (CADOM) 
21 

• Airportal and Metroplex Integration (AMI) 

6.1 Separation Assurance (SA) 

6.1.1 Overview 

The SA RFA is addressing airspace capacity barriers arising from human workload issues 

related to responsibility for maintaining separation assurance by utilizing sequential 

processing of sequence and merging with separation for transition and cruise airspace. 

SA research consists of the following elements: 

• A strategic conflict detection and resolution algorithm that uses aircraft intent 

information (planned trajectory) to detect and resolve conflicts predicted to occur up 

to about 30 minutes in the future. 

• A tactical conflict detection and resolution algorithm that uses aircraft state 

information; may use limited intent information to detect and resolve conflicts 

predicted to occur up to about 5 minutes in the future. 

The SA concept uses automation to detect conflicts and determine resolution maneuvers. 

There are two distinct concept variants; a centralized ground-based system and a 

distributed flight deck based system. On-going research will determine the allocation of 

SA functions between ground control and the aircraft. 

Both concept variants rely on GPS for navigation and ADS-B to provide aircraft position 

and intent information. The Traffic Alert and Collision Avoidance System (TCAS) 

remains the system of last resort to prevent collisions.

6.1.2 Airborne SA 

The flight deck system has access to current-state and trajectory-intent data for aircraft 

within Automatic Dependent Surveillance-Broadcast (ADS-B) range (nominally 150 

nm). ADS-B-equipped aircraft operate autonomously using automation to detect 

conflicts and calculate conflict-free resolution maneuvers. The flight crew selects from 

one or more resolutions for execution by the Flight Management System (FMS). The 

crew may request a different resolution or use tactical control to resolve the conflict, if 

necessary. 

A description of the flight deck based concept is available in [12]. 

6.1.3 Ground-based SA 

The ground-based system potentially has access to all of the NAS aircraft current state 

and trajectory intent data. ADS-B ground stations relay aircraft data to the centralized 

automation. The region of control of the automation could be center-wide or possibly the 

entire NAS. The automation system detects conflicts and then calculates a conflict-free 

resolution maneuver. This trajectory is uplinked to the aircraft for execution by the FMS 

after acceptance by the pilot. Controllers on the ground monitor the automation and may 

intervene, if necessary. 
22 

A description of the ground-based concept is available in [13]. 

6.1.4 Key Functions, Features, Characteristics and Capabilities 

Both ground-based and airborne concept variants rely on automation to detect conflicts 

and calculate resolutions using strategic and tactical algorithms. In normal operation, 

controllers do not resolve conflicts and do not need to give verbal directives to aircraft. 

6.1.5 Expected Performance Benefits 

The SA concept variants can increase airspace capacity by removing the humanworkload limitations of the current controller-based system. This can potentially allow a 

larger number of aircraft to use regions of airspace, with less need for diversions. This 

can lead to reduced delays, shorter flight times, and less variability in gate-to-gate arrival 

times. 

6.1.6 Flight Phase 

The automated SA concept variants affect the en route phase of flight. 

6.1.7 Contribution to JPDO Top-Level Goals and Objectives 

The SA concept contributes to the following JPDO NextGen goals and objectives: 

• Goal 1: Retain U.S. leadership in global aviation. 

o Objective 1.2: Reduce costs for air transportation. 

• Goal 2: Expand capacity. 

o Objective 2.1: Satisfy future growth in demand (up to 3 times current levels) 

and operational diversity. 

o Objective 2.2: Reduce transit time and increase predictability (domestic curbto-curb transit time cut by 30%). 

o Objective 2.3: Minimize impact of weather and other disruptions 

• Goal 3: Ensure safety. 

o Objective 3.2: Improve level of safety of U.S. air transportation system 

• Goal 4: Protect the environment. 

o Objective 4.1: Reduce noise, emissions, and fuel consumption. 

6.1.8 Impacts to ASP System-Level Metrics 

ASP-C1: Automated SA increases en route airspace capacity by removing the en route 

sector workload constraint. 

ASP-S1: Automated SA prevents losses of separation that may otherwise have occurred 

at traffic levels that exceed human workload constraints. 

ASP-S2: Automated SA reduces the probability of a missed conflict detection that may 

otherwise have occurred at traffic levels that exceed human workload constraints. 

ASP-S4: Automated SA has potential for hazardous events that are different from the 

current system - the consequences of these needs to be determined from a safety analysis. 
23 

ASP-S5: Automated SA has potential for hazardous events that are different from the 

current system - the likelihood of these needs to be determined from a safety analysis. 

Table 9—SA system-level metrics 

1. HITL - Human In The Loop

RFA metric 

type/ no. 

RFA metric Related system-level 

metric 

Comments/ how calculated 

Capacity/ 

SA-C1 

Ratio of ASP-C1 with 

and without automated 

SA. 

ASP-C1: The number of 

flights that can be 

operated on a good 

weather day within the 

delay limits specified in 

Section 4.1. 

Airspace capacity will 

primarily be limited by delays 

due to resolution maneuvers. 

(An accurate determination of 

the number of LoS requires 

high-fidelity simulation.) 

Determined from ACES 

simulation. 

Capacity/ 

SA-C2 

Ratio of maximum traffic 

density with and without 

automated SA. 

ASP-C1: The number of 

flights that can be 

operated on a good 

weather day within the 

delay limits specified in 

Section 4.1. 

Either unacceptable numbers 

of resolutions (efficiency) or 

unacceptable likelihood of 

LoS (safety) will limit traffic 

density. 

Determined from high-fidelity 

simulation. 

Safety/ 

SA-S1 

Ratio of ASP-S1 with 

and without automated 

SA. 

ASP-S1: Number of 

losses of separation with 

traffic or weather. 

