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TABLE OF CONTENTS 

1. EXECUTIVE SUMMARY ....................................................................................................1

2. INTRODUCTION ..................................................................................................................3

3. STUDY OBJECTIVES AND APPROACH .........................................................................4

4. BACKGROUND .....................................................................................................................5

4.1 NextGen Air Traffic Management (ATM) ..................................................................................... 6

4.2 Surveillance .................................................................................................................................... 7

4.3 Crew-Vehicle Interface (CVI) ........................................................................................................ 9

4.3.1 Potential CVI-relevant NextGen TMA Operational Changes ................................................... 9

4.3.2 NextGen CVI Equipage ........................................................................................................... 10

4.3.3 Potential CVI-Crew Performance Issues ................................................................................. 12

4.3.4 Potential impact of CVI and pilot performance on TMA CD&R safety levels ....................... 14

4.4 Other NextGen Equipage Changes ............................................................................................... 15

4.5 Ground Area Procedure Changes ................................................................................................. 15

4.6 Terminal Area Procedure Changes ............................................................................................... 17

4.6.1 RNAV and RNP Background .................................................................................................. 17

4.6.2 Precision Surveillance ............................................................................................................. 18

4.6.3 Parallel RNAV / RNP Approaches.......................................................................................... 18

5. ANALYSIS METHODS .......................................................................................................21

5.1 Modeling Baseline and Alternative NextGen Environments ....................................................... 21

5.1.1 Conflict Scenarios ................................................................................................................... 22

5.1.2 Model for Equipage Adoption ................................................................................................. 23

5.1.3 Aircraft Classes ....................................................................................................................... 23

5.1.4 Assets Used in Conflict Detection and Resolution .................................................................. 23

5.1.5 Encounter Rates between Aircraft Classes .............................................................................. 24

5.1.6 Selected Safety Metrics ........................................................................................................... 24

5.2 Analysis Framework .................................................................................................................... 25

5.3 Trades and Decisions for Modeling Analysis .............................................................................. 26

5.3.1 Response Time Model ............................................................................................................. 26

5.3.2 Surveillance Accuracy Models ................................................................................................ 28

5.3.3 Alerting Process Model ........................................................................................................... 28

6. MODEL RESULTS ..............................................................................................................30

6.1 Common Simulation Elements ..................................................................................................... 30

6.1.1 Trajectory Models ................................................................................................................... 30

6.1.2 Aircraft Extent ......................................................................................................................... 30

6.1.3 Measurements .......................................................................................................................... 32

6.1.4 Position and Velocity Estimation ............................................................................................ 32

6.1.5 Projected Quantities ................................................................................................................ 33
 ii 

6.2 Scenario 1: Taxi Crossing Arrival Runway.................................................................................. 33

6.2.1 Overview ................................................................................................................................. 33

6.2.2 Scenario Model........................................................................................................................ 34

6.2.3 Collision Risk With and Without an Alert .............................................................................. 38

6.2.4 Projected Times and Margins .................................................................................................. 40

6.2.5 Alerting Algorithm .................................................................................................................. 47

6.2.6 Nuisance Alert Rate ................................................................................................................. 51

6.2.7 Alert Effectiveness .................................................................................................................. 52

6.2.8 Comparison of NextGen Environments .................................................................................. 55

6.3 Scenario 2: Intersecting Arrivals .................................................................................................. 59

6.3.1 Overview ................................................................................................................................. 59

6.3.2 Scenario Model........................................................................................................................ 59

6.3.3 Collision Risk With and Without an Alert .............................................................................. 59

6.3.4 Projected Times and Margins .................................................................................................. 60

6.3.5 Alerting Algorithm .................................................................................................................. 60

6.3.6 Nuisance Alert Rate ................................................................................................................. 60

6.3.7 Alert Effectiveness .................................................................................................................. 60

6.3.8 Comparison of NextGen Environments .................................................................................. 62

6.4 Scenario 3: Taxi Following .......................................................................................................... 66

6.4.1 Overview ................................................................................................................................. 66

6.4.2 Scenario Model........................................................................................................................ 66

6.4.3 Collision Risk With and Without an Alert .............................................................................. 66

6.4.4 Projected Separation ................................................................................................................ 68

6.4.5 Alerting Algorithm .................................................................................................................. 68

6.4.6 Nuisance Alert Rate ................................................................................................................. 68

6.4.7 Alert Effectiveness .................................................................................................................. 69

6.4.8 Comparison of NextGen Environments .................................................................................. 71

6.5 Scenario 4: Arriver Following Lander ......................................................................................... 75

6.5.1 Overview ................................................................................................................................. 75

6.5.2 Scenario Model........................................................................................................................ 75

6.5.3 Collision Risk with and without an Alert ................................................................................ 75

6.5.4 Projected Times and Margins .................................................................................................. 77

6.5.5 Alerting Algorithm .................................................................................................................. 77

6.5.6 Nuisance Alert Rate ................................................................................................................. 77

6.5.7 Alert Effectiveness .................................................................................................................. 78

6.5.8 Comparison of NextGen Environments .................................................................................. 80

6.6 Scenario 5: RNAV / RNP Approach ............................................................................................ 83

6.6.1 Overview ................................................................................................................................. 83

6.6.2 Scenario Model........................................................................................................................ 85

6.6.3 Loss-of-Separation Risk with and without an Alert ................................................................ 85

6.6.4 Projected Times and Margins .................................................................................................. 85
 iii 

6.6.5 Alerting Algorithm .................................................................................................................. 85

6.6.6 Nuisance Alert Rate ................................................................................................................. 87

6.6.7 Alert Effectiveness .................................................................................................................. 87

6.6.8 Comparison of NextGen Environments .................................................................................. 90

7. CONCLUSIONS AND RECOMMENDATIONS .............................................................91

7.1 Relation of Safety Modeling Results to CVI ................................................................................ 93

8. REFERENCES .....................................................................................................................95

9. ACRONYMS AND ABBREVIATIONS .............................................................................97
 iv 

TABLE OF FIGURES 

Figure 1. Potential NextGen Pilot/Human Factors Issues ........................................................................... 12

Figure 2. Aircraft with Different RNP Capability Approach Runway on Different Paths ......................... 19

Figure 3. Spacing Geometry Details for RNP-governed Parallel RNAV Approaches ............................... 20

Figure 4. Analysis Framework for Alerting Capability Model ................................................................... 26

Figure 5. Notional Response Time Distributions ........................................................................................ 27

Figure 6. Model for Aircraft Extent ............................................................................................................ 31

Figure 7. Definition of Collision Potential .................................................................................................. 31

Figure 8. Taxi Crossing Arrival Runway Scenario ..................................................................................... 34

Figure 9. Measurements of Taxier’s Position (top) and Speed (bottom) as a Function of Time for a Single 

Encounter .................................................................................................................................................... 35

Figure 10. Arriver Trajectory Model .......................................................................................................... 36

Figure 11. Measurements of Arriver’s Position (top) and Estimates of its Speed (bottom) as a Function of 

Time for a Single Encounter ....................................................................................................................... 37

Figure 12. Definition of Safety Distance Margin........................................................................................ 38

Figure 13. Collision Risk as a Function of Safety Distance Margin, Capturing the Transition from Zero 

Collision Risk to Near-certainty of Collision ............................................................................................. 39

Figure 14. Value of Alert Ordering a Go-around, as a Function of the Time when Pilot Reaction Begins 40

Figure 15a. Projected Times of Taxier Clearing Intersection, and Arriver Crossing Threshold, as a 

Function of the Time of Estimation ............................................................................................................ 42

Figure 15b. Projected Times of Taxier Entering and Exiting Intersection, as a Function of the Time of 

Estimation ................................................................................................................................................... 43

Figure 15c. Projected Times of Arriver Entering and Exiting Intersection, as a Function of the Time of 

Estimation ................................................................................................................................................... 44

Figure 16. Rules Margin Estimates as a Function of Estimation Time ....................................................... 45

Figure 17. Safety Margin Estimates as a Function of Estimation Time ..................................................... 46

Figure 18a. Alert Quantities, Thresholds, and Values for an Example Encounter ..................................... 48

Figure 18b. Alert Quantities, Thresholds, and Values for an Example Encounter ..................................... 49

Figure 18c. Alert Quantities, Thresholds, and Values for an Example Encounter ..................................... 50

Figure 18d. Alert Quantities, Thresholds, and Values for an Example Encounter ..................................... 51

Figure 19. Rules Margin (left) and Safety Margin (right) Distributions for Ensemble used to Evaluate 

Nuisance Alert Rate .................................................................................................................................... 52

Figure 20. Collision Risk as a Function of Time when Taxier Crosses Entry Hold Line ........................... 53

Figure 21. Collision Risk as a Function of Distance from Threshold to Intersection ................................. 53

Figure 22. Relative Collision Rates in Four NextGen Environments, Relative to the Baseline (NextGen1) 

for Taxi Crossing Runway Scenario ........................................................................................................... 58

Figure 23. Intersecting Arrivals Scenario ................................................................................................... 59
 v 

Figure 24. Collision Risk as a Function of Safety Margin for Intersecting Arrivals Scenario ................... 61

Figure 25. Relative Collision Rates in Four NextGen Environments, Relative to the Baseline (NextGen1) 

for Intersecting Arrivals Scenario ............................................................................................................... 65

Figure 26. Taxi Following Scenario ........................................................................................................... 66

Figure 27. Position and Speed of Aircraft in Taxi Following Scenario ...................................................... 67

Figure 28. Projected Minimum Separation as a Function of Time ............................................................. 68

Figure 29. Collision Risk vs. Speed Difference for Taxi Following Scenario ............................................ 69

Figure 30. Collision Risk vs. Braking Initiation Distance for Taxi Following Scenario ............................ 70

Figure 31. Collision Risk vs. Follower Response Time for Taxi Following Scenario ............................... 70

Figure 32. Relative Collision Rates in Four NextGen Environments Relative to the Baseline (NextGen1) 

for Taxi Following Scenario ....................................................................................................................... 74

Figure 33. Arriver Following Lander Scenario ........................................................................................... 75

Figure 34. Position (Top) and Speed (Bottom) of the Two Aircraft in the Arriver Following Lander 

Scenario ...................................................................................................................................................... 76

Figure 35. Collision Risk as a Function of Distance to First Intersection (left) and Distance between 

Intersections (right) for Arriver Following Lander Scenario ...................................................................... 78

Figure 36. Relative Collision Rates in Four NextGen Environments, Relative to the Baseline (NextGen1) 

for Arrival Following Lander Scenario ....................................................................................................... 82

Figure 37. RNAV / RNP Approach Scenario ............................................................................................. 84

Figure 38. Position (Top) and Speed (Bottom) of the Two Aircraft in the RNAV / RNP Approach 

Scenario ...................................................................................................................................................... 86

Figure 39. Projected Time Margin at Merge Point, as a Function of Time when Projection Occurred ..... 87

Figure 40. Risk of Loss of Separation as a Function of the Time Margin between the Two Aircraft at the 

Merge Point ................................................................................................................................................. 88

Figure 41. Merge Time Margin Distribution in Blunder Ensemble ............................................................ 88

Figure 42. Relative Collision Rates in NextGen 1 & 4 Environments, Relative to the Baseline (NextGen1) 

for RNAV / RNP Approach Scenario ......................................................................................................... 90
 vi 

TABLE OF TABLES 

Table 1. Range of GPS Accuracy Expected in NextGen Environment ........................................................ 9

Table 2. Summary of Alternative NextGen Environments ......................................................................... 22

Table 3. Distribution of RT’s per the Alarm Initiated Activity Model (Stanton, 2006) ............................. 26

Table 4. Assignment of Response Time Curves to Conflict Scenario Types ............................................. 27

Table 5. Fraction of Collisions Averted by Alerting as a Function of Response time ................................ 54

Table 6. Fraction of Collisions Averted by Alerting as a Function of Position Measurement Precision ... 54

Table 7. Fraction of Collisions Averted by Alerting with and without ADS-B Velocity Measurement .... 55

Table 8. Situational Awareness Benefits as a Function of Aircraft Class and NextGen Environment ....... 55

Table 9. Alerts Available as a Function of Aircraft Class and NextGen Environment .............................. 55

Table 10. Relative Collision Risk in NextGen1 Environment for Taxi Crossing Runway Scenario .......... 56

Table 11. Relative Collision Risk in NextGen2 Environment for Taxi Crossing Runway Scenario .......... 57

Table 12. Relative Collision Risk in NextGen3 Environment for Taxi Crossing Runway Scenario .......... 57

Table 13. Relative Collision Risk in NextGen4 Environment for Taxi Crossing Runway Scenario .......... 58

Table 14. Collision Rate Relative to Baseline in Each NextGen Environment as a Function of Fleet 

Composition for Runway Crossing Scenario .............................................................................................. 59

Table 15. Fraction of Collisions Averted by Alerting as a Function of Response time .............................. 61

Table 16. Fraction of Collisions Averted by Alerting as a Function of Position Measurement Precision . 62

Table 17. Fraction of Collisions Averted by Alerting with and without ADS-B Velocity Measurement .. 62

Table 18. Relative Collision Risk in NextGen1 Environment for Intersecting Arrivals Scenario ............. 63

Table 19. Relative Collision Risk in NextGen2 Environment for Intersecting Arrivals Scenario ............. 63

Table 20. Relative Collision Risk in NextGen3 Environment for Intersecting Arrivals Scenario ............. 64

Table 21. Relative Collision Risk in NextGen4 Environment for Intersecting Arrivals Scenario ............. 64

Table 22. Collision Rate Relative to Baseline in Each NextGen Environment as a Function of Fleet 

Composition for Intersecting Arrivals Scenario ......................................................................................... 65

Table 23. Fraction of Collisions Averted by Alerting as a Function of Response time .............................. 71

Table 24. Fraction of Collisions Averted by Alerting as a Function of Position Measurement Precision . 71

Table 25. Fraction of Collisions Averted by Alerting with and without ADS-B Velocity Measurement .. 71

Table 26. Relative Collision Risk in NextGen1 Environment for Taxi Following Scenario ...................... 72

Table 27. Relative Collision Risk in NextGen2 Environment for Taxi Following Scenario ...................... 72

Table 28. Relative Collision Risk in NextGen3 Environment for Taxi Following Scenario ...................... 73

Table 29. Relative Collision Risk in NextGen4 Environment for Taxi Following Scenario ...................... 73

Table 30. Collision Rate Relative to Baseline in Each NextGen Environment as a Function of Fleet 

Composition for Taxi Following Scenario .................................................................................................. 74

Table 31. Fraction of Collisions Averted by Alerting as a Function of Response time .............................. 79
 vii 

Table 32. Fraction of Collisions Averted by Alerting as a Function of Position Measurement Precision . 79

Table 33. Fraction of Collisions Averted by Alerting with and without ADS-B Velocity Measurement .. 80

Table 34. Relative Collision Risk in NextGen1 Environment for Arrival Following Lander Scenario ..... 80

Table 35. Relative Collision Risk in NextGen2 Environment for Arrival Following Lander Scenario ..... 81

Table 36. Relative Collision Risk in NextGen3 Environment for Arrival Following Lander Scenario ..... 81

Table 37. Relative Collision Risk in NextGen4 Environment for Arrival Following Lander Scenario ..... 82

Table 38. Collision Rate Relative to Baseline in Each NextGen Environment as a Function of Fleet 

Composition for Intersecting Arrivals Scenario ......................................................................................... 83

Table 39. Fraction of Blunder Encounters Corrected by Alerting as a Function of Response time ........... 89

Table 40. Fraction of Blunder Encounters Corrected by Alerting as a Function of Position Measurement 

Precision ...................................................................................................................................................... 89

Table 41. Fraction of Blunder Encounters Corrected by Alerting With and Without ADS-B Velocity 

Measurement ............................................................................................................................................... 89

Table 42. Fraction of Blunder Encounters Corrected by Alerting as a Function of Track Deviation 

Uncertainty .................................................................................................................................................. 89

Table 43. Fraction of Blunder Encounters Corrected by Alerting as a Function of Maximum Relative 

Speed Adjustment ....................................................................................................................................... 90

Table 44. Fraction of Blunder Encounters Corrected by Alerting as a Function of Time When Conflict 

Checking Begins ......................................................................................................................................... 90
 1 

1. Executive Summary 

Emerging Next Generation Air Transportation System (NextGen) operational concepts represent a 

radically different approach to air traffic management and as a result, a dramatic shift in the tasks, roles, 

and responsibilities for the flight crew to ensure a safe, sustainable air transportation system. One of the 

key challenges associated with the NextGen involves automated surface management systems that utilize 

dynamic algorithms to calculate the most efficient movement of all surface traffic to increase efficiency. 

Fortunately, advanced surveillance, flight management, communication, navigation, and crew-vehicle 

interface equipage holds the promise of enabling more efficient and safer surface operations. Yet 

increases in pilot roles and responsibilities, higher information load, quicker paced operations, smaller 

safety margins, and more complex operations could lead to erosion of the proactive safety layers that 

enable present-day operations. It will be vital to retain and even improve upon these proactive safety 

layers for NextGen. 

Limited research to date has been conducted on concepts and issues associated with conflict detection and 

resolution (CD&R) in a NextGen environment, particularly in the Terminal Maneuvering Area (TMA) 

and on the surface. Further, 4-dimensional (4-D) terminal area operational concepts proposed for 

NextGen are being built upon present-day operations that are not free of risk. Consequently, the goal of 

this work was to analytically identify and quantify the issues, challenges, technical hurdles, and pilotvehicle interface issues associated with CD&R in emerging operational concepts for NextGen TMA, 

including surface operations. In order to assess the overall effectiveness of CD&R in a future 

environment, the study addressed many different factors at a high level, with the goal of creating a 

credible performance analysis framework and identifying potential issues for further research. 

More specifically, the following objectives were addressed: 

Develop first-order modeling techniques to quantitatively address the broadest possible set of factors 

affecting the achievable safety benefits in future NextGen environments. 

Apply these models to estimate the overall effectiveness of NextGen TMA CD&R as a function of 

critical parameters using different combinations of: 

– Representative conflict scenarios for low-altitude, runway, and taxiway conflicts, and 

– Alternative models of end-state NextGen equipage and ground assets. 

Identify Crew Vehicle Interface (CVI) technologies, hurdles, challenges & research and development 

(R&D) needs. 

To this end, the work entailed analytical and trade studies focused on modeling the achievable safety 

benefits of different CD&R strategies and concepts in the current and future airport environment. The 

initial activities defined equipage baselines and assumptions in terms of performance characteristics and 

capabilities of relevant surveillance and alerting systems such as Automatic Dependent Surveillance – 

Broadcast (ADS-B) and Airport Surface Detection Equipment – Model X (ASDE-X). Finally, 

assumptions were made concerning NextGen terminal area procedures, CVI and pilot alerting concepts, 

pilot performance, and overall CD&R requirements and constraints. NextGen documentation provides a 

vision for a variety of operational, aircraft, and ground infrastructure changes that could potentially 

increase capacity and safety. Yet it is uncertain which of these envisioned changes will be realized and 

how quickly. In order to specify a variety of values for the modeling analyses performed here, many 

operational assumptions and performance predictions had to be made. Some of these were simply agreed 

upon based on the expertise of the National Aeronautics and Space Administration (NASA), Honeywell, 

and Sensis team members, while others were derived from review and analysis of data and previous 

documentation. Many assumptions and performance predictions were ultimately simplified for 

practicality in order to make a broad modeling analysis possible. 
 2 

These assumptions were used as the framework for identifying a small set of alternative NextGen 

environments that reflected end-states based on different CD&R strategies and concepts. Based on this 

framework, CD&R parametric models were developed with a limited set of representative major equipage 

configurations and surveillance quality values. Five conflict scenarios were developed along with metrics 

for the safety impact of the assumed technologies in order to exercise the models and produce preliminary 

safety benefit predictions. 

In addition, CVI and pilot performance enhancements and potential issues were analyzed based on review 

of envisioned NextGen operations, expected equipage advances, and human factors expertise. These 

analyses led to development of a simple pilot response time model used in the parametric modeling 

analysis. However, significant uncertainty in pilot performance issues in a NextGen environment make it 

likely that the response time model used was an over-simplification and many of the pilot performance 

issues and degree of benefit of CVI advances will need to be explored empirically. 

The major findings of the study are listed below. 

Developing a quantitative analytical model for overall CD&R effectiveness in a future environment that 

addresses variations in surveillance quality, CD&R equipage, encounter parameters, and flight crew 

response time is challenging, but feasible. 

The assumption that CD&R performance is limited primarily by kinematic state estimation and 

prediction, rather than correctly classifying the encounter, is important to the study conclusions. This 

assumption can be assured by the provision of appropriate air-ground data links for information on critical 

operational transitions, particularly intended runway, acceleration, and turns. 

CD&R effectiveness should be modeled against a representative ensemble of encounters, rather than a 

small number of point scenarios, because the chance of successful response may depend critically on the 

time at which a mistake is made. Systematic or random (Monte Carlo) variation of the relative timing of 

encounters is essential to assess effectiveness. 

The sometimes-critical dependence of the conflict encounter outcome on flight crew response time 

demonstrates the need to consider the combined effect of situation awareness (SA), workload, indications, 

and multi-level alerts on outcomes, and to validate the response time models assumed for the study. 

The size of the margin between an acceptable operation and an encounter with collision risk is critical to 

the ability to achieve an acceptable nuisance alert rate while maintaining safety. Low nuisance alert rates 

are achievable under the assumption that occasional alerts for procedural violations of those safety 

margins are acceptable (i.e., not considered nuisance alerts). If operational safety margins are not much 

larger than the uncertainty in the CD&R prediction, either compromised detection or high nuisance alerts 

are inevitable. Therefore, if new NextGen procedures assume reductions in these margins, the CD&R 

capabilities assumed might not be achievable. 

Global Positioning System (GPS)-derived ADS-B velocity should be used in CD&R systems if it is as 

accurate as promised, but will only change alert effectiveness significantly where the margin between 

acceptable and unsafe behavior is relatively small and dependent on velocity measurement. In particular, 

it was found that using this measurement improved safety in the overtaking-taxi scenario, but not in the 

runway crossing or intersecting arrivals scenarios. 

The combination of object size uncertainty and residual error in ADS-B position in a multipath 

environment limits the ability to detect low-speed and static conflict situations (such as stationary targets 

slightly over the hold line). However, if reasonable position margins are maintained, the collision risk 

from undetected violations of this type is small. 
 3 

Often the largest contribution to the uncertainty in predictions of future state is the assumed behavior of 

an aircraft after an operational transition (for example, how hard an arriver will brake after landing). 

When these uncertainties dominate, improvements in surveillance precision offer no benefit. To the 

extent that future behavior can be predicted more precisely, the alerting system may become more 

effective. 

The example results of perturbation analysis, which quantified the high-level performance impact of 

changes to key parameters such as median response time and surveillance position error, showed that the 

analytical model developed could be useful in making technology investment decisions. 

Based on the simple models used for the benefits of passive situation awareness (provided by a Cockpit 

Display of Traffic Information (CDTI)), there was a substantial difference in overall safety capability 

between assumed NextGen environments. Under these models, investing in either comprehensive aircraftbased alerting or a ground-based alerting system would reduce the collision risk by multiple orders of 

magnitude compared to NextGen relying primarily on situation awareness. The degree to which initial 

lapses will be prevented by improved situation awareness is unknown; this factor, combined with 

increased demands in the NextGen environment, will determine whether the assumed NextGen baseline is 

significantly safer than current operations. 

The assumed situational awareness benefit of cockpit displays such as CDTI strongly impacted the 

difference in safety between NextGen environments. Further research is needed to measure the extent to 

which these technologies help pilots to avoid or recover from lapses. 

In scenarios requiring a prompt evasive maneuver to resolve a conflict, ground-based alerting was less 

effective due to the latency in communication between the aircraft and the alerting system. However, if a 

significant fraction of aircraft does not equip an on-board system, a ground-based system that issues alerts 

to all aircraft may offer adequate safety. Also, in some scenarios, the ground-based system may be able 

to coordinate alerts and resolution advisories among multiple aircraft to a degree that on-board systems 

cannot. 

The modeled safety benefits of CD&R depend on the ability to develop crew-vehicle interfaces that 

facilitate quick execution of the appropriate reaction, matching or exceeding the assumed response time 

distributions under realistic conditions. Integrating the capability for alerts with displays and indications 

that direct attention to the significant threats before they become acute should be a primary research 

focus. 

Among the scenarios examined, performance depended most strongly on the level of equipage for the 

overtaking-taxi scenario, because taxiway CD&R was assumed to be provided only by high-end on-board 

or ground-based alerting systems. For runway collision hazards, partial equipage offered adequate 

benefits because runway alerting was provided at a lower level of equipage. 

2. Introduction 

By 2025, United States (U.S.) air traffic is predicted to increase 3-fold, yet the current air traffic system 

may not be able to accommodate this growth. In response to this challenge, a consortium of industry, 

academia and government agencies has proposed a revolutionary new concept for U.S. aviation 

operations, termed the Next Generation Air Transportation System or “NextGen” (e.g., FAA, 2008; FAA, 

2009 (a); JPDO, 2007; JPDO, 2008; NextGen Mid-Term Implementation Task Force, 2009). Emerging 

NextGen operational concepts represent a radically different approach to air traffic management and as a 

result, a dramatic shift in the tasks, roles, and responsibilities for the flight crew to ensure a safe, 

sustainable air transportation system. 

One of the key challenges associated with the NextGen involves automated surface management systems 

that utilize dynamic algorithms to calculate the most efficient movement of all surface traffic to increase 
 4 

efficiency. Pilots will be required to comply with 4-D taxi clearances, dictating that aircraft arrive at 

specific locations within specific time windows. Further, pilots may be responsible for separation from 

other aircraft during these operations regardless of visibility conditions. 

Proactive safety layers enable present-day operations. These layers include automation to manage, assist, 

and even conduct these procedures and operations. In today’s operations, a significant layer of CD&R 

capability is provided by the Traffic Alert and Collision Avoidance System (TCAS). TCAS has been 

developed and improved for over 15 years and has been very effective in reducing or eliminating airborne 

collisions. 

NextGen operational concepts are beginning to emerge to meet the projected air traffic service demands. 

It will be vital to retain and even improve upon these proactive safety layers for NextGen. Limited 

research to date has been conducted on concepts and issues associated with CD&R in a NextGen 

environment, particularly in the TMA and on the surface. 4-D terminal area operations concepts 

proposed for NextGen are being built upon present-day operations that are already problematic. In the 

four year period between 2005 and 2008, 3,496 runway incursion events were reported, which is a rate of 

more than two runway incursion events per day (FAA, 2009 (b)). The present-day statistics and events 

are cause enough for alarm but, without proactive counter-measures, the increase in air traffic forecasted 

under NextGen and using novel 4-D operating concepts could potentially result in catastrophic increases 

in runway and taxiway incursion accidents. 

Consequently, the goal of this work was to analytically identify and quantify the issues, challenges, 

technical hurdles, and pilot-vehicle interface issues associated with CD&R in emerging operational 

concepts for NextGen TMA, including surface operations. This was a high level, “first pass” analysis 

intended primarily to identify potential issues for further research. 

3. Study Objectives and Approach 

The overall goal of this work was to analytically identify and quantify safety issues associated with 

CD&R in emerging operational concepts for NextGen TMAs. More specifically, the following objectives 

were addressed: 

Analyze the projected safety level for NextGen TMA CD&R with: 

Alternative NextGen TMA CD&R scenarios. 

Different surveillance equipage and their associated qualities, including ADS-B 

equipage, and different flight deck alerting assumptions. 

Identify CVI technologies, hurdles, challenges & R&D needs. 

To this end, the work entailed analytical and trade studies focused on modeling the achievable safety 

benefits of different CD&R strategies and concepts in the current and future airport environment. The 

initial activities defined equipage baselines and assumptions in terms of performance characteristics and 

capabilities of relevant surveillance and alerting systems such as TCAS, ADS-B, and ASDE-X. Further, 

future avionics and relevant airborne and ground system performance and capabilities were identified 

based on assumptions concerning likely extensions to current baselines. Finally, assumptions were made 

concerning NextGen terminal area procedures, CVI and flight deck alerting concepts, pilot performance, 

and overall CD&R requirements and constraints. 

These equipage baselines, predicted extensions of capabilities, and operational environment assumptions 

were used as the framework for analytical trade studies aimed at identifying a small set of alternative 

NextGen environments that reflected end-states based on different CD&R strategies and concepts. Based 

on the trades, iterated assumptions, and system performance predictions, CD&R parametric models were 

developed with a limited set of representative major equipage configurations and surveillance quality 

values. Five conflict scenarios were developed along with metrics for the safety impact of the assumed 
 5 

technologies in order to exercise the models and produce preliminary safety benefit predictions. The 

scope of the analysis was limited in the following ways to keep it within programmatic constraints: 

The primary safety metric was the fraction of potential hazards where the assumed CD&R 

systems would be effective in preventing collision, rather than an absolute accident rate. 

[Assuming an error that could result in a collision occurs, the probability of automatically 

generating an alert from any of the assumed CD&R systems in time to successfully initiate 

corrective action was estimated under the assumption that no other corrective action was 

taken. This approach avoided the complexity of inferring the rates of errors and corrective 

actions in the current environment and extrapolating those rates to a future environment that 

is not fully defined]. 

Metrics were applied to “end-state” NextGen environments, not transitional partial equipage 

environments, for easier technology comparison. 

CD&R functions were considered as an independent system for safety assurance, and, 

therefore, independent of the technology implemented for Equivalent Visual Operations 

(EVO) and Trajectory Based Operations (TBO). 

Only algorithms essential to predicting the performance limits of future CD&R, rather than a complete 

CD&R function were modeled. 

The tasks that comprised this effort are summarized below. 

Task 1. Characterize the set of airport operations in the assumed NextGen environment and identify the 

baseline systems that would provide safety assurance. The focus was on parametric changes to 

runway operations (e.g., faster, denser traffic) rather than hypothesizing specific NextGenpossible operational procedures. 

Task 2. Define analysis assumptions, metrics, and scenarios. Metrics focused on feasibility of effective 

alerting, rather than a predicted accident rate requiring guesswork on underlying human error 

rates by pilots or controllers. 

Task 3. Conduct preliminary trade studies of alternative CD&R strategies, methodologies and equipage 

and select alternatives to the baseline. For each strategy, a mature equipage end state was 

assumed with uniform adoption within each aircraft class of the equipage needed to support the 

CD&R strategy. The trade studies focused on the capability of the assumed CD&R technology to 

prevent collisions, rather than creating a rationale for the assumed environment. [The eventual 

NextGen environment will depend on a combination of economic, safety, and policy factors that 

are beyond the scope of the current study.] 

Task 4. Develop parametric models to evaluate the effect on safety of the interaction of assumed CD&Rrelevant equipage, communication, surveillance, alerting and display systems, and pilot 

performance. Parametric models focused on the coupled problems of defining rational alert 

criteria and estimating achievable sensitivity. Estimation of time clearance was the central 

problem, and outcomes were based on simple response time models for human performance. 

Task 5. Conduct parametric analyses. The models addressed representative airport environments rather 

than the entire range of possible scenarios. Techniques for scaling the Monte Carlo analyses to 

feasible sizes were developed after determining sensitivities in parametric model studies. 

4. Background 

In this section, background information is presented on the architecture, technology, and procedures that 

affect the future NextGen environment. The specific, simplified models used for the study are presented 

in Section 5. 
 6 

4.1 NextGen Air Traffic Management (ATM) 

NextGen documentation provides a vision for a variety of operational, aircraft, and ground infrastructure 

changes that could potentially increase capacity and safety (e.g. see JPDO, 2007). Yet it is uncertain 

which of these envisioned changes will be realized and how quickly. In order to specify a variety of 

values for the modeling analyses performed here, many assumptions and performance predictions were 

made. Some of these were simply agreed to be based on the expertise of NASA, Honeywell, and Sensis 

team members, while others were derived from review and analysis of data and previous documentation 

(referred to here as the “trade studies”). Some of the key assumptions used here, which either directly or 

indirectly affected the modeling input, are listed below. Many assumptions and performance predictions 

were ultimately simplified for practicality in order to make a broad modeling analysis possible. 

Key Assumptions 

General assumptions were: 

Procedures for precise TBO will enable reduced separations (especially for approaches to 

parallel runways) for properly equipped aircraft and airports. 

Role of CD&R equipage, in conjunction with new operational procedures, will be to provide 

an underlying safety layer in the TMA for such operations in zero or low visibility (with pilot 

visual detection of conflicts secondary). 

“Eyes out” will still be required for ground operation (either use of HUD/HMD or one pilot 

head down, one head up). 

Incentives for new equipage will increase the pace of introduction relative to historical 

norms. 

Ground-based surveillance services will be widely available. 

Ground stations for Automatic Dependent Surveillance- Rebroadcast (ADS-R) will be 

provided to support the dual-link concept. Under the dual-link concept, some aircraft transmit 

and receive ADS-B on one physical link (the 1090 MHz Extended Squitter or 1090ES 

protocol) while other aircraft (expected to be the typical General Aviation user) transmit and 

receive using a different physical link (Universal Access Transceiver or UAT, expected to 

use 978 MHz). The ground stations broadcast ADS-R on the appropriate link to provide 

mutual awareness between differently-equipped National Airspace System (NAS) users. 

ASDE-X or another ground surveillance system such as Low-Cost Ground Surveillance 

(LCGS) will provide Traffic Information Services-Broadcast (TIS-B) services for non-ADSB equipped traffic at some airports. 

Environment will include mix of aircraft/equipage capabilities: 

– Retrofit costs make complete upgrade unlikely. 

– Some aircraft may use audio-only situation awareness (SA) and alerting. 

EVO will be manifested as sustained operations at peak capacity. 

Pilots will retain final authority for conflict resolution (no automated maneuvers). 

Simplifying assumptions for CD&R-relevant equipage levels were: 

Limit aircraft classes to 3 major classes: 

– New transport aircraft. 

– Retrofitted transport aircraft. 

– Non-transport. 

Limit combinations of surveillance to: 
 7 

– TCAS or successor (potentially fusing ADS-B). 

– ADS-B on-board: Minimum capability of Out or Out+In assumed. 

– Ground surveillance: Either ASDE-X (fusing Multilateration (MLAT), Surface 

Movement Radar (SMR), and other sensors) or none. 

Crew interface for CD&R: moving map with ownship or CDTI with traffic. 

Limit conflict detection capability alternatives according to source: 

– On-board: non-traffic (risks unrelated to traffic, such as wrong runway or taxi route), 

basic runway (common high-speed runway collision risks), or comprehensive (collision 

risks in terminal airspace and surface movement area). 

– Ground uplink: comprehensive. 

4.2 Surveillance 

For the purpose of evaluating CD&R, surveillance is the set of all sources of real-time information on the 

identity, position, velocity, and other dynamic characteristics of objects in the terminal area. Therefore, 

processing to fuse data from multiple surveillance systems or to pre-process the data for better accuracy 

effectively becomes part of the surveillance input to CD&R. In the interests of keeping the study results 

general, the models characterized the accuracy of the surveillance data without specifying the source of 

the measurement. The results of research into the anticipated accuracy of NextGen surveillance are given 

below. 