The safety of automated SA 

should be at least that of 

current SA, but should allow 

higher traffic densities. 

HITL1

 experiments may 

provide data for comparison 

with the current system. 

Safety/ 

SA-S2 

Ratio of ASP-S2 with 

and without automated 

SA. 

ASP-S2: Time to 

predicted loss of 

separation from time of 

conflict detection. 

The safety of automated SA 

should be at least that of 

current SA, but should allow 

higher traffic densities. 

HITL experiments may 

provide data for comparison 

with the current system. 

Safety/ 

SA-S3 

Consequence of 

hazardous event. 

ASP-S4: Consequence 

of hazardous event. 

Based on FAA Order 8040.4. 

Determined from safety 

analysis. 

Safety/ 

SA-S4 

Ratio of ASP-S5 with 

and without automated 

SA for hazardous events: 

- LoS 

ASP-S5: Likelihood of 

hazardous event. 

Automated SA should reduce 

the Likelihood of LoS at 

increased traffic levels 

compared to the current 

system. 

HITL experiments may 

provide data for comparison 

with the current system. 
24 

6.2 Traffic Flow Management (TFM) 

6.2.1 Overview 

The TFM concept uses a sequential optimization technique to manage air traffic flow 

under uncertainty in airspace capacity and demand. 

A deterministic integer-programming model assigns delays to aircraft under en route 

capacity constraints. The model assigns only departure controls, and a tactical control 

loop consisting of a shortest-path routing algorithm and an airborne delay algorithm 

refines the strategic plan to keep flights from deviating into capacity-constrained 

airspace. 

A description of the TFM concept is available in [14]. 

6.2.2 Key Functions, Features, Characteristics and Capabilities 

TFM is focused on modifying airspace/airports capacity by using multiple optimization 

techniques to adjust demand through departure times, route modification, adaptive speed 

control, etc., in the presence of uncertainty. The TFM concept uses sequential traffic flow 

optimization to achieve optimal departure control assignment with an integerprogramming model. Weather avoidance uses Dijkstra’s algorithm with convective 

weather translation. 

6.2.3 Expected Performance Benefits 

The TFM concept minimizes departure delays subject to weather-impacted airport and 

airspace capacity constraints. The concept enables better use of weather-impacted 

airspace through integrated ground and airborne flight controls and improves estimates of 

weather affected sector capacities. This allows more flights within acceptable delay limits 

and thus increases system capacity. 

6.2.4 Flight Phase 

The TFM concept affects the pre-departure and en route phases of flight. 

6.2.5 Contribution to JPDO NextGen Top-Level Goals and Objectives 

The TFM concept contributes to the following JPDO NextGen goals and objectives: 

• Goal 1: Retain U.S. leadership in global aviation. 

o Objective 1.2: Reduce costs for air transportation. 

• Goal 2: Expand capacity. 

o Objective 2.1: Satisfy future growth in demand (up to 3 times current levels) 

and operational diversity. 

o Objective 2.2: Reduce transit time and increase predictability (domestic curbto-curb transit time cut by 30%). 

o Objective 2.3: Minimize impact of weather and other disruptions 

• Goal 3: Ensure safety. 

o Objective 3.2: Improve level of safety of U.S. air transportation system 
25 

• Goal 4: Protect the environment. 

o Objective 4.1: Reduce noise, emissions, and fuel consumption. 

6.2.6 Impacts to ASP System-Level Metrics 

The TFM concept is a system-wide concept. Therefore, all TFM RFA top-level metrics 

correspond to ASP system-level metrics. 

ASP-C1: Some delay will exist even on a good-weather day due to demand exceeding 

capacity at some airports or in airspace regions for some periods during the day. 

Therefore, TFM may provide some increase in capacity even under good-weather 

conditions. 

ASP-E1: The TFM concept assigns optimal departure delays and shortest-path reroutes to 

flights subject to en route weather induced capacity constraints. As a result, the gate-togate aircraft transit times for such flights is reduced compared to current day operations, 

which relies on a collection of ground delay programs, airspace flow programs, and 

national-level reroutes that may not be optimal. 

ASP-E3: The TFM concept uses a shortest-path rerouting algorithm for identifying flight 

paths that deviate around en route weather hazards. These routes result in reduced fuel 

usage of the aircraft fleet over current day operations. 

ASP-R1: The TFM concept assigns optimal departure delays and shortest-path reroutes 

that improve schedule robustness by reducing delays due to weather and other disruptions 

compared to current day operations. 

ASP-S1: The TFM concept may reduce the number of instances of demand exceeding 

airspace or airport capacity so may reduce the likelihood of LoS. This is only likely to be 

the case when weather or other disruption causes a large loss in capacity. Under these 

conditions, it is conceivable that without TFM action traffic density exceeds the 

capability of SA to maintain separation. 

ASP-S2: As traffic-density increases, the number of conflicts increases and it is 

conceivable that the number of conflicts detected at short-notice due to unanticipated 

aircraft maneuvers also increases. The TFM will reduce the number of instances of 

demand exceeding capacity and hence may reduce the number of conflicts compared to 

not using TFM. 

ASP-S4: Automated TFM has potential for hazardous events that are different from the 

current system - the consequences of these needs to be determined from a safety analysis. 

ASP-S5: Given airport and airspace-capacity constraints, the TFM concept assigns 

optimal departure delays to prevent demand from significantly exceeding capacity. This 

may reduce the likelihood of hazardous events at increased traffic levels compared to the 

current system. Automated SA has potential for hazardous events that are different from 

the current system - the likelihood of these needs to be determined from a safety analysis. 
26 

Table 10—TFM system-level metrics 

RFA metric 

type/ no. 

RFA metric Related system-level 

metric 

Comments/ how calculated 

Capacity/ 

TFM-C1 

Ratio of ASP-C1 with 

and without automated 

TFM. 