Under NextGen, ADS-B “Out” is expected to be provided by aircraft in the terminal areas of towered 

airports. Over the next decade, commercial aircraft are expected to equip with ADS-B “In” and “Out” in 

increasing numbers, supporting enhanced operating capabilities including merging and spacing, CDTI, 

TCAS, and other functions. Additionally, some aircraft will have Head-Up Displays (HUD) using 

Enhanced Vision Systems (EVS) with infrared or millimeter wave radars to allow operations in low 

visibility conditions. New landing systems such as the Global Navigation Satellite System Landing 

System (GNSS) and ground surveillance such as ASDE-X will also be widely available. However, 

aircraft will equip at different rates, and aircraft and airports with advanced surveillance capabilities will 

have to handle potential conflicts with less-equipped aircraft. 

The ASDE-X system is a ground surveillance system providing accurate position, velocity, and 

identification (via transponder code) of all aircraft. ASDE-X also provides conflict detection for a 

specified set of runway scenarios. ASDE-X fused track reports are based on the combination of all 

sources of information on an object, typically including both SMR primary radar reports and 

multilateration reports on Mode-S or Air Traffic Control Radar Beacon (ATCRB) System transponder 

replies. ASDE-X also receives ADS-B transmissions from equipped aircraft, which are becoming more 

common each year but still are fewer than 10% of all flights at most U.S. airports. (Equipage decisions by 

individual carriers can rapidly change that number for particular airports.) ASDE-X is scheduled for 

deployment at the 35 largest U.S. airports. The LCGS system is an alternative to ASDE-X and is 

currently in prototype development. LCGS is expected to be deployed at smaller airports with significant 

need to manage surface traffic. 

The precision of surveillance and positioning equipage is critical to the overall modeling results of CD&R 

safety levels. Some of the key assumptions about surveillance and positioning accuracy1

 are described 

below. 

1

 The performance models for GPS receivers are based on consultation with Brian Schipper, a subject matter expert 

at Honeywell [personal communication.] 
 8 

The types of errors for GPS receivers fall into four main categories: 

– Bias (e.g., constellation position or differential-GPS (DGPS) ground station survey 

errors), 

– White noise (e.g., measurement noise), 

– Lightly-damped Gauss-Markov noise (e.g., signal multipath), and 

– Heavily-damped Gauss-Markov noise (e.g., atmospheric conditions interference). 

The assumed variance for each GPS error source depends on: 

– The types of receiver, as higher quality receivers generally have less noise and other 

errors; for example, a receiver may use narrower correlation windows to reduce 

multipath. 

– Environment of operation (multipath is generally worse on the ground than in the air). 

– Availability and use of DGPS, as DGPS eliminates bias but may increase multipath 

susceptibility. 

Special considerations for GPS error modeling include: 

– The multipath period may be as long as 100 sec for static ground conditions, decreasing 

as trajectory dynamics increase. 

– A dual frequency receiver can eliminate ionospheric error contribution. 

– DGPS eliminates all atmospheric errors for receivers within approximately 10 km of the 

base station but its effectiveness is reduced during strong ionospheric scintillation. 

ADS-B surveillance will focus on position accuracy: 

– Reported accuracy category (Navigation Accuracy Category (NAC)-7, -8, ...). 

– Distinguish reported from internal accuracy (ownship knows internal). 

– Address important departures from simple white-noise Gaussian error models, including 

e.g., time-correlated errors and position outliers. 

A parametric model of GPS accuracy in the NextGen environment, assumed to represent the best case 

accuracy for ownship position and velocity, was developed with variants for each major category of 

aircraft assumed for the NextGen environment. The range of expected GPS accuracy is presented in Table 

1 and the basis for aircraft categorization is discussed further in Section 5.1.3. 

Models were developed for ground surveillance accuracy as well, based on the ASDE-X system 

capability. Ground surveillance accuracy was modeled to establish a baseline for ATC conflict detection 

and resolution capability without new equipage and also to characterize the quality of information 

potentially available (directly or as source of fused surveillance) to the flight deck via TIS-B and ADS-R). 

The ASDE-X internal system position accuracy is based on the smoothed output of fusion tracker which 

processes contributions from multilateration, surface radar, Airport Surveillance Radar (ASR), and ADSB from equipped aircraft. The typical multilateration position specified accuracy (transponder-centered) is 

6 m (1-sigma), but anomalies such as multipath may occur. Position accuracy for a surface movement 

radar measurement is computed based on the centroid of the object, which is usually over-resolved 

(appearing in multiple range-azimuth cells.) 

The ability to convey position report accuracy is to some extent limited by the communications format. It 

has been assumed for this study that ground-based fusion tracker position estimates used for ADS-R will 

likely conform to the DO-260A standard (RTCA, 2003). It is possible that in the future, services such as 

TIS-B could use a different format with better precision for reporting accuracy (as opposed to the 

quantization incurred in the DO-260A format by using NAC values as conservative accuracy bounds). 
 9 

For this study, the conflict detection performance model was based on single-update report accuracy (e.g., 

position or position/velocity) augmented with a simple model for achievable predicted future state 

accuracy, rather than a fully developed tracking process. The same model formulation was used for all 

sources, but the model accuracy parameters varied with the assumed source. 

Table 1. Range of GPS Accuracy Expected in NextGen Environment 

 Aircraft Category in NextGen Environment 

Error model parameter Units Transport - New Transport - Retrofit General Aviation 

(Low-cost) 

GPS position errors: 

Bias m 3, 1, 5 3, 1, 5 3, 1, 5 

White noise m 0.5, 0.1, 2 0.5, 0.1, 2 1, 0.5, 3 

Fast drift (lightly-damped) m 2, 0.1, 10 2, 0.1, 10 5, 1, 20 

Slow drift (heavily-damped) m 0.1 0.1 0.1 

Fast drift period sec 1.5, 0.5, 3 1.5, 0.5, 3 1.5, 0.5, 3 

GPS velocity errors: 

White noise m/s 0.03, 0.01, 0.10 0.03, 0.01, 0.10 0.1, 0.01, 0.30 

Note: The three values in each cell are estimates of the nominal, minimum, and maximum value expected for the 

given aircraft class under normal airborne conditions. 

4.3 Crew-Vehicle Interface (CVI) 

Human error is a causal or contributing factor in a large majority of aviation accidents and incidents. 

Hence, when analyzing potential safety issues related to CD&R in a NextGen TMA environment, a key 

element of the analysis is flight crew performance and the state-of-the-art of flight deck CVI supporting 

the flight crew. This section describes some of the NextGen TMA operations, equipage, human factors 

issues, and potential NextGen TMA CD&R safety impacts related specifically to CVI and flight crew 

performance. In the Conclusions and Recommendations section, this topic is revisited through discussion 

of outcomes of the modeling analysis that have CVI-related safety implications. 

4.3.1 Potential CVI-relevant NextGen TMA Operational Changes 

There are a variety of envisioned NextGen TMA operations that could impact CVI requirements and pilot 

performance. Key operations that could have significant impact on CVI and flight crew performance are 

listed below. 

1. Equivalent Visual Operations (EVO). EVO, which implies that aircraft could operate under visual 

flight rules (VFR) in zero-zero conditions, would require that flight crews perform CD&R actions 

without the ability to visually acquire the potentially conflicting aircraft, using natural vision. This 

would mean that ATM, ground and aircraft surveillance systems, positioning data, and the CVI 

displays (including EVS) and alerts are the primary source of CD&R information and would need to 

meet more stringent reliability and integrity levels. Without natural visual data to confirm or deny the 
 10 

presence of a potential conflict, required surveillance system “miss” and false/nuisance alarm rates 

will need to be re-evaluated. 

2. Faster paced, higher density operations. Increasing airport throughput means faster paced and higher 

density operations compared to today, especially in low visibility conditions. Ground operations are 

already a very busy time for flight crews, and increases in time pressure along with smaller margins 

of error mean that the CVI will need to provide critical information in a time-critical manner without 

inundating pilots with too much information or distracting them with too many alerts or 

annunciations. 

3. More brittle operations. With time-based operations and smaller margins for separation assurance, 

any deviation such as a missed Required Time of Arrival (RTA) at an intersection, or a perturbation 

such as an aircraft needing to stop or slow down to solve some aircraft problem, may create more 

rippling effects in terms of disruptions to overall traffic flow and airport operations than in today’s 

operations. This brittleness, if it were to occur, could create even more pressure on flight crews to 

hurry, accept clearances that are difficult to meet, and try to multitask during taxi operations when 

they should be focused exclusively on maneuvering the aircraft between the runway and the gate. In 

order to mitigate these brittleness effects, the CVI will need to supply pilots with better situation 

awareness, help them manage workload, and provide more strategic and pro-active information about 

potential problems in executing issued clearances. 

4. Mixed CVI equipage. NextGen will clearly need to accommodate mixed levels of CVI equipage. 

Even though the intent is to incentivize equipage levels which enable new higher capacity operations, 

not all operators will be able to equip to the same levels or at the same pace for economic as well as 

other reasons. In terms of TMA CD&R, this will mean that better CVI-equipped aircraft will need to 

account for potential conflicts with less equipped aircraft where certain assumptions about the 

accuracy and timeliness of pilot responses based on higher levels of equipage will not be valid. 

5. Different levels of airport surveillance infrastructure. Utilization of new landing systems such as 

Ground-based Augmentation System (GBAS) and broadcast of ground surveillance information such 

as ASDE-X to the flight deck should provide heightened levels of TMA CD&R performance. But 

CVI design will need to accommodate integration of ground-based surveillance and aircraft 

surveillance to minimize confusion, and pilots will need to be able to clearly differentiate between 

airports where the additional CD&R infrastructure is available and those where it is not. 

6. Different pilot roles and responsibilities. Generally, the roles and responsibilities of pilots will be 

different, and probably more demanding. Without mitigation, this will likely increase workload, 

which can delay response times and increase errors. The CVI challenge is presenting information to 

pilots that supports the new roles and responsibilities without inundating them with too much 

information or too many attention-getting alerts and indications, and keeping the information and 

tasks simple. 

7. More highly automated and more complex operations. Use of automation will continue to increase, 

requiring pilots to monitor more systems and increase awareness of automation behavior. More 

automation complexity could increase the problem of lack of mode awareness and automation 

surprises. The CVI challenge is providing proper information content that allows pilots to accurately 

assess the state, mode, behavior, and implications of automated system operation and consequent 

flight status. 

4.3.2 NextGen CVI Equipage 

The overall aircraft equipage assumptions for the analyses are provided in a subsequent section. However, 

a more detailed summary in terms of potential CVI equipage for NextGen is provided here. CVI equipage 

can play an important role in maintaining or improving safety in a NextGen TMA environment. 
 11 

There are many CVI products and concepts under development that are aimed at improving flight crew 

SA, especially as it relates to TMA CD&R (e.g., see Hooey et al., 2000, 2002; Jones, 2008; Zeitlin, 

2006). The elements of SA that are particularly relevant to this discussion are awareness of the position of 

potential conflict hazards and awareness of own aircraft position. These elements of SA have a static 

element (where is potential conflicting traffic now, where ownship is now) and a predictive element 

(where potential future conflicts are and where ownship will be when those conflicts might come into 

play). The working assumption that is relevant to TMA CD&R is that the better the displays that pilots 

have in terms of providing good situation awareness, the more likely they are to avoid blunders and 

conflict situations in the first place, and the more likely they are to react quickly and accurately if a 

conflict situation occurs. Below is a list of CVI equipage and concepts that should improve pilot situation 

awareness and thus increase safety by avoidance and resolution of conflict situations. A second list of 

CVI equipage and concepts aimed more generally at pilots’ monitoring and managing tasks is also 

provided. These concepts should provide an indirect benefit to CD&R by helping pilots manage factors 

that affect workload, information load, decision making, and overall mission management. 

CVI equipage and concepts directly relevant to CD&R: 

Synthetic Vision System (SVS) perspective airport views. 

SVS- EVS fusion. 

Airport moving map, surface guidance. 

CDTI (ADS-B, TIS-B, ASDE-X, etc. as sources). 

HUD and Near-to-Eye devices. 

Electronic Flight Bag (EFB) improvements. 

Multifunction Display (MFD) improvements. 

Low end carry-on devices. 

Integrated weather displays. 

Integrated, tailorable, electronic charts and maps. 

Graphical data link and flight planning interfaces. 

Display of data linked info (e.g., Flight Information Services- Broadcast (FIS-B) information). 

Terrain, obstacle, and airport database-driven alerts and advisories (e.g., Runway Awareness & 

Advisory System or RAAS). 

Increased integration of flight deck alerts. 

Taxi display—alerting integration. 

Error and discrepancy checkers. 

3-dimensional (3-D) audio alerting, integrated multimodal alerting. 

CVI equipage and concept improvements indirectly relevant to CD&R: 

“Non-normals” management aids. 

“Non-normals” communication aids. 

Pilot monitoring concepts and fatigue management aids. 

Adaptive user interfaces. 

Task and information management aids. 

Mission management aids. 

Automation management aids. 
 12 

Dynamic function allocation concepts. 

Strategic planning aids. 

Attention management aids. 

Energy management aids. 

Collaboration aids. 

Strategic safety aids. 

4.3.3 Potential CVI-Crew Performance Issues 

While CVI advances should help improve safety related to NextGen TMA operations, the operational 

changes envisioned, in conjunction with expanding pilot roles/responsibilities and CVI changes that 

generally make more information available to the flight crew, have the potential to negatively affect 

safety as well. For example, a more complex operational environment with more highly automated flight 

deck systems will likely exacerbate the age old issue of how to keep pilots involved and informed without 

overloading them with information and high workload. Further, more alerts, notifications and other types 

of information that compete for pilots’ attention could create more distractions, interruptions, and 

disruptions in a very demanding multitasking environment, and it will be more and more difficult to 

maintain an overall quiet, dark cockpit philosophy that has been an important part of aircraft safety in the 

past. 

Terminal area operations are typically high workload, and pilots often feel time pressure, especially 

during departure. Add quicker paced operations, closer spacing, RTAs, EVO, more communications, and 

flight crew stress/workload could increase significantly. Further, current terminal area pilot human factors 

issues such as aircraft energy awareness and management could be exacerbated as arrival and landing 

procedures become more complex and precise (e.g., Required Navigation Performance (RNP), RTAs, 

etc.). This volatile combination of increased operational, procedural, automation, and information 

complexity during operations that are more demanding with less margin for error, could easily offset all 

the benefits of additional technologies and equipage that are aimed at improving NextGen surveillance 

and CD&R. Figure 1 shows key potential pilot performance and human factors issues, and more detailed 

discussion of these issues is provided below.

Figure 1. Potential NextGen Pilot/Human Factors Issues 
 13 

1. Higher Workload. When workload is very high, subtle degradations in human performance occur. 

This can manifest as delays in response, errors, misjudgments, and poor decisions. Low visibility, 

more communications, more information, closer spacing, more precise navigation requirements, and 

faster paced operations all can lead to higher pilot workload. The frequency of “outliers” in terms of 

delayed or erroneous pilot responses to conflict alerts will be higher when workload is higher. Further 

research is needed to determine if the log normal curves for pilot response time used in the modeling 

effort accurately reflect the likelihood of response delays and errors under potentially higher 

workload conditions than are typical in today’s environment. 

2. More distractions. With more sources of information, more tasks, more communication, more alerts 

and notifications, pilots are likely to be more susceptible to distractions and interruptions in a 

NextGen environment. Distractions have the same basic effect on performance as very high workload 

– pilots’ understanding of, and responses to, potential conflict situations are more likely to be delayed 

or erroneous if they are distracted by another task or other information that is not related to the 

situation at hand. The CVI challenge is to help manage the increased tasking, information, and alerts 

so that distractions are minimized (e.g., quiet dark cockpit philosophy), yet attention management 

assistance is provided to assure that pilots attend to the most important information. 

3. Increased automation confusion. Both flight deck control automation and information automation 

will be more complex in order to meet the efficiency and safety requirements of the NextGen 

operational environment. Today’s automation issues, such as lack of trust versus over-reliance on 

automation and processed information, pilot input and cross checking errors, misunderstanding of 

automation behavior and modes, especially in anomalous or unusual situations, are all likely to be 

exacerbated as future automation complexity and interconnectivity increase. The CVI challenge is to 

provide information and feedback to pilots that simplify the task of monitoring and managing the 

automation. The more layers and types of automation that are an integral part of surveillance and 

conflict avoidance, and the more information and information sources that must be processed and 

integrated by the pilot in order to understand and respond to possible conflict situations, the more 

likely errors and delays in response can occur. An important research issue to address is pilot 

response to conflict situations when surveillance automation and various information sources add an 

automation management/awareness task to the primary task of assessing the conflict situation. 

4. Increased information management demands. Integration of information and information sources add 

“overhead” for pilots just as the need to understand complex automation behaviors and modes does. 

For surveillance and CD&R, this could become a critical issue if the sources and reliability of 

surveillance information vary by airport infrastructure and aircraft equipage, and if multiple sources 

of surveillance information are available simultaneously. Funk et al. (2008) identify issues such as, 

“procedures using different sources of traffic information (visual, ADS-B, TCAS-II, Air Traffic 

Control (ATC)) may lead to unsafe situations,” and “lack of standardization of ADS-B cockpit 

equipment may lead to errors,” as new opportunities for errors created by variations in CVI and 

surveillance advances such as ADS-B and CDTI. 