ASP-C1: Number of flights 

that can be operated on a 

good weather day within 

the delay limits specified in 

Section 4.1. 

There may be a small benefit 

from TFM even on a good 

weather day with no 

disruptions. 

Determined from ACES 

simulation. 

Efficiency/ 

TFM-E1 

Ratio of ASP-E1 with 

and without TFM. 

ASP-E1: Reduction in gateto-gate aircraft transit time Determined from ACES 

simulation. 

Efficiency/ 

TFM-E2 

Ratio of ASP-E3 with 

and without automated 

FM. 

ASP-E3: Reduction in fuel 

usage of the aircraft fleet in 

terms of fuel consumed per 

aircraft nautical mile 

Determined from ACES 

simulation. 

Robustness/ 

TFM-R1 

Ratio of ASP-R1 with 

and without automated 

TFM. 

ASP-R1: Ratio of ASP 

system-level capacity 

metrics for poor weather 

and/or system failure 

scenarios, within the delay 

limits specified in Section 

4.1 compared with the good 

weather, nominal operating 

conditions scenario. 

Determined from ACES 

simulation. 

Safety/ 

TFM-S1 

Ratio of ASP-S1 with 

and without automated 

TFM. 

ASP-S1: Number of losses 

of separation with traffic or 

weather. 

Determined from ACES 

simulation..

Safety/ 

TFM-S2 

Ratio of ASP-S2 with 

and without automated 

TFM. 

ASP-S2: Time to predicted 

loss of separation from time 

of conflict detection. 

Determined from ACES 

simulation.

Safety/ 

TFM-S3 

Consequence of 

hazardous event. 

ASP-S4: Consequence of 

hazardous event. 

Based on FAA Order 8040.4. 

Determined from safety 

analysis. 

Safety/ 

TFM-S4 

Ratio of ASP-S5 with 

and without automated 

TFM for hazardous 

events: 

o Intrusion into 

weather 

o Significantly 

exceeding capacity 

ASP-S5: Likelihood of 

hazardous event. 

Determined from high-fidelity 

simulation. 

6.3 Dynamic Airspace Configuration (DAC) 

6.3.1 Overview 

DAC is focused on a new operational paradigm in ATM that seeks to modify static 

airspace resources (controllers/structure) by temporally increasing capacity based on the 

movement of resources. DAC research is focused on the following elements: 
27 

• Adapting airspace sector boundaries to balance controller workload as traffic demand 

fluctuates. 

• Creating air corridors dedicated to major traffic flows so that traffic in these corridors 

does not cross sector boundaries. Traffic may potentially self-separate within these 

corridors. 

Since the primary purpose of sectors is to partition traffic separation workload among 

controllers, DAC assumes a human controller workforce (as opposed to fully automated 

separation assurance), in at least some portions of the airspace. A description of the DAC 

concept elements is available in [15], [16], [17]. 

6.3.2 Key Functions, Features, Characteristics and Capabilities 

The DAC concept uses dynamic airspace to even out controller workload. Air corridors 

reduce controller workload. Sector boundaries may be adapted and air corridors created 

or removed several times a day to meet traffic demand. 

6.3.3 Expected Performance Benefits 

In the current system, sector boundaries are static and a dedicated team of controllers 

works each specific sector. Combination of these static sectors at times of low demand 

occurs today, but the DAC concept envisages a much more dynamic use of airspace. The 

main benefit is efficient use of resources, enabling accommodation of more traffic using 

the same number of controllers. 

Use of air corridors does not require sector handoffs and promotes smoother, organized 

traffic flows. The separation assurance function is simpler within a corridor; delegation to 

the flight deck with reduced in-trail separations is a possibility. The main benefit is 

increased airspace capacity. 

6.3.4 Flight Phase 

The DAC concept affects the en-route phase of flight.

6.3.5 Contribution to JPDO Top-Level Goals and Objectives 

The DAC concept contributes to the following JPDO NextGen goals and objectives: 

• Goal 1: Retain U.S. leadership in global aviation. 

o Objective 1.2: Reduce costs for air transportation. 

• Goal 2: Expand capacity. 

o Objective 2.1 Satisfy future growth in demand (up to 3 times current levels) 

and operational diversity. 

6.3.6 Impacts to System-level Metrics 

ASP-C1: DAC can accommodate increased demand within acceptable delay limits, by 

dynamically adapting airspace boundaries to meet demand and by creating air corridors 

along high traffic routes. 
28 

ASP-S4: DAC has potential for hazardous events that are different from the current 

system - the consequences of these needs to be determined from a safety analysis. 

ASP-S5: DAC has potential for hazardous events that are different from the current 

system - the likelihood of these needs to be determined from a safety analysis. 

 Table 11—DAC system-level metrics 

RFA 

metric 

type/ no. 

RFA metric Related system-level 

metric 

Comments/ how calculated 

Capacity/ 

DAC-C1 

Ratio of ASP-C1 with and 

without DAC concepts. 

ASP-C1: Number of flights 

that can be operated on a 

good weather day within the 

limits specified in Section 

4.1. 

For a human-controller based system, 

a more optimal design of airspace 

structures may allow increased 

capacity. 

Determined from ACES simulation. 

Capacity/ 

DAC-C2 

Ratio of the number of 

airspace sectors (plus 

corridors if any) with and 

without DAC concepts. 

ASP-C1: Number of flights 

that can be operated on a 

good weather day within the 

limits specified in Section 

4.1. 

For a human-controller based system, 

reducing the number of airspace 

structures may allow increased 

capacity. 

Determined from ACES simulation. 

Capacity/ 

DAC-C3 

Ratio of airspace 

complexity as defined in [17]

with and without DAC 

concepts. 

ASP-C1: Number of flights 

that can be operated on a 

good weather day within the 

limits specified in Section 

4.1. 

For a human-controller based system, 

reducing the airspace complexity 

may allow increased capacity. 

Determined from ACES simulation. 

Safety/ 

DAC-S3 

Consequence of hazardous 

event. 

ASP-S4: Consequence of 

hazardous event. 

Based on FAA Order 8040.4. 

Determined from safety analysis. 

Safety/ 

DAC-S4 

Ratio of ASP-S5 with and 

without DAC for hazardous 

events: 

o Loss of spatial awareness 

during sector 

reconfiguration 

o Intrusion into airspace 

corridor by other traffic 

ASP-S5: Likelihood of 

hazardous event. 

HITL experiments may provide data 

for comparison with the current 

system. 

Determined from high-fidelity 

simulation. 

6.4 Airspace Super Density Operations (ASDO) 

6.4.1 Overview 

ASDO is addressing airspace capacity barriers due to human workload/responsibility for 

separation assurance by utilizing simultaneous sequencing, spacing, merging, and deconfliction for terminal airspace with nearby runway thresholds. ASDO research focuses 

on the following elements: 

• Runway scheduler and router for very closely spaced parallel runway operations 

(VCSPRO) 
29 

• Dynamic area navigation (RNAV) and Required Navigation Performance (RNP) 

flexible routing 

• Flight-deck-based merging and spacing for arrivals 

• Continuous descent approach (CDA)/ optimal profile descent (OPD) 

A description of the ASDO concept elements are available in [18], [19], [20]. 

6.4.2 Key Functions, Features, Characteristics and Capabilities 

Aircraft pairing and offset routing to VCSPR allows near-visual flight rules (VFR) 

capability under instrument flight rules (IFR). 

GPS-based navigation and improved FMS allows aircraft to fly trajectories with less 

dispersion, which enables dynamic flexible routing. 

ADS-B-based flight deck systems allow aircraft to accurately merge into an arrival 

stream and self-space from a lead aircraft. Combined merging and spacing with a CDA/ 

OPD allows aircraft to follow a more optimal descent. 

6.4.3 Expected Performance Benefits 

In the current system, the capacity of VCSPR is much less under IFR conditions because 

operational rules require the dependent runways operate as if they were a single runway. 

The runway scheduler will pair aircraft with wake characteristics that minimize the 

required spacing and compute the required time of arrival at the arrival fixes. This will 

improve runway throughput. Use of accurate, and possibly offset, routing will restore 

runway throughput to near-VFR values. 

Dynamic, flexible routing with less trajectory dispersion allows more arrival and 

departure routes and allows routing more closely around weather and through smaller 

gaps in weather than is currently the case. The main benefits are improved throughput 

under all conditions and less degradation due to poor weather. Less reliance on fixed 

routes reduces the distance flown and hence reduces flight times during the arrival and 

departure phase. 

Flight-deck-based merging and spacing allows arriving aircraft accurately meet a 

required-time-of-arrival at the runway threshold and reduces the spacing buffer between 

aircraft due to reduced uncertainty. The main benefit is improved runway throughput. A 

secondary benefit is reduced workload for the arrival controller, due to delegation of 

functions to the flight deck. 

Use of a CDA/ OPD reduces fuel burn, emissions and noise but can have a negative 

impact on throughput. Combing a fuel-optimal descent with flight deck merging and 

spacing maintains throughput while allowing the aircraft to fly the desired OPD. 

6.4.4 Flight Phase 

The ASDO concept affects the arrival and departure phase of flight. 
30 

6.4.5 Contribution to JPDO Top-Level Goals and Objectives 

The SA concept contributes to the following JPDO NextGen goals and objectives: 

• Goal 1: Retain U.S. leadership in global aviation. 

o Objective 1.2: Reduce costs for air transportation. 

• Goal 2: Expand capacity. 

o Objective 2.1: Satisfy future growth in demand (up to 3 times current levels) 

and operational diversity. 

o Objective 2.2: Reduce transit time and increase predictability (domestic curbto-curb transit time cut by 30%). 

o Objective 2.3: Minimize impact of weather and other disruptions. 

• Goal 4: Protect the environment. 

o Objective 4.1: Reduce noise, emissions, and fuel consumption. 

6.4.6 Impacts to ASP System-Level Metrics 

ASP-C1: ASDO will provide increased throughput of VCSPR and allow increased traffic 

density in transition airspace and thus will increase system capacity. 

ASP-E1: ASDO allows shorter arrival and departure paths with less additional distance 

required for weather rerouting. This contributes to reduced gate-to-gates times. 

ASP-E3: The ASDO concept uses CDA/ OPD. This reduces fuel usage of the aircraft 

fleet compared to current day operations. 

ASP-R1: The ASDO concept allows VCSPRO under IMC closer to the throughput of 

VMC conditions. Dynamic flexible routing with less trajectory dispersions allows shorter 

path rerouting due to weather. Both of theses improve robustness by reducing the effect 

of weather on system capacity. 

ASP-S1: The ASDO concept increases airspace capacity so may reduce the likelihood of 

LoS for increased traffic levels. 

ASP-S4: The ASDO concept has potential for hazardous events that are different from 

the current system - the consequences of these needs to be determined from a safety 

analysis. 

ASP-S5: The ASDO concept provides dynamic, flexible routing with less trajectory 

dispersion and precision merging and spacing that may reduce the likelihood of 

hazardous events by reducing uncertainty. The ASDO concept has potential for 

hazardous events that are different from the current system - the likelihood of these needs 

to be determined from a safety analysis. 
31 

Table 12— ASDO system-level metrics 

RFA metric 

type/ no. 

RFA metric Related system-level 

metric 

Comments/ how calculated 

Capacity/ 

ASDO-C1 

Ratio of ASP-C1 with 

and without ASDO. 