5. Difficulty in keeping training, procedures, and design aligned. In addition to the concerns about the 

general trend toward less trained, less experienced pilots, the projected new equipage required to 

enable NextGen operations raises a “too much, too fast” concern about required changes in 

accompanying training and procedures. Pilots will use new equipment, requirements, procedures, and 

terminology that will require specific training. In order to make such wholesale changes in equipage 

and procedures successful from a pilot usability perspective, there is an added priority on CVI design 

to make the pilot tasks and interactions with the new systems as simple and intuitive as possible. 

Research is needed to assure that the aggregate of new systems, equipage, and procedures don’t 

overwhelm pilots with system interaction and training requirements. 

6. More multitasking because of new pilot roles and responsibilities. When considering new pilot roles 

and responsibilities, new operational requirements must be looked at, such as self separation, greater 

air-ground collaboration, time and trajectory based clearances and so on, but changes in pilot roles 
 14 

and responsibilities as automation and information monitors and managers must be considered. This 

issue emerges from several of the other issues described above, such as greater information demands 

and higher workload, but needs to be addressed as a separate issue because envisioned changes to 

equipage and procedures can have such a profound effect on cumulative flight crew tasking in the 

more demanding phases of flight in a NextGen environment. The research issue that needs to be 

addressed here is how specific tasks such as CD&R are performed in a full mission environment, 

especially if the conflict is completely unexpected. 

7. Variation in operations, infrastructure and equipage. Funk et al. (2008) summarized CVI and pilot 

performance issues related to these variations: “For the foreseeable future, aircraft equipped with 

ADS-B will share the airspace with aircraft that are not ADS-B equipped. Furthermore, ADS-B 

aircraft will need to operate in areas with and without radar coverage with varying degrees of ATC 

supervision. Thus, on a single flight an aircraft could fly from an area where ATC has primary 

responsibility for the separation of aircraft but all aircraft are visible to the flight crew on the ADS-B 

display, to airspace in which ADS-B aircraft continue to be displayed while non-ADS-B equipped 

aircraft disappear due to gaps in radar coverage. To ensure safe and efficient flight, different 

monitoring, communication, and avoidance procedures for different conditions must be developed 

and pilots must be trained in these procedures. Furthermore, displays will need to be built that will 

alert the pilot to the conditions in which they are operating. This will be especially important if 

NextGen plans, which call for the ability to dynamically shift airspace depending on weather and 

traffic demands, are implemented.” In particular, Funk et al. point out potential CVI/pilot 

performance issues related to introduction of ADS-B and CDTI, and issues related to variations in 

that equipage: 

a. Pilots using CDTIs may be more likely to unsafely deviate from ATC clearances. 

b. Excessive use of CDTIs may reduce visual traffic scan skills. 

c. ADS-B equipment may increase workload and distractions. 

d. Pilots may become overconfident in and over-reliant on ADS-B. 

e. Pilots may not adequately understand ADS-B capabilities and limitations. 

f. CDTI placement may make displays difficult to read. 

g. CDTI display clutter may exacerbate conflicts. 

8. Data integrity and reliability issues. Especially in EVO conditions where there may be no “raw data’ 

by which to cross check or validate surveillance data from ground and airborne sensor sources, there 

will be greater need for data integrity and reliability. If there are differences in accuracy or integrity 

of data from different data sources, pilots will need some indication of what the performance levels of 

the systems are, or there will need to be some type of graceful degradation strategy such that data is 

not relied upon if it is deemed to be below some accuracy threshold. Further, if multiple sources of 

surveillance data are available, voting or fusing schemes will be required in order to present pilots 

with composite data to avoid confusion and the need for pilots to disambiguate disparate sources of 

data. 

4.3.4 Potential impact of CVI and pilot performance on TMA CD&R safety levels 

The assumption was made that CVI and pilot performance issues can have two main effects on TMA 

CD&R safety levels: (1) they can influence the probability of blunders and deviations, that is, the 

likelihood that a conflict situation arises in the first place; and (2) they can influence timely and accurate 

CD&R, that is, the likelihood that a conflict will be safely avoided once it occurs. While the probability of 

blunders and deviations was only assumed at a gross level in conjunction with the modeling analysis 

performed here, a short discussion of CVI and probability of blunders/deviations is provided below. 

Then, more importantly, a discussion of CVI and CD&R is re-visited in the conclusions, based on results 

of the modeling analysis. 
 15 

1. Probability of blunders & deviations. NextGen CVI advances such as SVS, ADS-B, TIS-B, CDTI, 

taxi displays and guidance, data linked ground clearances, etc., are likely to significantly reduce the 

probability of blunders and conflict situations in terminal areas in a NextGen environment. Not only 

should pilots have better awareness of potential conflicts, they should also have more reliable and 

precise guidance in terms of assuring their own aircraft is where it should be. On the other hand, 

because of faster paced operations with smaller margins for error, and the multitude of issues that can 

lead to more pilot distractions, higher workload, more automation awareness issues, and so on, 

blunders could actually increase because of increased opportunities for human error. These 

conflicting scenarios, in terms of potential blunder and deviations probabilities, make this an 

important issue to address empirically as well as with parametric analyses. 

2. Probability of safe CD&R. The modeling effort assumed a log normal response time curve for pilot 

reaction to conflict alerts (similar to that used in Kuchar and Hansman, 1996, but using response 

times judged to be more representative of airport operations). This is a simple assumption that could 

over- or under-estimate the expectancy of pilot response times due to not only individual differences, 

but to the benefits of CVI advances and the possibility of creating new pilot performance behaviors 

(as described above). In general, one might assume that conflicts which arise from “quantitative 

deviations,” that is, tighter operations and smaller tolerances, where a potential conflict might occur, 

for example, because an aircraft is slightly early or late to an intersection, will result in generally 

quicker and less variable responses by pilots because they are more prepared and focused on the 

potential conflict situation. Conversely, conflicts which arise from “qualitative blunders,” where an 

aircraft or another vehicle is not where it should be and is not expected by the other aircraft, will 

result in higher likelihood of pilot response “outliers” where abnormally slow or inappropriate 

responses may violate the assumptions of the pilot response time model used here. 

Pilot responses in a variety of CD&R scenarios using full mission operational conditions should be 

assessed to validate the simple pilot response time models used here. Further, the current modeling effort 

did not account for erroneous responses – i.e., not performing the correct response and in some cases, 

doing the opposite of the appropriate response (e.g., turning left instead of right, stopping instead of 

accelerating). The potential for response errors such as these must be empirically evaluated as well. 

4.4 Other NextGen Equipage Changes 

While expected CVI and surveillance equipage changes were described in previous sections, there are 

many other equipage changes expected as well, primarily in communication, database, flight 

management, and navigation systems. These changes will enable precise trajectory and time-based 

operations, more precise aircraft position information relative to permanent obstacles and airport 

geometries, data linked communication of taxi clearances and more time-critical alerts and information 

from ground to aircraft, integration of flight planning, communication, and surveillance functions, and so 

on. The overall key equipage assumptions for generating the alternative NextGen environments for these 

analyses included: ADS-B Out, ADS-B In, cockpit moving map, TCAS, CDTI, basic and/or 

comprehensive alerting, database-driven airport surface alerts, data linked taxi routes, and uplinked 

alerting. 

4.5 Ground Area Procedure Changes 

The CD&R function anticipated for NextGen will exist with increasing deployment of technological 

advances in communication (air-ground data links, System Wide Information Management), navigation 

(area navigation (RNAV), required navigation performance (RNP)), and surveillance (ASDE-X, ADS-B, 

and LCGS). The use of Runway Status Lights (RWSL) and other lighting guidance will also increase. 

The use of CDTI to give flight crews immediate access to a representation of ownship position on an 

airport map and showing known traffic is expected to provide substantial benefits through reduction in 

errors and inefficiencies (such as hesitation). Some improvement in situation awareness is expected from 

the use of a moving map with ownship position. 
 16 

NextGen is likely to bring many changes to ground procedures, as part of a continual drive to translate 

technology advancements into operational improvements. However, it is difficult to predict the final form 

of those procedures, as research continues to find matches between operational benefits and feasible 

equipage and support system improvements. For this study, it is assumed that NextGen procedural 

efficiency benefits will be based primarily on the following two mechanisms: improving average airport 

throughput by reducing inefficiencies caused by adverse conditions (congestion and/or bad weather), and 

enabling operations that are predictably more efficient by using time-based clearances (Surface 

Trajectory-Based Operations, or STBO) (JPDO, 2007). So far, the procedures envision conducting 

operations in bad weather that match the throughput known to be achievable in good weather. No 

advocacy for systematically increasing peak taxi speeds above those achieved in good traffic and weather 

conditions has been found. This is consistent with the general belief that congestion and maximum 

runway throughput are a greater detriment to airport efficiency than the peak taxi speed. Increased 

predictability of the taxi phase of flight is also desired; this goal will tend to discourage procedures based 

on increasing the maximum taxi speeds. 

Therefore, initial assumptions were made that the limits on speed and separation of aircraft on runways 

and on the surface will be fundamentally unchanged, and that the effect of procedure changes will be 

primarily to: 

Enable speeds and spacing in poor weather that match those achieved under best-case 

conditions in good weather; 

Automate tasks (such as delivery of taxi route clearances) that presently make high demands 

on controllers, pilots, and the radio communication between them, reducing the need for 

consultation of maps and holding while awaiting instructions; and 

Selectively introduce new procedures (such as precise time-based taxi schedules aided by 

speed guidance in the cockpit) with the aim of improving overall efficiency, but only for the 

best-equipped aircraft. 

The modeling assumed that a robust CD&R function must exist independently of any equipment designed 

to implement new procedures. The CD&R function will protect against any hazards that arise through 

failures (mechanical or human) in the new equipment and the way that it is used, in addition to currentday hazards. 

In today’s environment, aircraft and vehicle traffic in the movement area (taxiways and runways) is 

directed by one or more ground controllers working in the ATC tower, while departing and landing 

aircraft are directed by local controllers in the tower. Larger airports may have one or more ramp towers 

to direct traffic in non-movement areas, staffed by the airport authority or by individual airlines. The 

primary method of control and coordination is voice communication. 

Surface traffic conflict avoidance begins with the taxi instructions (route and hold points) issued by the 

ground controller, complemented by visual separation, as possible and practical, by the flight crew as the 

taxi instructions are executed. Taxi instructions allow the pilot to proceed along the route to the next hold 

point identified in the clearance with spacing from preceding aircraft or vehicles at the pilot’s discretion. 

(Controller clearance is now required for all runway crossings. Previously, instructions to taxi to the 

active runway were considered to imply clearance to cross any other runways en route). If visibility is 

extremely poor, controllers may change the instructions to provide more distance between aircraft, but 

separation responsibility remains with the pilot. Pilots may also request “progressive taxi” instructions 

(turn-by-turn instructions during taxi) as needed due to poor visibility or uncertainty in the airport layout. 

The availability of CDTI is not expected to alter the fundamental reliance on pilot responsibility for 

separation during taxi using “out-the-window” visual scanning. As ADS-B adoption becomes widespread 

under the Notice of Proposed Rule Making (NPRM) (FAA, 2007), it becomes more probable that all 

nearby aircraft (and vehicles at some airports) will appear on a CDTI; however, it cannot be guaranteed. 
 17 

The chance of encountering a non-cooperative vehicle, a vehicle or aircraft with a failed transponder, or 

failure of the CDTI itself is nonzero, making it unlikely that procedures will be adopted that do not rely 

on visual scan for surface navigation. The doctrine for CDTI use is expected to be similar to TCAS, in 

that it increases situation awareness but should not be relied on for separation. 

NextGen is also expected to move routine exchanges of information such as taxi instructions from voice 

channels to data link, with voice used for confirmation, runway clearances, and backup/contingency (i.e., 

voice by exception). Procedures and practices that insure consistency between the data link and voice 

communication will be required. The availability of a high-speed, low-latency data link is also essential to 

concepts proposed for the use of STBO. STBO implies that taxi instructions will include constraints on 

the times of arrival at waypoints along the surface route, and that these constraints are centrally 

coordinated by ATC to meet constraints on safety and efficiency. Some STBO variants extend as far as 

adding cockpit indications for the taxi speed required by the planned schedule. As this feature would 

require dedicated cockpit instrumentation, it was assumed that such procedures would not be widely used 

in the envisioned end-state. 

4.6 Terminal Area Procedure Changes 

4.6.1 RNAV and RNP Background 

RNAV is the widely used avionics system that defines the three-dimensional reference path for the 

aircraft, and then provides automatic steering (guidance) signals or commands in order to follow that 

path. This reference path may come from the flight plan, be communicated to the aircraft from ATC / Air 

Navigation Service Provider (ANSP), or from direct pilot entry. RNAV provides a way to follow a more 

direct or optimal path without that path being anchored to ground-based navigation aids such as VHF 

Omni-directional Range (VOR). RNAV has been available for 40 years. 

With the advent of precision navigation systems such as Inertial Navigation Systems and GPS, the 

concept of “required navigation performance” (RNP) has emerged. RNP is a measure of navigation 

accuracy that guarantees that the aircraft flying a prescribed route or path, such as from RNAV, maintains 

its desired position along that path within some specified set of parameters. It is a measure of how 

accurate the aircraft navigation and guidance systems are. RNP is currently used to specify how close to 

the route centerline the aircraft will be maintained during flight; that is, RNP specifies the maximum size 

of the flight path lateral or cross-track position error that would be expected during flight. For example, a 

navigation system with RNP of 1.0 nm will maintain the aircraft’s position along the reference path 

within 1.0 nm. As this capability evolves, both the vertical and in-trail / longitudinal accuracies will be 

added to the RNP specification. 

The multiple uses of RNAV and RNP are very important to early realizations of several NextGen 

operational improvements. With very accurate lateral navigation along a more direct RNAV path, the 

following benefits can be obtained: 

Shorter, more direct paths can be defined that connect the beginning and ending points of a route 

segment. Shorter paths take less time and fuel saving operating cost and decreasing emissions. 

Smaller, more accurate RNP performance enables defining and using more paths within a given 

airspace volume. These paths can be more complex than if they are restricted to what an air traffic 

controller can readily command. This makes better use of the airspace, allows more flexible 

routing when airspace is restricted by storm cells, and, in general, enables greater overall capacity 

in congested terminal metroplex airspace. 

Aircraft with accurate RNP performance are more predictable in their flight progress making the 

job of the air traffic manager less demanding and more productive. 
 18 

4.6.2 Precision Surveillance 

The use of RNAV and RNP to enable flying more closely separated flight paths requires that the groundbased ANSP surveillance system ensure that the aircraft are successfully flying along these paths within 

the designated envelope expected for the flight. In other words, the applications of precision navigation 

and precision surveillance go hand in hand. If a flight path is defined with a small RNP value, and the 

aircraft / flight crew commits to fly such a path, then the surveillance system must have even greater 

accuracy than the RNP accuracy for monitoring the flight progress. It must assess the flight trajectory to 

assure that it is within its intended accuracy envelope, and if there is too large a deviation, for whatever 

reason, it must detect that unexpected error. Even more importantly, if the flight path error creates a risk 

of conflict with another aircraft or ground obstacle, the surveillance system must detect that conflict, and 

issue warning and conflict resolution instructions to the flight crews involved. 

Thus, precision surveillance is another necessity and enabler for the NextGen applications of more 

precise, closely spaced aircraft operations within the congested terminal area. Existing Terminal Radar 

Control (TRACON) radars do not provide that accuracy. However, extending ASDE-X surveillance 

capability to implement a wide area multi-lateration (WAM) system does, in the near term. As more and 

more aircraft become ADS-B “Out” equipped, this equipage can provide the necessary surveillance 

accuracy with WAM serving as a suitable safety backup. 

4.6.3 Parallel RNAV / RNP Approaches 

4.6.3.1 Application Description 

A near-term example application of improved navigation and surveillance accuracy that exploits the 

capabilities of aircraft equipped with more accurate RNP capability is referred to here as “parallel RNAV 

/ RNP approaches.” In this application, those aircraft equipped with more accurate RNP capability get to 

fly more direct routes to the destination runway. To describe this, suppose that aircraft approaching a 

common runway can be divided into those with RNP of 1.0 nm and 0.3 nm; those with 0.3 nm RNP get 

approach route preference. 

Figure 2 illustrates two parallel approach paths for aircraft entering the terminal airspace on the (long 

side) downwind legs. The inner path closer to the runway and more direct is the RNP 0.3 path. The outer 

path that is off-set further from the runway is for the RNP 1.0 aircraft. As the aircraft turn to base and 

then final, they are merged into a common path with appropriate in-trail spacing based on wake vortex 

and runway occupancy considerations. 

This application can be extended to have the two paths end onto parallel final approach paths leading to 

parallel runways. That is, they do not necessarily merge into a single path. Another consideration is that a 

similar parallel path structure can be used to design the approach routes coming from the short side of the 

runway. The short and long side paths all merge onto the final approach extended runway centerline(s). 