ASP-C1: The number 

flights that can be 

operated on a good 

weather day within the 

delay limits specified in 

Section 4.1. 

Determined from ACES 

simulation. 

Efficiency/ 

ASDO-E1 

Ratio of ASP-E1 with 

and without ASDO. 

ASP-E1: The reduction 

in gate-to-gate aircraft 

transit time. 

Determined from ACES 

simulation. 

Efficiency/ 

ASDO-E2 

Ratio of ASP-E3 with 

and without ASDO. 

ASP-E3: The reduction 

in fuel usage of the 

aircraft fleet in terms of 

fuel consumed per 

aircraft nautical mile. 

Determined from ACES 

simulation. 

Robustness/ 

ASDO-R1 

Ratio of ASP-R1 with 

and without ASDO. 

ASP-R1: The ratio of 

ASP system-level 

capacity metrics for poor 

weather and/or system 

failure scenarios, within 

the delay limits specified 

in Section 4.1. 

Determined form ACES 

simulation. 

Safety/ 

ASDO-S1 

Ratio of ASP-S1 with 

and without ASDO. 

ASP-S1: The number of 

losses of separation with 

traffic or weather. Determined from high-fidelity 

simulation. 

Safety/ 

ASDO -S2 

Consequence of 

hazardous event. 

ASP-S4: Consequence 

of hazardous event. 

Based on FAA Order 8040.4 

Determined from safety 

analysis. 

Safety/ 

ASDO-S3 

Ratio of ASP-S5 with 

and without ASDO for 

hazardous events: 

- deviation from 

trajectory 

- loss of in-trail spacing 

ASP-S5: Likelihood of 

hazardous event. 

Determined from high-fidelity 

simulation. 

6.5 Coordinated Arrival and Departure Operations Management 

(CADOM) 

6.5.1 Overview 

The CADOM RFA is exploring concepts and technologies focused on mitigating 

operational constraints to maximizing single-airport capacity and facilitating metroplex 

operations. CADOM research is focused on the following elements: 

• Runway Configuration Management (RCM) 
32 

• Combined Arrival/ Departure Runway Scheduling (CADRS) 

• Wake Vortex modeling and prediction 

6.5.2 Key Functions, Features, Characteristics and Capabilities 

CADOM enables optimal runway configuration for weather conditions and allows use of 

the same runway for departures and arrivals with improved scheduling. Improved wake 

vortex position and intensity prediction given local weather conditions will enable 

reduced wake separations between aircraft. 

6.5.3 Expected Performance Benefits 

Efficient use of runways, better co-ordination of runway configuration changes and 

reduced in-trail wake-spacing based on predicted weather conditions will contribute to 

increased runway throughput. 

6.5.4 Flight Phase 

The CADOM concept affects the arrival and departure phase. 

6.5.5 Contribution to JPDO Top Level Goals and Objectives 

The CADOM concept contributes to the following JPDO NextGen goals and objectives: 

• Goal 2: Expand capacity. 

o Objective 2.1: Satisfy future growth in demand (up to 3 times current levels) 

and operational diversity. 

o Objective 2.3: Minimize impact of weather and other disruptions. 

6.5.6 Impacts to ASP System-Level Metrics 

ASP-C1: CADOM increases runway throughput and hence improves system capacity. 

ASP-R1: CADOM improves schedule robustness by reducing delays due to runway 

configuration changes due to weather and other disruptions compared to current day 

operations. 

ASP-S4: CADOM has potential for hazardous events that are different from the current 

system - the consequences of these needs to be determined from a safety analysis. 

ASP-S5: CADOM has potential for hazardous events that are different from the current 

system - the likelihood of these needs to be determined from a safety analysis. 
33 

Table 13—CADOM system-level metrics 

RFA metric 

type/ no. 

RFA metric Related system-level metric Comments/ how calculated 

Capacity/ 

CADOM-C1 

Ratio of ASP-C1 

with and without 

CADOM. 

ASP-C1: The number flights 

that can be operated on a 

good weather day within the 

delay limits specified in 

Section 4.1. 

Determined form ACES 

simulation. 

Robustness/ 

CADOM-R1 

Ratio of ASP-R1 

with and without 

CADOM. 

ASP-R1: The ratio of ASP 

system-level capacity metrics 

for poor weather and/or 

system failure scenarios, 

within the delay limits 

specified in Section 4.1. 

Determined form ACES 

simulation. 

Safety/ 

CADOM -S2 

Consequence of 

hazardous event. 

ASP-S4: Consequence of 

hazardous event. 

Based on FAA Order 8040.4 

Determined from safety 

analysis. 

Safety/ 

CADOM-S3 

Ratio of ASP-S5 

with and without 

CADOM for 

hazardous events: 

- simultaneous 

runway occupancy 

- loss of wakeseparation 

ASP-S5: Likelihood of 

hazardous event. 

Determined from high-fidelity 

simulation. 

6.6 Safe and Efficient Surface Operations (SESO) 

6.6.1 Overview 

The purpose of the SESO RFA is to manage traffic on the airport surface (gates, 

taxiways, and runways) safely and efficiently to enable maximum throughput and 

capacity in the airport environment. SESO research is focused on the following concept 

elements: 

• Pushback scheduler 

• Taxi planner 

• Departure planner 

• Environmental planner 

• Conformance monitor 

• Surface Conflict Detection and Resolution (CD&R) 

SESO concept elements rely on Trajectory Based Surface Operations (TBSO) that allow 

the precise position of all surface vehicles to be monitored and predicted allowing for 

efficient planning of surface operations. 

The result is a trajectory that enables an aircraft to leave the gate, taxi to the runway using 

the most efficient taxi route with minimal halts and smoothly take-off with minimal 
34 

holding at the runway threshold. Arriving aircraft similarly use TBSO, taking into 

account departing aircraft. An environmental planner assists with creating trajectories 

that minimize fuel burn and emissions. 

A conformance monitor and automated surface CD&R assist the pilot and controllers 

with maintaining safe separation from other aircraft, ground vehicles and obstructions. 

6.6.2 Key Functions, Features, Characteristics and Capabilities 

SESO uses a ground trajectory that includes the planned time at each waypoint for all 

aircraft and surface vehicles. Automation uses these trajectories to efficiently plan and 

schedule aircraft gate departures and taxi routes. Automated conformance monitoring and 

CD&R maintain separation. 

6.6.3 Expected Performance Benefits 

At some major airports, taxi-operations today are quite inefficient where standard 

operating practice is to have aircraft leave the gate despite not having a clear path to the 

runway. Aircraft then queue up all along the taxi-routes with numerous stops and starts, 

often taking tens of minutes to reach the runway. Aircraft often hold for a few more 

minutes at the threshold before take-off. At busy times, this practice results in a large 

number of aircraft burning fuel, and creating emissions, making no-progress toward their 

destination. This is also a waste of pilot and passenger time and uses valuable aircraft 

operating hours resulting in increased maintenance costs. 

There are good reasons for this inefficient practice; having a queue of waiting aircraft 

maximizes runway throughput in the presence of uncertainty and freeing up gates on time 

makes the gate available for arriving aircraft. 

SESO concept elements enable a reduction in the uncertainty associated with surface 

operations reducing the need for queuing up of aircraft while maintaining runway 

throughput. Accurate departure planning allows the aircraft to depart from the gate at the 

scheduled time without obstruction along the taxiways or holding at the runway 

threshold. The benefits will be reduced taxi-times, reduced fuel burn and emissions, cost 

savings to the airlines and saving of passenger and crew time. 

In addition, SESO may reduce the number of surface safety incidents by enabling more 

accurate and timely automated monitoring of conflicts between all surface traffic and 

obstructions. 

6.6.4 Flight Phase 

The SESO concept affects the gate-to-runway threshold phase of flight. 

6.6.5 Contribution to JPDO Top Level Goals and Objectives 

The SESO concept contributes to the following JPDO NextGen goals and objectives: 

• Goal 1: Retain U.S. leadership in global aviation. 
35 

o Objective 1.2: Reduce costs for air transportation. 

• Goal 2: Expand capacity. 

o Objective 2.2: Reduce transit time and increase predictability (domestic curbto-curb transit time cut by 30%). 

• Goal 3: Ensure safety. 

o Objective 3.2: Improve level of safety of U.S. air transportation system 

• Goal 4: Protect the environment. 

o Objective 4.1: Reduce noise, emissions, and fuel consumption. 

6.6.6 Impacts to ASP System-Level Metrics 

ASP-E1: The SESO concept reduces gate-to-runway transit time contributing to an 

overall reduction in gate-to-gate times. 

ASP-E3: SESO reduces engine-idling times and the need to decelerate and accelerate the 

aircraft so reduces fuel consumption on the ground. 

ASP-S1: SESO uses an automated system of surface CD&R with improved flight crew 

and controller situational awareness so may reduce the likelihood of LoS. 

ASP-S4: SESO has potential for hazardous events that are different from the current 

system - the consequences of these needs to be determined from a safety analysis. 

ASP-S5: SESO may reduce the likelihood of hazardous events by providing better 

situational awareness and using automation assisted CD&R on the ground. SESO has 

potential for hazardous events that are different from the current system - the likelihood 

of these needs to be determined from a safety analysis. 

Table 14—SESO system-level metrics 

RFA metric 

type/ no. 

RFA metric Related system-level 

metric 

Comments/ how calculated 

Efficiency/ 

SESO-E1 

Ratio of ASP-E1 with 

and without SESO. 

ASP-E1: The reduction in 

gate-to-gate aircraft transit 

time. 

Determined form ACES 

simulation. 

Efficiency/ 

SESO-E2 

Ratio of ASP-E3 with 

and without SESO. 

ASP-E3: The reduction in 

fuel usage of the aircraft 

fleet in terms of fuel 

consumed per aircraft 

nautical mile. 

Determined form ACES 

simulation. 

Safety/ 

SESO-S1 

Ratio of ASP-S1 with 

and without SESO. 

ASP-S1: The number of 

losses of separation with 

ground traffic. 

Determined from high-fidelity 

simulation. 
36 

RFA metric 

type/ no. 

RFA metric Related system-level 

metric 

Comments/ how calculated 

SESO-S2 Consequence of 

hazardous event. 

ASP-S4: Consequence of 

hazardous event. 

Based on FAA Order 8040.4 

Determined from safety 

analysis. 

Safety/ 

SESO-S3 

Ratio of ASP-S5 with 

and without SESO for 

hazardous events: 

o Collisions with 

aircraft and other 

vehicles on the 

ground. 

o Collisions with static 

objects. 

ASP-S5: Likelihood of 

hazardous event. 

Determined from high-fidelity 

simulation. 
37 

6.7 Airportal and Metroplex Integration (AMI) 

6.7.1 Overview 

The AMI RFA analyzes and integrates research across the NextGen-Airportal Project’s 

technical areas, as well as performing crosscutting research (e.g., human/system and 

metroplex operational concepts development) and portfolio management. AMI research 

consists of three elements: 

• Characterization of airspace interdependencies between airports within a metropolitan 

area, and methods for mitigating their effects 

• Connectivity within a Metroplex area—.i.e., how airports within a Metroplex area are 

connected (or not connected) and what type of connections are needed (including 

multi-modal transportation needs) 

• Connectivity within the NAS— i.e., how Metroplex areas are connected with other 

airports or Metroplexes in the NAS and how to improve the quality of the connections 

and recover from lost connections (e.g., when an airport is shut down due to bad 

weather) 

6.7.2 Key Functions, Features, Characteristics and Capabilities 

The AMI concept improves coordination of operations between different airports. 