Figure 3 illustrates in more detail how the two parallel paths would be positioned. The centerline of the 

RNP 0.3 path is offset from the runway centerline the amount that allows aircraft on this path to turn to 

base and then final within the ride-comfortable turning envelope of the aircraft assuming it is 0.6 nm (4-

sigma) to the right of centerline as it begins its base turn. The centerline of the RNP 1.0 path is offset 

from the RNP 0.3 path by 2.6 nm (to create a conservative 4-sigma cross-track separation between the 

parallel RNAV routes.) 
 19 

Figure 2. Aircraft with Different RNP Capability Approach Runway on Different Paths 

RNAV 2 nm Radar

RNP 0.3 nm Procedural Separation

With ATC Conformance Monitoring

(CDA)

RNP 1.0 nm Radar

RNP 0.3 nm Procedural Separation

With ATC Conformance Monitoring

(CDA)

RPI (Ghost)
 20 

RNAV with Surveillance Standard of 2.0 nm or

RNP of 1.0 nm at 2 sigma

1.0 nm

0.6 nm

1.0 nm

RNP of 0.3 nm at 2 sigma

RNAV with Surveillance Standard of 2.0 nm or

RNP of 1.0 nm at 2 sigma

1.0 nm

0.6 nm

1.0 nm

RNP of 0.3 nm at 2 sigma

RNAV with Surveillance Standard of 2.0 nm or

RNP of 1.0 nm at 2 sigma

1.0 nm

0.6 nm

1.0 nm

RNP of 0.3 nm at 2 sigma

RNAV with Surveillance Standard of 2.0 nm or

RNP of 1.0 nm at 2 sigma

1.0 nm

0.6 nm

1.0 nm

RNP of 0.3 nm at 2 sigma

Figure 3. Spacing Geometry Details for RNP-governed Parallel RNAV Approaches 

4.6.3.2 Potential for Conflicts and Their Detection Requirements 

There are two types of conflicts that must be addressed in considering the choice of spacing parameters 

for implementing the parallel RNAV/RNP approaches – cross-track error, and in trail spacing error. This 

conflict characterization is not unique to this particular example application, but is typical of all 

RNAV/RNP NextGen concepts. 

Cross-track Error 

In designing parallel or converging reference paths through a congested airspace, proper allowance must 

be made for the collective normal cross-track flight path errors due to imperfections or variations in 

navigation, reference RNAV path flight following ability (flight technical error), winds aloft, surveillance, 

and equipment failures. As shown in the earlier example, the parallel RNAV paths were placed the width 

of two RNP distances (2.0 nm plus 0.6 nm) apart. A parametric study is needed to determine what 

distances should be designed into the path separations in order that the cross-track conflicts due to highly 

unlikely errors would be kept to an acceptable level of safety. 

In addition, the analysis must consider the possibility of a blunder caused by equipment failure, pilot 

negligence, or other cause. The blunder would be such that the aircraft on one of the adjacent paths would 

leave that path and fly into conflict with an aircraft on the other path. This analysis should determine that 

such a blunder would be detected by the surveillance system in time for the traffic manager to resolve the 

conflict. 
 21 

In-trail Spacing Error 

Aircraft can be spaced closer together in trail by ANSP use of (a) a scheduling algorithm (such as used by 

the TMA function); (b) plan form display that shows projections of aircraft on routes that will merge; plus 

(c) desired inter-aircraft spacing indications that account for wake vortex, runway occupancy, and 

departure slot constraints. By spacing aircraft closer together in trail, this implies that tighter control must 

be maintained in keeping the aircraft within some error bound (deviation tolerance) of their desired 

relative positions in the schedule of incoming aircraft. These error bounds must be determined such that 

an envelope protection system can be designed to monitor the relative spacing, and so that procedures can 

be designed for the traffic manager to issue commands that will return an errant aircraft back into safe 

spacing tolerances or to leave the approach sequence and be placed at a later position for approach. 

5. Analysis Methods 

This section describes the analysis methods used to model NextGen CD&R. 

The approach was to build models that credibly represent the most fundamental limitations on conflict 

resolution performance to first-order accuracy under the aforementioned assumptions. This approach was 

taken because of the infeasibility of forecasting the interactions on all conceivable NextGen 

developments, and because the study priority was to differentiate the performance associated with 

different cockpit and ground system technologies in an end-state representing full deployment. 

The following steps were taken in developing the modeling approach and framework. 

Developed algorithms, software, and processes to model effects of interest. 

Determined form and parameters of models for: 

– Surveillance accuracy, 

– CD&R decision variable accuracy, 

– CD&R decision threshold determination, and 

– Probability of each type of interaction (encounter) between each assumed category of 

aircraft. 

Developed techniques for estimating model parameters via analysis or simulation. 

Used plausible rationale for assumed environment and systems. 

Avoided disproportionate effort on second-order effects. 

Concentrated on analytical models as basis for future requirements. 

Built analytical foundation rather than system architecture. 

Assumed reasonable requirement allocation (i.e., no unrealistic mandates). 

Assumed solutions to peripheral issues could be developed. 

Built framework to connect the results of separate models into general conclusions. 

5.1 Modeling Baseline and Alternative NextGen Environments 

Four NextGen environments were considered, representing a baseline end-state (NextGen 1) and three 

variants. Aircraft were divided into 3 major classes, as will be discussed in Section 5.1.3. After extensive 

discussions of the expected equipage levels, a consensus on representative and plausible equipage 

combinations was reached. The minimum ADS-B equipage was assumed to be ADS-B Out, in keeping 

with the current ADS-B NPRM. Table 2 summarizes the alternative NextGen environments for purposes 

of this study. 
 22 

Table 2. Summary of Alternative NextGen Environments 

LabelTransport—NewTransport—RetrofitNonTransport

NextGen1

(Baseline)

ADSBOut

Cockpitmovingmapwith

ownshipposition

TCASorsuccessorAirborne

CollisionAvoidanceSystem

(ACAS)

ADSBOut

Cockpitmovingmapwith

ownshipposition

TCASorsuccessorACAS

ADSBOut

Cockpitmovingmapwith

ownshipposition

NextGen2ADSBOut,ADSBIn

CDTIwithownship,traffic,alert

presentation

Onboardalertingforbasic

runwayhazards

TCASorsuccessorACAS

ADSBOut

Cockpitmovingmapwith

ownshipposition

TCASorsuccessorACAS

Onboardindication/alertingfor

nontraffichazards

ADSBOut

Cockpitmovingmapwith

ownshipposition

NextGen3ADSBOut,ADSBIn

CDTIwithownship,traffic,alert

presentation

Datalinkedtaxiroutes

Onboardalertingfor

comprehensivehazards(taxiway,

lowaltitude,expandedrunway)

TCASorsuccessorACAS

ADSBOut,ADSBIn

CDTIwithownship,traffic,alert

presentation

Datalinkedtaxiroutes

Onboardalertingforbasic

runwayhazards

TCASorsuccessorACAS

ADSBOut,ADSBIn

CDTIwithownship,traffic

Onboardalertingforbasic

runwayhazards

NextGen4ADSBOut,ADSBIn

CDTIwithownship,traffic,alert

presentation

Uplinkedalertingfor

comprehensivehazards(taxiway,

lowaltitude,expandedrunway)

TCASorsuccessorACAS

ADSBOut,ADSBIn

CDTIwithownship,traffic,alert

presentation

Uplinkedalertingfor

comprehensivehazards(taxiway,

lowaltitude,expandedrunway)

TCASorsuccessorACAS

ADSBOut,ADSBIn

CDTIwithownship,traffic,alert

presentation

Uplinkedalertingfor

comprehensivehazards(taxiway,

lowaltitude,expandedrunway)

5.1.1 Conflict Scenarios 

The analysis employed five conflict scenarios. The scenarios were chosen to include instances of taxiway, 

runway, and low-altitude conflicts and to show a variety of conflict geometries. The chosen scenarios 

were: 

Arrival and landing with taxiing aircraft crossing runway. 

Two arrivals to intersecting runways. 

Two aircraft taxiing along same path with following aircraft overtaking. 

An arriver approaching the runway while the previous landing aircraft is still on the runway. 

RNAV / RNP approach to same runway using parallel paths. 

The geometry and parameters of the scenarios analyzed are described in subsequent sections. Other 

scenarios considered included: two aircraft taxiing toward a common intersection; an aircraft departing as 

another arrives to a closely-spaced parallel runway; and simultaneous approaches to parallel runways. 

For each scenario, there are many possible formulations of tests to discriminate between safe and unsafe 

encounters. For this study, an attempt was made to identify the most critical decision variable, based on 

sensitivity to the joint surveillance quality of the aircraft involved. This approach recognized that “tight” 
 23 

operations, such as those associated with maximum airport throughput, generally put maximum stress on 

CD&R discrimination. 

5.1.2 Model for Equipage Adoption 

The models were intended to mimic end-states in which the transition to one of the four alternative 

NextGen environments has occurred. The goal was to provide clear differentiation of the potential safety 

improvement realized under each alternative, in order to guide further research and possibly policy 

decisions. Early in the study, the approach of predicting risk levels throughout a “build-out” period 

concluding in an end-state of full NextGen equipage (according to one of the four NextGen environments 

described in Table 2) was considered. However, this approach was rejected for two main reasons. 

First, the effort to evaluate effects over a period of incremental adoption was far more complicated than 

modeling the end-state and the effort was judged to be impractical. The problem of predicting the 

incremental safety benefits of partial equipage during the build-out period merits a separate study. In 

addition to postulating an adoption timeline for each equipage improvement considered, many more 

combinations of equipage and ground systems would need to be evaluated in the performance models. For 

example, it is likely that the mix of transport aircraft will include many combinations of Mode S position 

reporting and associated situation awareness and conflict detection technologies until full adoption and 

standardization are completed. Also, the timeline for training flight crews with the evolving technologies, 

and the rate of introduction of new procedures using the technologies, would also need to be modeled to 

produce meaningful results. 

Second, introducing a gradual adoption rate would tend to dilute the improvements associated with each 

technology in the end-state, making the study less useful in differentiating the effect of alternative 

NextGen equipage levels. 

Therefore, it was assumed that each NextGen alternative represented a steady state with a defined level of 

equipage (equipment installation) for each defined aircraft class. The set of classes used are described in 

the next section. 

5.1.3 Aircraft Classes 

Although the study considered only possible NextGen “end-states,” the population of aircraft in each 

assumed end-state was non-uniform, guaranteeing that encounters between aircraft with different 

equipage were considered. The approach was to limit the model to three classes -- “New Transport,” 

“Retrofit Transport,” and “Non-Transport.” Within these classes, equipage characteristics were chosen to 

match the lowest common denominator, representing the anticipated minimum equipment level for each 

class. Therefore, although older transports or high-end general aviation (GA) (i.e., Non-Transport) aircraft 

may voluntarily invest in high-performance GPS, ADS-B In, and a comprehensive on-board CD&R 

capability, these features are not expected to be required for all aircraft in that class, and so no credit for 

the advanced capabilities beyond the minimum equipment level will be given. It is assumed, however, 

that advanced equipment is designed for compatibility with other systems so that the advanced 

capabilities are not detrimental to safety. 

5.1.4 Assets Used in Conflict Detection and Resolution 

The ability to perform CD&R in a given encounter between two aircraft depends on the interaction of 

multiple ground and aircraft assets, including communication, surveillance, conflict detection, and 

conflict alert presentation. The services available are assumed to include all ground infrastructures 

required for effective ADS-B, such as ADS-R, to communicate information between aircraft on different 

data links (Mode S and Universal Access Transceiver (UAT)). For surveillance, an end-state with 

universal ADS-B Out (or equivalent) has been assumed; therefore, variations between airports, 

specifically whether ASDE-X surveillance was provided, were not modeled. Obviously, primary 

surveillance is necessary for safe management of a mixture of cooperative and non-cooperative mobile 
 24 

objects; therefore, the model did not address the risks of using vehicles without transponders or equivalent 

surveillance. 

The assumed capabilities for conflict detection and subsequent alerting vary according to the perceived 

difficulty of development and installation or retrofit. Generally, a higher level of alerting capability was 

assumed for new transport aircraft, and a lower level for “non-transport.” This ignores the likelihood that 

“high-end GA” (such as private jets or air taxi) may well have surveillance and conflict detection 

equipment; however, no credit for the possibly improved CD&R performance of this aircraft class was 

taken. 

The costs of providing a certified CD&R capability rise significantly if it is made comprehensive. 

Recognizing that there is disagreement about whether CD&R is effective in some environments, such as 

airport taxiways, the various NextGen alternatives assumed different levels of alert generation and 

presentation capability. In contrast, the most comprehensive ground-based alerting capability was 

assumed to be available to all aircraft. This in turn was based on the assumptions that CDTI will be 

available for all aircraft classes and NextGen end-state, that necessary alerts can be effectively presented 

on the CDTI with negligible retrofit costs, and that the full-featured ground-based alerting will be 

provided at every airport, because the development effort can be performed once for the entire NAS. 

An infrastructure to rapidly distribute any ground-generated alerts via data link was also assumed to be 

available, as a cockpit data link is desired for many other purposes (such as communication of TBO 

clearances.) The bandwidth required to transmit infrequent time-critical alert messages should be small 

compared to the bandwidth needed for routine exchange of taxi instructions and schedules. 

5.1.5 Encounter Rates between Aircraft Classes 

There are many difficulties in quantifying the rate at which aircraft in specified classes will operate in 

close enough proximity to be considered candidates for conflict detection alerts. The study used a simple 

model for NAS -wide effects by assuming that each of the aircraft classes modeled represented a fraction 

of all NAS operations, and that encounters between each class of aircraft occurred in proportion to their 

representation in the population of NAS operations. This model ignored the variations in aircraft class 

representation correlated to airports, specific runways, and daily, weekly, or seasonal cycles. (As an 

example, the proportion of GA aircraft operations at Milwaukee (MKE) on a fair weekend day will be 

relatively high and the proportion of GA aircraft at New York John F. Kennedy (JFK) on a rainy weekday 

morning will be relatively low.) Aside from the complexity of gathering data to analyze these 

correlations, there is every reason to believe that new NextGen procedures may create new correlation 

patterns, for example, by reserving a runway or an approach corridor for a better-equipped class of 

aircraft. These interactions should be considered if the approach used in this study is extended to a more 

detailed analysis of safety capabilities. More specific definitions of assumed procedures, particularly 

those linked to equipage, and the assumed changes in the population of aircraft operations by class or 

type, would be needed to perform such an analysis. 

5.1.6 Selected Safety Metrics 

The majority of the analysis effort was focused on estimating the ability of an assumed CD&R capability 

to prevent a collision, conditioned on the assumption that a conflict occurs in a particular assumed 

NextGen environment. The metric developed for this ability is referred to as the Conditional Collision 

Prevention Capability (CCPC). 

The CCPC makes use of first-order models for pilot response time and assumed kinematic state accuracy 

variability, based on realistic models of surveillance and estimation errors. The CCPC also differentiates 

between qualitative (“blunder”) and quantitative (“deviation”) error mechanisms, and accounts for 

variations in conflict detectability between multiple potential alert sources (on-board either aircraft 

involved, or uplinked from a ground CD&R system.) 
 25 

The CCPC is emphasized because it is easier to estimate the effectiveness of a safety capability than to 

predict how often it will be required. The model elements involved in measuring CCPC are relatively 

well-defined and clearly show the effects of equipage capabilities. In contrast, due to the difficulty of 

predicting error rates in future environments for which the procedures, workload, and mix of aircraft 

types are unknown, there is much more uncertainty about the rate at which conflicts could be expected to 

develop in the assumed NextGen environments than there is in the capabilities of the assumed CD&R 

systems. 

However, a model based on simple assumptions about these error rates has been developed so that the 

relationship between individual conflict detection capabilities and overall system safety levels can be 

explored. Further research is strongly recommended to validate the assumptions used in these scenarios. 

For example, the rate at which blunders are avoided due to improvements in general situation awareness 

strongly affects the incremental benefits to be derived from providing CD&R coupled with alerts. 

For each NAS aircraft class, the assumed level of equipage and ground support systems (as a function of 

NextGen scenario) was associated with three contributing factors: 

1. A driving rate of lapses that could lead to conflicts (typically pilot deviations or operational errors). 

2. A fraction (“yield”) of lapses that result in potential conflicts eligible for alerts (complemented by 

the fraction of lapses that are resolved prior to alert eligibility). 

3. A fraction of potential conflicts for which alerts are generated in time for successful conflict 

resolution by the specified alert source (implying that no party involved acted to prevent the lapse 

from becoming a potential conflict). 

In other words, for each encounter type and each assumed equipage/ground system combination, the 

model assumed that there is a fixed probability of noticing a problem and resolving it before it becomes 

detectable as a potential conflict. The assumed mechanism is that there is an underlying rate of potential 

errors due to lapses; of course, the lapse rate itself could be affected by changes in pilot or controller 

workload and environment, as described in Section 4.3. Assuming that lapses remain undetected due to 

deficiencies in information availability or presentation, improvements in situation awareness should 

reduce the yield of potential conflicts from lapses. Therefore, the assumed introduction of CDTI into all 

aircraft in each of the assumed NextGen end-states will reduce the rate of potential conflicts, even before 

considering the benefits of CD&R. 

The model implicitly assumed that the performance of each party to a potential conflict (Aircraft A, 

Aircraft B, or controller) in either noticing the conflict or reacting to an alert is independent. Therefore, 

there are multiple parties who can act to resolve the situation, which increases the assumed system 

effectiveness. The possibility of interference between resolution actions (for example, A’s response tends 

to diminish the benefit of B’s action) was not considered. This is an area for which further research is 

recommended to determine whether there are “common mode” effects reducing the assumed benefit of 

independent conflict prevent actions. For example, if both A and B rely on a single source of information 

for a critical parameter such as B’s position, and that source is incorrect, the probability of a correct 

response between the two is correlated, violating the assumption of independent performance. 

5.2 Analysis Framework 

The analysis framework was based on the use of many first-order models to capture all the major 

dependencies in system performance. The goal was not to develop an alerting system as such, but to 

describe the expected performance limits of possible systems parametrically in terms of surveillance and 

equipage provided. In order to get comparable performance limits across scenarios and with different 

assumed NextGen environments, a consistent approach to each environment was needed. 