6.7.3 Expected Performance Benefits 

Increased capacity during times when airports’ operations interact; increased capacity 

through greater use of secondary-level airports to offload excess demand from primarylevel airports; delay reduction due to more efficient use of facilities. 

6.7.4 Flight Phase 

The metroplex concept affects the departure/arrival phase.

6.7.5 Contribution to JPDO Top Level Goals and Objectives 

The Metroplex concept contributes to the following JPDO NextGen goals and objectives: 

• Goal 1: Retain U.S. leadership in global aviation. 

o Objective 1.2: Reduce costs for air transportation. 

• Goal 2: Expand capacity. 

o Objective 2.1: Satisfy future growth in demand (up to 3 times current levels) 

and operational diversity. 

o Objective 2.2: Reduce transit time and increase predictability (domestic curbto-curb transit time cut by 30%). 

o Objective 2.3: Minimize impact of weather and other disruptions 

• Goal 4: Protect the environment. 

o Objective 4.1: Reduce noise, emissions, and fuel consumption. 
38 

6.7.6 Impacts to ASP System-Level Metrics 

ASP-C1: The metroplex concept can accommodate increased demand within acceptable 

delay limits, by better coordination of flights through airspace shared by multiple 

airports, through better scheduling of flights into congested areas, and through offloading of flights from over-capacity airports. 

ASP-E1, ASP-E3: The metroplex concept can reduce delays through better coordination 

and use of constrained resources; this can decrease flight times and reduce fuel usage. 

ASP-R1: The metroplex concept can mitigate the loss in capacity that occurs due to poor 

weather and system disruptions by improving the connectivity of airports and speeding 

the recovery from lost connections. 

 ASP-R2: The metroplex concept can reduce delays due to poor weather by mitigating the 

effects of loss of airport capacity. 

ASP-R3: The metroplex concept can improve gate-to-gate time predictability through 

improved coordination. 

Table 15-–AMI system-level metrics 

RFA metric 

type/ no. 

RFA metric Related system-level 

metric 

Comments/ how calculated 

Capacity/ 

AMI-C1 

Ratio of ASP-C1 with 

and without AMI 

concepts. 

ASP-C1: The number of 

flights that can be operated 

on a good weather day 

within the delay limits 

specified in Section 4.1. 

Determined form ACES 

simulation. 

Efficiency/ 

AMI-E1 

Ratio of ASP-E1 with 

and without AMI 

concepts. 

ASP-E1: The reduction in 

gate-to-gate aircraft transit 

time 

Determined form ACES 

simulation. 

Efficiency/ 

AMI-E2 

Ratio of ASP-E3 with 

and without AMI 

concepts. 

ASP-E3: The reduction in 

fuel usage of the aircraft 

fleet in terms of fuel 

consumed per aircraft 

nautical mile. 

Determined form ACES 

simulation. 

Robustness/ 

AMI-R1 

Ratio of ASP-R1 with 

and without AMI 

concepts. 

The ratio of ASP capacity 

metrics for poor weather 

and/or system failure 

scenarios, within the delay 

limits specified in Section 

4.1 compared with the good 

weather, nominal operating 

conditions scenario. 

Determined form ACES 

simulation. 
39 

RFA metric 

type/ no. 

RFA metric Related system-level 

metric 

Comments/ how calculated 

Robustness/ 

AMI-R2 

Ratio of ASP-R2 with 

and without AMI 

concepts. 

The mean gate-to-gate 

delay for the good-weather 

number of flights, flown 

over the range of weather 

days expected in the course 

of a year. 

Determined form ACES 

simulation. 

Robustness/ 

AMI-R3 

Ratio of ASP-R3 with 

and without AMI 

concepts. 

The variance of gate-to-gate 

delay for the good-weather 

number of flights, for the 

poor weather and/or system 

failure scenarios, compared 

with the good weather, 

normal operating conditions 

scenario. 

Determined form ACES 

simulation. 

7 Summary and Conclusions 

This document contains an initial set of metrics for measuring National Airspace System 

capacity, efficiency, robustness, and safety at the system-level and at the Research Focus 

Area (RFA) level. The paper shows that the top-level RFA metrics link directly to the 

system-level metrics and to NextGen goals and objectives, as defined by the Joint 

Program Development Office. 

Planned updates to the document in 2011 and 2012 will complete the definition of RFA 

top-level metrics and will guide the work planned for integration design studies and 

system-level assessments. 
40 

Annex A: Example Values from Simulation Results 

This Annex presents example values for capacity metrics and shows how to calculate the 

metrics from TSAM data and ACES results. 

System-Level Capacity Metrics 

ASP-C1

The number of flights that can be operated on a good-weather day within the delay limits 

specified in Section 4.1. 

Method

1. Start with the baseline demand scenario (1X current traffic levels) and increase 

demand in e.g. 0.5X increments. 

2. Run ACES to determine delays. 

3. Plot the mean, 95th-percentile, 99th-percentile of system-wide delay at 1-hour 

increments, for commercial flights only and for all flights. 

4. Determine the maximum demand, beyond which delay exceeds any of the 

specified limits (mean =15 minutes, 95th = 30 minutes, 99th = 60 minutes) for 

either commercial or all flights. 

5. Graph the parameter (mean, 95th or 99th-percentile) that most exceeds the limit 

versus demand for the hour with maximum delay and interpolate to determine the 

system operating point. 

6. Determine the corresponding number of flights. 

 

Baseline NAS Capacity

For this example, delays are determined from ACES 6.0 simulation. 