Figure 4 shows the elements of the analysis framework used to insure a consistent modeling approach. 
 26 

Figure 4. Analysis Framework for Alerting Capability Model 

5.3 Trades and Decisions for Modeling Analysis 

5.3.1 Response Time Model 

For purposes of modeling pilot response times (RT) in these analyses, a set of simple log normal curves 

of pilot response times to alerts was used. The basic log normal RT curve was proposed by Kuchar and 

Hansman (1996), and using the RT variations proposed by Stanton (2006) based on the alarm initiated 

activity (AIA) model (see Table 3), plausible variations of the basic log normal curve (see Figure 5) were 

created based on differences in predicted pilot workload and degree of readiness for different conflict 

scenarios (see Table 4). It should be noted that while very simple pilot response time assumptions were 

used here, much more sophisticated human performance models (e.g., Foyle et al., 2005; Glenn et al., 

2005; Kuchar et al., 2000; Lee et al., 2004; Pritchett et al., 2002; Reising et al., 2004; Tyler et al., 1998 ) 

could be applied for more rigorous analyses in the future. 

Table 3. Distribution of RT’s per the Alarm Initiated Activity Model (Stanton, 2006) 

AIAMinimumRT(sec)MaximumRT(sec)

Observation12

Acceptance18

Analysis26

Investigation640

MonitoringVariableVariable

Correction780

Total17136 27 

Figure 5. Notional Response Time Distributions 

Table 4. Assignment of Response Time Curves to Conflict Scenario Types 

Activity / 

Source of Conflict 

Crew Readiness 

for Potential 

Conflict 

Competing 

Workload 

Applicable 

Response Time

Model 

Response Times

(Median, 90%) 

in seconds 

Takeoff 

own runway 

Medium High Good 3.0, 4.5 

Landing 

own runway 

High High Good 3.0, 4.5 

Taxi on runway 

(before entry) 

High Low Fast 2.0, 3.0 

Taxi on runway 

(along, crossing) 

Medium Low Good 3.0, 4.5 

Taxi intersection Low Medium Fair 4.0, 6.0 

Taxi following Medium Low Good 3.0, 4.5 

Takeoff/Landing 

parallel runways 

Medium High Fair 4.0, 6.0 

Takeoff/Landing 

other runways 

Low High Slow 5.0, 7.5 

Terminal Area – 

prior to base leg 

Low High Slow 5.0, 7.5 

Notes: 

Best practices in CVI design and training will be required to achieve desired responsiveness. 

Indications that promote situational awareness and aid conflict identification are essential. 
 28 

5.3.2 Surveillance Accuracy Models 

Information on the capabilities and limitations of GPS receivers, assumed to be the primary basis for 

ADS-B outputs, was described in Section 4.2. A reduced set of phenomena were included in the models 

used to generate analysis results. 

Position measurement errors were assumed to be zero-mean Gaussian noise; this simplification grouped 

both lightly- and heavily-damped Gauss-Markov errors as white noise. This model also ignored the 

effects of systematic bias; based on Table 1, these biases are unlikely to be large enough to affect alert 

decisions. The principal model parameters were the standard deviation of position measurement error and 

the standard deviation of velocity error. These values were consistent with the ranges shown in Table 1; 

however, the range used depended on the assumed equipage model, and statistics for a given aircraft 

varied between random trials in Monte Carlo simulations. 

The state information (position, velocity, and acceleration) was initially modeled as the products of a 

process to smooth recent position measurements within a fixed time window. However, GPS receivers are 

also capable of measuring velocity without using multiple position measurements, generally producing 

more accurate velocity measurements as described in Section 4.2. The assumed errors in velocity derived 

in this way can be much smaller than the errors achievable through short-term position smoothing of an 

aircraft moving on the surface. Furthermore, the errors in GPS-derived velocity may be largely 

decorrelated from errors in GPS-derived position, to the extent that the velocity errors are due to noise in 

the measured frequency shift of satellite signals rather than to noise in the measured time delay 

difference. The degree of decorrelation depends on the details of the GPS state estimation process. For 

this study, it was assumed that GPS-derived velocity errors were independent of position errors. For 

simplicity, the models used for simulating surveillance accuracy implement only Gaussian measurement 

errors, representing only a subset of the phenomena affecting accuracy described in Section 4.2. 

Compared to the simplified accuracy model, the root mean square (RMS) estimated state error of a more 

advanced process such as an extended Kalman filter (EKF) could be smaller. However, it is not clear that 

the EKF would be optimum, since the quality of alert decisions may be affected more by the rate of 

unacceptably large errors from the “tails” of the distribution rather than the standard deviation of error. 

5.3.3 Alerting Process Model 

Due to the sensitivity of alerting behavior to the details of the surveillance source (particularly the “tails” 

of error distributions), practical alerting algorithms tend to be designed around a specific surveillance 

source and validated against a large volume of sample data. For the current study, it was judged 

impractical to develop a complete, robust alerting algorithm. Instead, a simplified model of the alerting 

process was used, with features sufficient to show the effect of surveillance accuracy on alerting 

performance. 

In keeping with this approach, it was assumed that any practical alerting system would be subject to the 

constraints listed below. This assessment is based on experience with conflict alert algorithm design for 

ASDE-X and various international A-SMGCS systems. 

Alerting Assumptions 

Alert decisions require the ability to discriminate between permissible and potentially 

hazardous interactions. 

Alert decision thresholds set a ceiling on system effectiveness in two major ways: 

– Limited situation coverage (alerting may be inhibited by design in situations that 

otherwise would generate excessive alerts). 

– Limited warning time (if alerts are delayed until confidence is high enough to meet 

nuisance alert requirements, the probability of a safe outcome may be reduced). 
 29 

Systems must limit total number of all unwanted (false and nuisance) alerts to an acceptable 

rate for certification and operational acceptance by users: 

– A nominal limit on the rate of unwanted alerts of 10-5 per operation2

 was assumed (higher 

rates may be tolerated in some contexts, such as taxi). 

– If standards permit higher rates for less-severe alerts, the concept of “downgrading” an 

alert (to caution or advisory) in cases where geometry or surveillance make the nominal 

rate unachievable3

 could be beneficial (for this study, only a single level of alert severity 

was considered). 

The total unwanted alert rate should be allocated between all contributing causes to derive 

realistic thresholds. However, in order to limit the number of Monte Carlo trials used in this 

initial study, each alert type was tested against a single relatively strict criterion limit 

requiring a nuisance alert rate due to predictable surveillance noise less than 10-5 per modeled 

encounter. Additional work would be required to estimate the encounter rate per operation 

and the combined rate from all sources. 

Alert Categorization 

Unwanted alerts are divided into the following categories for this analysis: 

– Alert caused by equipment failures.4

 

– “Nuisance alert”, here defined as non-beneficial alerts generated during normal 

operations with alert input sources operating within normal bounds, further divided into 

two subcategories: 

o Discrete errors caused by qualitative errors in assessment of situation: 

False targets or false reports (excessive position error). 

Incorrect assessment of current or intended movement state. 

Wrong predicted arrival runway. 

o Parameter errors caused by quantitative errors in alert discriminants: 

Predicted time to enter or clear runway or intersection. 

Predicted position at time of another significant event. 

Acceptable alerts: 

2

 The rate of 10-5 per operation is comparable to the acceptance criteria for unwanted alerts during runway 

operations for ASDE-X safety logic, and is also within an order of magnitude of TCAS Resolution Advisory 

nuisance alert requirements. This value was proposed to RTCA SC-186 as a basis for setting requirements for flight 

deck alerts; however, the draft standard has relaxed this requirement to 10-3 per operation. These values are applied 

during flight situations when the consequence of an unwanted alert are relatively high, possibly leading to an 

unnecessary missed approach or rejected takeoff; therefore, it seems plausible that higher levels of nuisance alerts 

could be tolerated in taxi situations, where the disruption caused by an unnecessary stop is less. 

3

 This approach assumes that the goal is to provide as great a safety benefit as possible for a surveillance source of 

fixed quality. It also assumes that lower-severity alerts (that do not demand immediate operator response) may be 

tolerable at higher rates than warning alerts, and could still provide a safety benefit, for example through improved 

situation awareness. This approach is not completely satisfactory, but may be preferable to the alternative of 

disabling alerting output in situations where the potential danger cannot be confirmed through available surveillance 

at an acceptable unwanted alert rate. 

4

 The term “false alert” is limited to alerts caused by equipment failures, i.e. operation outside design specifications, 

in RTCA SC-186 WG-1 documents. 
 30 

– Tolerable (justified by apparent conflict or procedural irregularity). 

– Necessary (contributes directly to prevention or resolution of real conflict). 

The tolerable unwanted alert rate could depend on the type of alert or indication. (Notional 

values proposed to the RTCA SC-186 WG-1 effort to define requirements for Surface 

Indications and Alerts (SURF IA) for flight deck use were rates of 10-5, 10-4, 10-3, and 10-2 per 

operation for Warning, Caution, Traffic Indication, and Runway Status Indication, 

respectively). 

In order to limit the scope of the current analysis, only single-level (warning) alerting and the resulting 

safety benefits were modeled. It was anticipated that for some scenarios (with worse sensors and more 

stressing timeline), the desired warning time couldn’t be met at a specified unwanted alert rate. By 

measuring the overall CD&R capability with “coverage” metric showing the probability of successfully 

preventing collision over an ensemble of conflict situations, the limitations of each assumed NextGen 

capability will be shown in a consistent way. However, the analysis performed only addressed the 

probability of unwanted alerts due to parametric error in situation discriminants. It would be necessary to 

limit discrete errors (incorrect identification of the situation) to a comparable or lower level to achieve the 

desired unwanted alert rate. 

6. Model Results 

6.1 Common Simulation Elements 

6.1.1 Trajectory Models 

Aircraft were modeled along their primary axis of horizontal motion. Lateral deviations were assumed to 

be small and not relevant to the alerting process. The vertical profile (for landing aircraft, for example) 

was not modeled, as the modeled alerting process does not make use of it. It should be noted that to the 

extent that the measured height of the aircraft could be used to predict parameters such as touchdown 

position, it is possible that the alerting process could be improved relative to the results presented here. 

Motion along the primary axis was modeled as a series of constant-acceleration segments. For example, a 

landing aircraft approaching at constant velocity, after touchdown, brakes with constant deceleration, and 

upon reaching a final rollout speed, taxis with constant velocity (this model is discussed in greater detail 

in Section 6.2.2). The constant-acceleration model, though not realistic, was sufficient to illustrate the 

features of alerting systems and the dependence on various parameters. 

6.1.2 Aircraft Extent 

The physical extent of each aircraft was modeled with three parameters: length, wingspan, and the 

position of the wings along the length of the aircraft. The aircraft was modeled as two line segments – 

one representing the fuselage and a perpendicular one representing the wings, as shown in Figure 6. 

Aircraft traveling on the same path collided if their fuselage line segments overlapped. When two 

aircraft’s paths intersected, an intersection region on each path was defined by the area swept out by one 

aircraft’s wing as it crossed the other’s path. For the time interval that any part of the aircraft (nose, tail, 

and/or wingtip) was in the intersection region, that aircraft was considered to be in the intersection. A 

collision was assumed to occur if both aircraft occupied the intersection at the same time, as illustrated in 

Figure 7. 

Encounters were simulated with a variety of real aircraft parameters. In each encounter, the type for each 

aircraft was randomly chosen (based on the assumed proportion of each type) and the appropriate size 

parameters were assigned. 
 31 

Figure 6. Model for Aircraft Extent 

Figure 7. Definition of Collision Potential 
 32 

Additionally, the position of the transponder was defined along the aircraft fuselage. Measurements were 

modeled as unbiased estimates of the transponder position, rather than of the aircraft centroid; it was 

assumed that all alerting systems modeled the asymmetric displacement of nose and tail positions relative 

to the transponder. From the measured transponder position and the aircraft size, the measured nose, tail, 

and/or wingtip positions were calculated. From these, the separation between, for example, a lead 

aircraft’s tail and a following aircraft’s nose may be calculated. 

It was assumed that, in the NextGen environment, aircraft will transmit accurate information about their 

physical size. Therefore, the alerting system used the true size parameters to evaluate the separation 

between aircraft. Uncertainty in the exact position of the transponder on each aircraft was accounted for 

by adding an additional 5 m to the modeled uncertainty in all position measurements. 

6.1.3 Measurements 

It was assumed that position and velocity measurements will be derived primarily from GPS. The data 

was generated once per second, with each aircraft performing measurements at the same time. 

Position measurements were assumed to have a 5-meter, Gaussian-distributed error. Each measurement 

was generated by adding to the true position an error randomly drawn from a Gaussian distribution with 5 

m standard deviation. As such, 95% of measurements were within 10 m of the true position. Variations 

in the standard deviation parameter were also explored, but unless otherwise noted a value of 5 m was 

used. 

It was assumed that a velocity measurement was also available from the GPS receiver, with negligible 

correlation between position and velocity errors, as discussed in Section 5.3.2 (both position and velocity 

are still derived from the same surveillance subsystem). As this represents a significant accuracy 

improvement over the velocity than can be derived from smoothing position, the effect of removing the 

assumed independent velocity source was examined in sensitivity analyses discussed later in Section 

6.2.7.3. The velocity measurement was modeled by adding a Gaussian error to the true velocity. The 

standard deviation is 0.03 m/s for Transport aircraft (both New and Retrofit) and 0.10 m/s for NonTransport. 

Any alerting system resides either on-board an aircraft, or on the ground. A system must receive reports 

via ADS-B for all aircraft other than ownship (or for all aircraft in the case of a ground-based system). 

On-board systems use the source measurement model for ownship, and the reported measurement model 

for all other aircraft. The ground-based system uses the reported measurement model for all aircraft. 

The ADS-B reporting process was modeled as follows. A random time delay uniformly distributed 

between 0 and 1 second was added between the time of the measurement and the reporting of that 

measurement to others. The system receiving the measurement knows the measurement time, but cannot 

act on it until the later time when the report was issued. Furthermore, the Mode S data link assumed for 

baseline ADS-B data provides only coarsely quantized reporting of measurement uncertainties. The 

quantization was modeled for reported measurements by using only values of 0.3 m, 1 m, 3 m, 10 m, 30 

m, etc. for position uncertainty or 0.01 m/s, 0.03 m/s, 0.1 m/s, 0.3 m/s, 1 m/s, etc. for velocity uncertainty 

to be reported. The true uncertainty was rounded up to the next highest allowable value, and this higher 

value was reported. All other aircraft used the higher uncertainty. 

6.1.4 Position and Velocity Estimation 

The current state estimate was computed using algorithms based on fitting curves to measurements 

received over a fixed duration. A time window of 10 seconds was chosen based on experience with 

aircraft trajectory prediction in the airport environment. The window estimation method was used because 

the implementation is simple, the error characteristics are well-defined, and because it tends to be robust 

when the properties of the input measurement error distribution are uncertain, particularly the length of 
 33 

error distribution “tails” and the correlation of errors in time. A fit to all measurements within the 

assumed time window was used to generate the estimate of the current position and velocity used in the 

simulated conflict detection algorithm. 

When it was assumed that an aircraft was traveling at constant velocity, a linear fit to position 

measurements was performed as a function of time, and from this fit, the current position and its 

uncertainty were computed. The fitted position has a smaller uncertainty than the individual 

measurements. The last measured velocity and its uncertainty was used as the estimated current velocity. 

When an aircraft had non-zero acceleration, position and its uncertainty were estimated with a quadratic 

fit. Current velocity and its uncertainty were estimated with a linear fit to the measured velocity as a 

function of time. As discussed in Section 5.3.2, the simple model was considered to be sufficiently 

representative of the expected state estimation performance; an alternate estimation technique such as an 

Extended Kalman Filter would have required greater development effort and might well prove less robust 

in operational conditions. 

6.1.5 Projected Quantities 

The alerting algorithms required projections, such as the time margin between when two aircraft would 

occupy an intersection, or the smallest separation two aircraft would have in the future. These projected 

quantities were compared to thresholds to determine whether an alert should be issued. The projection 

computation was assumed to be applied in the same way regardless of the measurement model (source or 

reported measurements), as the projection used the provided uncertainty parameters. 

Calculation of projected quantities always included calculation of the uncertainties on those projections. 

The uncertainties came from two types of sources: measurement and assumed future behavior. For 

example, to predict when a taxiing aircraft will clear a hold line the uncertainty on the current estimated 

position and velocity needed to be accounted for, as well as the possible magnitude of future speed 

fluctuations. Often the uncertainties due to assumed future behavior are larger than those due to 

measurement; in these cases, improved information about pilot intent would be more valuable than 

improved measurement of the current state. 

6.2 Scenario 1: Taxi Crossing Arrival Runway 

6.2.1 Overview 

Figure 8 shows a diagram of the scenario. An aircraft taxied across an active runway at an intersection a 

variable distance from runway threshold (from 0 to 1000 m) while an arriver was on approach. The taxier 

was required by rule to clear the exit hold line before the arriver’s nose crossed threshold. However, the 

hold line provided sufficient margin that a small violation posed minimal risk of collision. The hold lines 

were assumed to be 85.34 meters from the centerline of the runway. 

In this Section, it is shown that an alerting system can prevent almost all collisions if it provides alerts to 

both aircraft or to the taxier. Alerts issued to the arriver were less effective; alerts could not be issued 

before the taxier is sure to cross the entry hold line, but due to a blunder this may happen after the arriver 

has crossed threshold and can neither go around nor stop before the intersection. 
 34 

Hold lines

Runway

Threshold tthres

tclear

280ft

Arriver

Taxier

Figure 8. Taxi Crossing Arrival Runway Scenario 

6.2.2 Scenario Model 

The taxier was modeled with a constant-velocity trajectory along the center of the taxiway. Position and 

velocity measurements were simulated as the true value plus a random error, as discussed in Section 

6.1.3. Figure 9 shows these measurements for a single encounter. 

The arriver was modeled as a series of three phases, shown in Figure 10. The first phase was a constantspeed final approach. When the aircraft reached the touchdown point, the braking phase began. Braking 

was modeled as constant deceleration. Finally, upon reaching a target rollout speed (rollout phase), 

motion was again constant-velocity. Speed and deceleration parameters were chosen randomly for each 

encounter, but remained constant throughout each individual encounter. 
 35 

-50 -40 -30 -20 -10 0 10 20 30 40 -300

-200

-100

0

100

200

300

400

Time (sec)

Tail Position of Taxier (meters)

Measured position (reported)

True position

Hold line at exit

Taxier clears hold line

Arriver at threshold

Taxier enters intersection

Taxier exits intersection

Arriver enters intersection

Arriver exits intersection

-50 -40 -30 -20 -10 0 10 20 30 40 7

7.1

7.2

7.3

7.4

7.5

7.6

7.7

7.8

Time (sec)

Taxier Speed (m/s)

Estimated speed

True speed

Taxier clears hold line

Arriver at threshold

Taxier enters intersection

Taxier exits intersection

Arriver enters intersection

Arriver exits intersection

Figure 9. Measurements of Taxier’s Position (top) and Speed (bottom) as a Function of Time for a 

Single Encounter 
 36 

Time

Speed

Touchdown

(@ dtouch ~ 350 m

from threshold)

Stop braking

(@ vrollout)

Final

approach

Braking

Rollout

vfinal = 58-82 m/s

vrollout = 15-20 m/s

abrake = -1.1 to -1.9 m/s2

Figure 10. Arriver Trajectory Model 

Although the arrival model made the simplifying assumption that braking deceleration was constant, it 

was sufficient to demonstrate the significant features of this scenario and to evaluate the effectiveness of a 

feasible alerting system. As discussed below, the alerting system does not rely on the assumptions of 

constant speed deceleration; it allows for fluctuations in these values when computing the uncertainties on 

its estimates. 

Position and velocity measurements were simulated as the true value plus a random error, as discussed in 

Section 6.1.3 (and identical to the process for the taxier). Figure 11 shows position measurements and 

velocity estimates derived from the individual measurements for a single encounter. 
 37 

-50 -40 -30 -20 -10 0 10 20 30 40 -3000

-2500

-2000

-1500

-1000

-500

0

500

1000

1500

2000

Time (sec)

Nose Position of Arriver (meters)

Measured position (ownship)

True position

Runway threshold

Taxier clears hold line

Arriver at threshold

Taxier enters intersection

Taxier exits intersection

Arriver enters intersection

Arriver exits intersection

-50 -40 -30 -20 -10 0 10 20 30 40 25

30

35

40

45

50

55

60

65

70

Time (sec)

Arriver Speed (m/s)

Estimated speed

True speed

Taxier clears hold line

Arriver at threshold

Taxier enters intersection

Taxier exits intersection

Arriver enters intersection

Arriver exits intersection

Figure 11. Measurements of Arriver’s Position (top) and Estimates of its Speed (bottom) as a 

Function of Time for a Single Encounter 
 38 

6.2.3 Collision Risk With and Without an Alert 

The collision risk in each encounter was modeled if no alert was issued or if one or both aircraft received 

alerts. These risk values were used to evaluate the effectiveness of an alerting system (the fraction of 

collisions that it prevented). 

Collision risk in the absence of an alert was determined by the scenario parameters, which defined the 

trajectories of the two aircraft. If both aircraft were in the intersection at the same time, a collision was 

very likely. If they never occupied the intersection at the same time, a collision was very unlikely. 

The intersection was defined previously in Section 6.1.2 as follows: for each aircraft, the wingtips swept 

out two parallel lines, and the wings swept out the region between those lines. The intersection was 

where the regions for the two aircraft overlapped, as illustrated previously in Figure 7. An aircraft 

entered the intersection when its nose, wingtip, or tail entered this overlap region, and it exited when 

every part of it cleared the region. 

A safety distance margin was defined as Aircraft A’s distance of closest point of approach to the 

intersection while Aircraft B occupied it, or vice versa. This margin was positive if A stayed clear while 

B was inside, and negative otherwise, as shown in Figure 12. Large positive values indicated a large 

safety margin, and negative values indicated that a collision was nearly certain. 

The transition from near-certainty of collision to near-certainty of safety was captured with a collision 

risk function that depended on the safety distance margin. This function was introduced to represent the 

transition from safe to unsafe separation in a way that reduces artificial edge effects in a computationally 

simple way. Beginning the transition from zero risk to finite risk at a margin of 10 meters is consistent 

with past FAA practice in configuring safety logic, where separations less than 35 feet were considered 

unsafe. The remaining points were chosen based on the assumption that the risk of collision is not 100% 

at the first contact between the rectangular outlines chosen to represent aircraft extent. Also, it represents 

the possibility of deviations from the assumed motion model (constant-speed or constant-acceleration 

depending on the scenario) to represent extreme evasive maneuvers. Figure 13 shows the function with 

the parameters used for all scenario models. At very negative values, the probability of collision was 

100%, while at large positive values it was 0%. Between these regimes there was a smooth transition 

with a collision probability between 0 and 1. 

Figure 12. Definition of Safety Distance Margin 
 39 

The collision risk for a particular encounter was determined by first calculating the safety distance margin 

based on the aircraft trajectories (as determined by the scenario parameters), and then calculating the 

collision risk from the function in Figure 13. 

-30 -20 -10 0 10 20 30

0

0.2

0.4

0.6

0.8

1

Distance margin at closest approach (m)

Collision risk

Collision Risk vs. Distance Margin

Figure 13. Collision Risk as a Function of Safety Distance Margin, Capturing the Transition from 

Zero Collision Risk to Near-certainty of Collision 

When an alert was issued, collision risk was determined from the scenario parameters and the time at 

which the alert was issued. An alert issued by the ground system was first delayed by a latency of 0.5 

seconds to account for transmission to the aircraft, whereas, an on-board alert suffered no delay before it 

was presented in the cockpit. After the alert was presented, a random pilot response time was added. 

This time was drawn at random from a log normal distribution, as discussed in 5.3.1. After the response 

time had passed, the pilot began a resolution maneuver. The probability of success depended on the type 

of maneuver, the time at which it began, and the other parameters of the scenario. 

An alert issued to the taxier may instruct the pilot to either halt before the intersection or to expedite the 

crossing in order to exit sooner. To evaluate the value of an alert, the collision risk was calculated under 

either action. The trajectory was modified such that the pilot initiated either braking or acceleration at the 

time when the response began, and collision risk was calculated using the two alternative trajectories. 

The system and pilot were assumed to choose the action that produced the lowest collision risk; the 

lowest risk was then considered to be the outcome of that scenario. 

An alert to the arriver may occur in either of two phases. If it is given after touchdown, it orders the pilot 

to brake to a stop as soon as possible. The effectiveness of such an alert was evaluated in the same way 

as for the taxier. Braking began after the response time had elapsed, and collision risk was calculated 

using the modified trajectory. If the alert was given before the aircraft crossed the threshold, it was 

presumed to trigger a go-around. The probability of success was taken to be a function of the time when 

pilot response began. If the go-around was initiated well before threshold, it was nearly certain to 

successfully avoid the collision. After threshold, a go-around alert had very little value. In between was 

a transition. The go-around procedure was modeled with the function shown in Figure 14. The collision 

risk was the product of the alert value determined from this function and the collision risk in the absence 

of an alert. The role of vertical separation in preventing collision was not addressed explicitly, but the 
 40 

effect was folded into the dependence of successful resolution on time before threshold when resolution 

action began (after the modeled pilot reaction time has elapsed). 

-5 0 5 10 15

0

0.2

0.4

0.6

0.8

1

Go-around Time Margin (threshold time - go-around initiation time) (sec)

Alert value

Alert Value vs. Go-around Time Margin

Figure 14. Value of Alert Ordering a Go-around, as a Function of the Time when Pilot Reaction 

Begins

The value of an alert was defined as the probability that it averted a collision if one could occur. It was 

calculated as: 

Alert value = 1 – (probability of collision with alerting system)/(probability of collision 

without alerting system) 

Then the probability of collision was: 

(Probability of collision with alerting system) = (1- alert value)*(probability of collision 

without alerting system) 

If both aircraft received alerts, then a collision occurred only if both alerts failed to produce a successful 

avoidance maneuver. Assuming the success of two aircraft’s maneuvers were independent, then 

(Probability of collision with alerting system) = (1- taxier alert value)*(1 - arriver alert 

value)*(probability of collision without alerting system) 

6.2.4 Projected Times and Margins 

The alerting system required projection of future states from the current state, and estimates of the 

uncertainties on the future states. In this section the process used to project future states is described. 

Projections suffer from two types of uncertainties, as discussed in Section 6.1.5. First, uncertainties in 

measurements of the current state propagate into the estimates of future state. Second, no measurement 

can predict future pilot behavior, and so the model used to project future states may not be accurate. 

For example, consider an arriver currently on final approach. The aircraft’s current position and velocity 

are measured with some uncertainty. To predict, for example, the time when the aircraft reaches 

threshold, the average speed until that time must be projected. Since speed may fluctuate, that average 

speed will have a higher uncertainty than the uncertainty on the current measured speed. Therefore, when 
 41 

estimating time to threshold, an uncertainty on speed was included that accounted for the typical 

magnitude of speed fluctuations. 

To project the time when the arriver will enter an intersection located some distance from threshold, it 

must be assumed where the aircraft will touch down and how hard it will brake. Since these quantities are 

not known, the estimate of the time entering the intersection will be dominated by their uncertainty. 

Although the truth-level trajectories feature constant velocity or acceleration, the future state projection 

process did not assume such simplicity. The uncertainties include the possibility of fluctuations in speed 

or acceleration, and uncertainty of future pilot behavior. 

As each new measurement was received, projected times of different events for each aircraft were 

calculated. For the taxier, the time of three events was calculated: nose entering intersection, tail exiting 

intersection, and tail clearing exit hold line. For the arriver, the time of three events was also calculated: 

nose crossing threshold, nose entering intersection, and tail exiting intersection. These estimates are 

shown as a function of time in Figures 15a-15c . For each estimate, the measurements converged on the 

true value (shown as one of the horizontal lines), and the error bars cover a range that includes the true 

value. The uncertainties (shown by the error bars) decreased as the event time occurred; that is, the 

estimates were more precise when projected over a shorter period of time. 

At later times, more information was available and the projections occurred over shorter time spans, so 

estimates were more precise (as indicated by the smaller error bars). Plots on the left are for the taxier; 

from top they are projections of intersection entry time, intersection exit time, and time clearing the exit 

hold line. Plots on the right are for the arriver; from top they are projections of time crossing threshold, 

intersection entry time, and intersection exit time. 

The projected times were used to determine the time margin between events. For example, a rules 

violation occurred if the arriver’s nose crossed threshold before the taxier’s tail cleared the exit hold line. 

Therefore, a predicted “rules margin” was calculated as the time difference between these events. A 

positive margin indicated that the taxier cleared in time, and no rules were violated. A negative margin 

indicated a rules violation. Figure 16 shows an example of how the rules margin estimate evolved, 

increasing in precision over time. The alerting algorithm used the estimated rules margin, and its 

uncertainty, to determine whether it was likely that the encounter violated an operational rule. 
 42 

Figure 15a. Projected Times of Taxier Clearing Intersection, and Arriver Crossing Threshold, as a 

Function of the Time of Estimation 

-50 -40 -30 -20 -10 0 10 20 30 40 -20

-15

-10

-5

0

5

10

15

Time (sec)

Predicted Clear Time (sec)

Predicted Clear Time vs. Time

Predicted Clear Time

Taxier clears hold line

Arriver at threshold

Taxier enters intersection

Taxier exits intersection

Arriver enters intersection

Arriver exits intersection

-50 -40 -30 -20 -10 0 10 20 30 40 -20

-15

-10

-5

0

5

10

15

Time (sec)

Predicted Time at Threshold (sec)

Predicted Time at Threshold vs. Time

Predicted Time at Threshold

Taxier clears hold line

Arriver at threshold

Taxier enters intersection

Taxier exits intersection

Arriver enters intersection

Arriver exits intersection
 43 

Figure 15b. Projected Times of Taxier Entering and Exiting Intersection, as a Function of the Time 

of Estimation 

-50 -40 -30 -20 -10 0 10 20 30 40 -25

-20

-15

-10

-5

0

5

10

15

Time (sec)

Predicted Time when Taxier Enters Intersection (sec)

Predicted Time when Taxier Enters Intersection vs. Time

Predicted Time when Taxier Enters Intersection

Taxier clears hold line

Arriver at threshold

Taxier enters intersection

Taxier exits intersection

Arriver enters intersection

Arriver exits intersection

-50 -40 -30 -20 -10 0 10 20 30 40 -20

-15

-10

-5

0

5

10

15

Time (sec)

Predicted Time when Taxier Exits Intersection (sec)

Predicted Time when Taxier Exits Intersection vs. Time

Predicted Time when Taxier Exits Intersection

Taxier clears hold line

Arriver at threshold

Taxier enters intersection

Taxier exits intersection

Arriver enters intersection

Arriver exits intersection
 44 

Figure 15c. Projected Times of Arriver Entering and Exiting Intersection, as a Function of the 

Time of Estimation 

-50 -40 -30 -20 -10 0 10 20 30 40 -20

-15

-10

-5

0

5

10

15

Time (sec)

Predicted Time when Arriver Enters Intersection (sec)

Predicted Time when Arriver Enters Intersection vs. Time

Predicted Time when Arriver Enters Intersection

Taxier clears hold line

Arriver at threshold

Taxier enters intersection

Taxier exits intersection

Arriver enters intersection

Arriver exits intersection

-50 -40 -30 -20 -10 0 10 20 30 40 -20

-15

-10

-5

0

5

10

15

Time (sec)

Predicted Time when Arriver Exits Intersection (sec)

Predicted Time when Arriver Exits Intersection vs. Time

Predicted Time when Arriver Exits Intersection

Taxier clears hold line

Arriver at threshold

Taxier enters intersection

Taxier exits intersection

Arriver enters intersection

Arriver exits intersection
 45 

-50 -40 -30 -20 -10 0 10 20 30 40 -1

0

1

2

3

4

5

6

7

Time (sec)

Rules Margin (sec)

Predicted Rules Margin

True Rules Margin

Taxier clears hold line

Arriver at threshold

Taxier enters intersection

Taxier exits intersection

Arriver enters intersection

Arriver exits intersection

Figure 16. Rules Margin Estimates as a Function of Estimation Time 

As time progressed the estimates became more precise. In this example, the taxier cleared the hold line 

3.2 seconds before the arriver crossed threshold, as indicated by the horizontal dashed line. 

Also significant to the alerting algorithm is the question of whether the two aircraft were likely to be in 

the intersection at the same time. For this purpose, two safety margins were evaluated. The first tested 

whether the taxier exited the intersection before the arriver entered. For this test, a safety margin was 

computed that was the difference between the projections of these two times. The second test was for the 

alternative hypothesis that the arriver exited before the taxier entered. A second safety margin was 

computed from these projected times. If either of the two margins was positive, then no collision 

occurred. If both were negative, then both were in the intersection at the same time, and a collision was 

likely. Therefore, the alerting algorithm issued an alert if there was significant risk that both were 

negative. 

In the example shown in Figure 17, the arriver entered the intersection about 24 seconds after the taxier 

exited (top plot), indicating that there was no collision risk. The bottom plot, showing the safety margin 

under the false assumption that the arriver preceded the taxier, shows a negative margin, but a safe 

operation requires only that one of the two margins is positive. In a high-risk encounter, both margins 

would be negative. 
 46 

-50 -40 -30 -20 -10 0 10 20 30 40 0

5

10

15

20

25

30

Time (sec)

Safety Margin assuming arriver after taxier (sec)

Safety Margin assuming arriver after taxier

Predicted Safety Margin assuming arriver after taxier

True Safety Margin assuming arriver after taxier

Taxier clears hold line

Arriver at threshold

Taxier enters intersection

Taxier exits intersection

Arriver enters intersection

Arriver exits intersection

-50 -40 -30 -20 -10 0 10 20 30 40 -40

-35

-30

-25

-20

-15

-10

-5

0

Time (sec)

Safety Margin assuming taxier after arriver (sec)

Safety Margin assuming taxier after arriver

Predicted Safety Margin assuming taxier after arriver

True Safety Margin assuming taxier after arriver

Taxier clears hold line

Arriver at threshold

Taxier enters intersection

Taxier exits intersection

Arriver enters intersection

Arriver exits intersection

Figure 17. Safety Margin Estimates as a Function of Estimation Time 
 47 

6.2.5 Alerting Algorithm 

The alerting algorithm compared various measured and projected quantities to threshold values, and 

issued an alert if all quantities passed those thresholds. The quantities and thresholds were chosen to 

satisfy three broad requirements: 

An alert would be issued only if the potential threat required a prompt avoidance maneuver, 

and if that maneuver was feasible. For example, an arriver would almost certainly execute a 

go-around if the maneuver began at least ~30 seconds from threshold. Therefore, there was 

no need to issue alerts before that time. At the other end of the range, there was no reason to 

issue a go-around alert after touchdown. 

An alert would be issued only if there was high confidence that an operational rule had been 

violated. This translated to a requirement that there was high confidence that the rules 

margin was negative. 