Scenarios are the 26 September 2006 baseline good weather high volume day scenario 

and future scenarios derived from this baseline with increased demand 

The sector capacities are current day "CorrectedSectors_2007-07-24" as supplied with 

ACES. The airport capacity file is "Top250AirportCapacity_2004_BASELINE" based on 

current day values in the FAA 2004 Airport Capacity Benchmark report. 

Figure 1 depicts ACES delay results for all flights, i.e. commercial passenger, cargo and 

GA combined. For each hour of simulation, the mean delay is well below the specified 

limit. The overall mean value of delay is 111 seconds for all flights. The 99th and 95thpercentiles of delay are below the specified limits. 

Figure 2 depicts ACES delay results for commercial passenger flights only. For each hour 

of simulation, the mean delay is well below the specified limit, with the 95th and 99thpercentiles are below the limits. The overall mean value of delay is 165 seconds for 
41 

commercial passenger flights. The mean delay for commercial passenger flights is longer 

than for all flights combined because the mean value for all flights includes the delays for 

many GA flights that fly into smaller less congested airports. 

Figure 3 depicts ACES delay results for all flights using a scenario generated from 

TSAM demand projections, nominally for the year 2008. (Note baseline year scenario is 

2006 for the ASP). This scenario includes some consolidation of passengers into larger 

aircraft as demand increases. The scenarios documentation[2] describes the method used 

to generate future scenarios. 

For each hour of simulation, the mean delay is well below the specified limit. The overall 

mean value of delay is 165 seconds for all flights. The 95th-percentile of delay is also 

below the specified limits, but the 99th-percentile exceeds the 60-minute limit. 

Figure 4 depicts ACES delay results for commercial passenger flights for the year 2008 

scenario. For each hour of simulation, the mean delay is below the specified limit. The 

overall mean value of delay is 237 seconds for all flights. The 95th-percentile of delay is 

just above the 30-minute limit and the 99th-percentile significantly exceeds the 60-

minute limit. The delays for commercial flights are longer than for all flights combined, 

as expected. The 99th-percentile exceeds the limit by the greatest margin, so this 

parameter, for commercial flights, is the one that determines the system operating point 

beyond which delays become unacceptable in this example case. 

Figure 5 depicts the 99th-percentile of delay versus demand. The total number of flights 

in 24 hours for the 2006 scenario is 48,467 and for the 2008 scenario are 51,762 so the 

demand ratio is 1.07. Linear interpolation gives a demand ratio of 1.023 at the 60-minute 

delay limit. This corresponds to 49,581 flights. 

The analysis shows that the capacity of the NAS for metric ASP-C1, using the sector and 

airport capacity assumptions documented above is approximately 49,500 flights per day 

before delays reach the limits specified in Section 4.1. 
42 

0

20

40

60

80

100

120

0123456789

101112131415161718192021222324252627282930313233343536373839

Simulation Time (hours)

Delay (minutes) .

mean mean limit

95th percentile 95th percentile limit

99th percentile 99th percentile limit

time of interest (27 hours)

Total Flights in TOI = 49618

Mean Delay per Flight = 111 sec

Figure 1—26th Sept. 2006 Baseline Delays for All Flights 

0

20

40

60

80

100

120

0123456789

101112131415161718192021222324252627282930313233343536373839

Simulation Time (hours)

Delay (minutes) .

mean mean limit

95th percentile 95th percentile limit

99th percentile 99th percentile limit

time of interest (27 hours)

Total Flights in TOI = 29109

Mean Delay per Flight = 165 sec

Figure 2—26th Sept. 2006 Baseline Delays for Commercial Passenger Flights
43 

0

20

40

60

80

100

120

0123456789

101112131415161718192021222324252627282930313233343536373839

Simulation Time (hours)

Delay (minutes) .

mean mean limit

95th percentile 95th percentile limit

99th percentile 99th percentile limit

time of interest (27 hours)

Total Flights in TOI = 53152

Mean Delay per Flight = 165 sec

Demand Ratio = 1.07

Figure 3—2008 TSAM Scenario Delays for All Flights 

0

20

40

60

80

100

120

0123456789

101112131415161718192021222324252627282930313233343536373839

Simulation Time (hours)

Delay (minutes) .

mean mean limit

95th percentile 95th percentile limit

99th percentile 99th percentile limit

time of interest (27 hours)

Total Flights in TOI = 31706

Mean Delay per Flight = 237 sec

Demand Ratio = 1.09

Figure 4—2008 TSAM Scenario Delays for Commercial Passenger Flights 
44 

y = 1192.9x - 1159.8

0

20

40

60

80

100

120

140

1.00 1.01 1.02 1.03 1.04 1.05 1.06 1.07 1.08

Demand Ratio to 2006 Baseline

Minutes of Delay .

99th percentile 99th percentile limit

Figure 5—2008 TSAM Scenario 99th-percentile of Delay for Commercial Passenger Flights 

ASP-C2

The number of passenger origin-to-destination trips that can be operated on a goodweather day within the delay limits specified in Section 4.1. 

Method

1. Determine the number of flights that can be operated ASP-C1 

2. Determine the corresponding number of passenger origin-to-destination trips from 

TSAM results. 

The average number of origin-to-destination trips per day in 2006 is 1,317,094 and in 

2008 is 1,743,163 according to TSAM analysis[2]. Linear interpolation to the system 

operating point gives a corresponding passenger origin-to-destination capacity of 

1,457,270 trips. 

 

The analysis shows that the capacity of the NAS for metric ASP-C2, using the sector and 

airport capacity assumptions documented above is 1,457,270 origin-to-destination trips 

per day before delays reach the limits specified in Section 4.1. 

 
45 

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2

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46 

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