An alert would be issued only if there was a significant risk of collision. Here “significant” 

meant that the chance of collision was greater than, say, one or a few percent. This means 

that an alert was not issued unless there was high confidence that at least one of the two 

safety margins was positive. 

Figures 18a-18d show the alert quantities and thresholds for an example encounter in which an alert was 

generated for the arriver during final approach. 

Note that this encounter is different from the ones used to generate earlier plots, as it features a higher 

collision risk. The red X’s mark times when an alert was issued (because all quantities passed their 

respective thresholds). The top plot of Figure 18a shows that the taxier was past the entry hold line, or 

could not stop before it. The bottom plot shows that the taxier had not yet passed the intersection exit. 

The next two plots (Figure 18b) show that the arriver was less than 30 seconds from threshold but not yet 

past threshold. The next two (Figure 18c) show that the two aircraft were in the intersection at the same 

time. The final plot (Figure 18d) shows that a rules violation was likely (that the taxier will fail to clear 

the exit hold line before the arriver crosses threshold). 

The requirements are formulated in a way that incorporates the uncertainties on the parameters. For the 

rules margin and safety margin requirements, the margin value was normalized by dividing by its 

estimated uncertainty. The first requirement for generating an alert was that the scaled rules margin was 

less than -2, indicating that the system was confident that a rules violation was occurring. The second 

requirement was that the scaled safety margin was less than +2, indicating that the system was not

confident that collision risk was insignificant. That is, the system suppressed the alert only if it was 

confident that there was a positive safety margin. These criteria were chosen as a simple model of alert 

behavior constrained by unwanted alert requirements; the parameters assumed would be modified in an 

operational system to meet specified design criteria. 

An alert could also be issued to the arriver after touchdown. After touchdown, the alert criteria were the 

same except that the position was required to be after, rather than before, threshold. It was determined 

whether the aircraft had touched down by measuring weight on wheels. If an alert was generated after 

threshold but before touchdown, it was not presented in the cockpit until 1 second after touchdown 

(whether the alert was generated on-board or by an external system). 

For the taxier, the same alert criteria were used, except that there was no requirement on the arriver’s 

position relative to runway threshold. An alert may be issued to the taxier at any point after the arriver 

was 30 seconds from threshold and before it passed through the intersection. 

For the arriver, the most significant requirement affecting alert effectiveness was the requirement that the 

taxier could not stop before the entry hold line. If the intersection was some distance down the runway, a 
 48 

blundering taxier could cross this line after the arriver had touched down and could no longer go around, 

but before the arriver reached the intersection. In this case the arriver could receive an alert to halt, but if 

the intersection was not too far down-runway there may not be enough distance to stop. In these 

scenarios, an alert issued to the arriver would not be effective. This is discussed further below. 

Figure 18a. Alert Quantities, Thresholds, and Values for an Example Encounter 

-50 -40 -30 -20 -10 0 10 20 30 -200

-100

0

100

200

300

400

Time (sec)

Distance Across Entry Hold Line of Stopping Point (m)

Distance Across Entry Hold Line of Stopping Point (m)

Distance Across Entry Hold Line of Stopping Point (m)

Alert threshold

Alert issued

Alert not issued, but this variable passed threshold

Alert not issued

Taxier clears hold line

Arriver at threshold

Taxier enters intersection

Taxier exits intersection

Arriver enters intersection

Arriver exits intersection

-50 -40 -30 -20 -10 0 10 20 30 -400

-300

-200

-100

0

100

200

Time (sec)

Measured Distance of Taxier Beyond Intersection Exit (m)

Measured Distance of Taxier Beyond Intersection Exit (m)

Measured Distance of Taxier Beyond Intersection Exit (m)

Alert threshold

Alert issued

Alert not issued, but this variable passed threshold

Alert not issued

Taxier clears hold line

Arriver at threshold

Taxier enters intersection

Taxier exits intersection

Arriver enters intersection

Arriver exits intersection
 49 

Figure 18b. Alert Quantities, Thresholds, and Values for an Example Encounter

-50 -40 -30 -20 -10 0 10 20 30 -30

-20

-10

0

10

20

30

40

Time (sec)

Predicted Time to Threshold (sec)

Predicted Time to Threshold (sec)

Predicted Time to Threshold (sec)

Alert threshold

Alert issued

Alert not issued, but this variable passed threshold

Alert not issued

Taxier clears hold line

Arriver at threshold

Taxier enters intersection

Taxier exits intersection

Arriver enters intersection

Arriver exits intersection

-50 -40 -30 -20 -10 0 10 20 30 -3000

-2500

-2000

-1500

-1000

-500

0

500

1000

1500

Time (sec)

Measured Distance Beyond Threshold (m)

Measured Distance Beyond Threshold (m)

Measured Distance Beyond Threshold (m)

Alert threshold

Alert issued

Alert not issued, but this variable passed threshold

Alert not issued

Taxier clears hold line

Arriver at threshold

Taxier enters intersection

Taxier exits intersection

Arriver enters intersection

Arriver exits intersection
 50 

Figure 18c. Alert Quantities, Thresholds, and Values for an Example Encounter 

-50 -40 -30 -20 -10 0 10 20 30 -2

-1.5

-1

-0.5

0

0.5

1

1.5

2

Time (sec)

Predicted Safety Margin / Uncertainty assuming arriver after taxier

Predicted Safety Margin / Uncertainty assuming arriver after taxier

Predicted Safety Margin / Uncertainty assuming arriver after taxier

Alert threshold

Alert issued

Alert not issued, but this variable passed threshold

Alert not issued

Taxier clears hold line

Arriver at threshold

Taxier enters intersection

Taxier exits intersection

Arriver enters intersection

Arriver exits intersection

-50 -40 -30 -20 -10 0 10 20 30 -18

-16

-14

-12

-10

-8

-6

-4

-2

0

2

Time (sec)

Predicted Safety Margin / Uncertainty assuming taxier after arriver

Predicted Safety Margin / Uncertainty assuming taxier after arriver

Predicted Safety Margin / Uncertainty assuming taxier after arriver

Alert threshold

Alert issued

Alert not issued, but this variable passed threshold

Alert not issued

Taxier clears hold line

Arriver at threshold

Taxier enters intersection

Taxier exits intersection

Arriver enters intersection

Arriver exits intersection
 51 

Figure 18d. Alert Quantities, Thresholds, and Values for an Example Encounter

6.2.6 Nuisance Alert Rate 

The nuisance alert rate was evaluated in an ensemble of encounters taken to represent normal operations. 

In this ensemble, it was assumed that the controller issued a clearance for the taxier to cross the runway, 

aiming to achieve a 5-second margin between the taxier clearing the exit hold line and the arriver crossing 

threshold. The actual margin varied due to uncertainties in the taxier’s response time in complying with 

the clearance, the taxier’s actual speed, and the arriver’s actual time at threshold. Figure 19 shows the 

distribution of the actual “rules margin” (left plot). Note that this assumed ensemble contained an 

unrealistically high fraction of encounters in which a rules violation occurred. This was actually a more 

demanding test of the nuisance alert rate. If the rate was acceptably low in this ensemble, it would be 

even lower in a more realistic one. 

The actual safety margin is a function of the rules margin and the distance from runway threshold to the 

intersection; its distribution in the ensemble is also shown in Figure 19 (right plot). Even though many 

encounters included a rules violation, none of them were severe enough to pose a collision risk. 

An ensemble of 400,000 encounters was simulated with a ground-based alerting system. None of these 

encounters generated an alert. Based on a binomial distribution, this shows that the nuisance alert rate 

was less than 0.9 x 10-5 at 95% confidence. Thus it has been validated that the nuisance alert rate was 

acceptably low for the ground-based alerting system. While this test was not performed for on-board 

alerts, there is no reason to suspect a significant difference. 

-50 -40 -30 -20 -10 0 10 20 30 -18

-16

-14

-12

-10

-8

-6

-4

-2

0

Time (sec)

Predicted Rules Margin / Uncertainty

Predicted Rules Margin / Uncertainty

Predicted Rules Margin / Uncertainty

Alert threshold

Alert issued

Alert not issued, but this variable passed threshold

Alert not issued

Taxier clears hold line

Arriver at threshold

Taxier enters intersection

Taxier exits intersection

Arriver enters intersection

Arriver exits intersection
 52 

-10 -5 0 5 10 15 20 25 30 35 40 0

1

2

3

4

5

6

x 104

Rules Margin (sec)

Encounters / (1 second)

Rules Margin Distribution for Ensemble

No Rules Violation

Rules Violation

-10 -5 0 5 10 15 20 25 30 35 40 0

0.5

1

1.5

2

2.5

3

x 104

Safety Margin (sec)

Encounters / (1 second)

Safety Margin Distribution for Ensemble

Safe

Unsafe

Figure 19. Rules Margin (left) and Safety Margin (right) Distributions for Ensemble used to 

Evaluate Nuisance Alert Rate 

6.2.7 Alert Effectiveness 

The effectiveness of the alerting system was evaluated by comparing collision rates with and without the 

alerting system, as discussed in Section 6.2.3. 

Collision risk was primarily determined by the time when the taxier initiated the crossing, relative to the 

arriver’s landing time. A blunder must occur at “just the wrong time” to result in a collision. This is 

illustrated in Figure 20, which shows collision risk as a function of crossing initiation time for a given 

distance (500 m) from runway threshold to taxiway. The uppermost, red line (with triangles) shows the 

collision risk without an alerting system. For a range of blunder times, collision was very likely, and 

outside that range collision was very unlikely. The other lines in the plot show the collision risk with 

alerts issued to the arriver, taxier, or both. 

Effectiveness depended strongly on whether alerts were issued to the arriver, taxier, or both, and on the 

distance from runway threshold to the intersection. As shown in Figure 21 for the 500 m threshold-tointersection case, alerts to the taxier were very effective, and alerts to the arriver only were ineffective 

when the blundering taxier began the crossing at a later time relative to arriver crossing threshold (leaving 

too little time to execute a missed approach). In Figure 21, these conditions are further highlighted 

showing the fraction of collisions averted by the alerting system as a function of who received alerts and 

of the distance to the intersection. 

Alerts issued to the arriver were not effective at intermediate distances (~800 m). In these encounters, the 

taxier crossed the entry hold line too late for a go-around alert, but the arriver could not brake to a stop 

before the intersection. At smaller distances, the taxier must, in order to be a danger, begin the crossing 

while the arriver was still airborne and capable of going around. At larger distances, the arriver can brake 

to an emergency stop after touchdown, stopping before the intersection. 

Alerts issued to the taxier were effective at all distances, as they could instruct a blundering taxier to halt 

before entering the intersection. Even if the taxier was past the entry hold line, it could still halt prior to 

entering the intersection. Such an event would technically be classified as a runway incursion, but it 

would pose little collision risk. 

Figure 21 shows that alerts were more effective when issued on-board than when issued by a groundbased system. The main reason for this is that the ground-based system incurred additional latency for 

transmission of measurements from aircraft to ground, evaluation of the alert criteria, and transmission of 

alerts from ground to aircraft. With this latency, alerts would be presented later in the cockpit. 
 53 

-40 -30 -20 -10 0 10 20 30

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time When Taxier Crosses Entry Hold Line (sec)

Collision risk

Collision risk vs. Time When Taxier Crosses Entry Hold Line (sec)

Without alerting system

With arriver alert only

With taxier alert only

With on-board alerts to both

With ground system alert

Figure 20. Collision Risk as a Function of Time when Taxier Crosses Entry Hold Line 

0 100 200 300 400 500 600 700 800 900 1000

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Distance from Threshold to Intersection (m)

Fraction of collisions averted

Fraction of collisions averted vs. Distance from Threshold to Intersection (m)

With arriver alert only

With taxier alert only

With on-board alerts to both

With ground system alert

Figure 21. Collision Risk as a Function of Distance from Threshold to Intersection 
 54 

6.2.7.1 Dependence on Response time 

Response times were modeled as a random draw from a log normal distribution for each aircraft. The 

arriver’s response time distribution had a median of 3 seconds, and the taxier’s had a median of 2.5 

seconds. 

To explore the sensitivity of alert effectiveness to response time, all response times were halved and 

doubled and re-evaluated for alert effectiveness. Table 5 shows the fraction of collisions averted as 

response time varies. The results were sensitive to response time, especially for alerts issued to the 

arriver. 

Table 5. Fraction of Collisions Averted by Alerting as a Function of Response time 

Responsetimesx1⁄2BaselineResponsetimesx2

ArriverAlertOnly54.8±1.2%44.3±1.0%27.1±0.9%

TaxierAlertOnly100.0±0.0%99.8±0.1%86.8±0.9%

GroundBasedAlertstoBoth99.9±0.1%97.3±0.4%70.6±1.1%

6.2.7.2 Dependence on Position Measurement Precision 

A one-sigma position measurement accuracy of 5 m was assumed. To explore the sensitivity of alert 

effectiveness to this parameter, new ensembles with accuracies of 2.5 m and 10 m were generated. Table 

6 shows the fraction of collisions averted as measurement precision varied. There was modest 

improvement in alert effectiveness as precision improved. 

Table 6. Fraction of Collisions Averted by Alerting as a Function of Position Measurement 

Precision 

2.5mPrecision5mPrecision10mPrecision

ArriverAlertOnly51.9±3.7%46.1±4.0%44.8±3.9%

TaxierAlertOnly100.0±0.0%99.6±0.3%98.4±0.8%

GroundBasedAlertstoBoth98.9±0.8%95.8±1.8%93.5±2.0%

6.2.7.3 Dependence on ADS-B Velocity Measurement 

The alerting algorithm was assumed to have access to an independent ADS-B measurement of velocity, 

rather than being limited to a velocity estimated from position measurements. The assumed accuracy of 

this measurement, either 0.03 m/s or 0.10 m/s according to the equipage model described in Section 6.1.3, 

was much better than the accuracy for velocity derived from smoothing recent position measurements. 

To explore the sensitivity of alert effectiveness to the availability of this measurement, alert effectiveness 

was evaluated with and without it. Table 7 shows the results. The high-precision ADS-B velocity 

measurement did not discernibly improve alert effectiveness. 

Although the collision risk and nuisance alert rate did not change when the ADS-B velocity measurement 

was removed, the alert rate did increase for encounters containing a rules violation but no significant 

collision risk. With the increased uncertainties, the alert algorithm could not rule out a collision risk in 

some of these cases, and so it issued more alerts. 
 55 

Table 7. Fraction of Collisions Averted by Alerting with and without ADS-B Velocity Measurement 

WithADSBVelocityWithoutADSBVelocity

ArriverAlertOnly44.3±1.5%44.0±1.4%

TaxierAlertOnly99.6±0.2%99.6±0.1%

GroundBasedAlertstoBoth97.5±0.5%97.0±0.5%

6.2.8 Comparison of NextGen Environments 

To evaluate safety in the four NextGen environments, the effects of both the alerting system and the 

situational awareness benefits of the adopted technologies were modeled. The technologies and 

associated benefits depend on aircraft class (New Transport, Retrofit Transport, or Non-Transport) and 

environment, as shown in Table 2.

The SA benefits are summarized in Table 8. Non-traffic alerting was assumed to prevent some blunders 

onto the runway, so if the taxier had this technology some fraction of blunder encounters would never 

happen. It was assumed that in this case the blunder rate was reduced by 20%. CDTI provided additional 

SA benefits; either the taxier or the arriver may see the other aircraft and take corrective action before a 

blunder develops. It was assumed that CDTI reduced the taxier’s blunder rate by 80%, and the arriver’s 

rate by 60%. If both aircraft enjoyed SA benefits, the combined effect was multiplicative. For example, 

if both had CDTI the blunder rate was reduced by a factor of (1-80%)(1-60%) = 0.08. That is, 92% of 

blunder encounters were prevented by SA. 

Table 8. Situational Awareness Benefits as a Function of Aircraft Class and NextGen Environment 

NewTransportRetrofitTransportNonTransport

NextGen1basebasebase

NextGen2CDTInontrafficalertingbase

NextGen3CDTICDTICDTI

NextGen4CDTICDTICDTI

The alerting systems available in each NextGen scenario are shown in Table 9. Basic runway alerting 

included the alerts in the taxi crossing scenario, so alerts were provided to all aircraft in NextGen 3 & 4, 

and to New Transport in NextGen 2. The fraction of collisions prevented by alerting was evaluated using 

the simulation discussed above. This quantity was determined for each pair of aircraft classes under each 

type of alerting. When no alerting was available, the fraction prevented was 0%. 

Table 9. Alerts Available as a Function of Aircraft Class and NextGen Environment 

NewTransportRetrofitTransportNonTransport

NextGen1nonenonenone

NextGen2basicrunwaynonenone

NextGen3comprehensivebasicrunwaybasicrunway

NextGen4groundgroundground 56 

To evaluate safety in each NextGen environment, first, an ensemble of blunder encounters was generated 

and the number of collisions in the ensemble was determined. This number may be fractional because a 

probability was associated with each encounter. Then the ensemble was separated into 9 subgroups, one 

for each pair of aircraft classes. For each pair, the fraction of collisions prevented by situational 

awareness and by the alerting system was determined. Multiplying the number of collisions by the 

fraction of collisions remaining after SA and after alerting, the number of collisions was obtained for that 

class pair. Finally, the number of collisions in the ensemble with the benefits of SA and alerting was 

determined by summing over the class pairs. Comparing to the number of collisions without those 

benefits, the relative safety was determined. This procedure was performed for each NextGen 

environment, using the same ensemble for each. 

Note that NextGen 1, the baseline, offered no SA or alerting benefits. Its relative collision risk was, by 

definition, 100%. 

Tables 10 through Table 13 show these calculations in the four NextGen environments. The relative 

collision risks in each environment, highlighted in the lower right corner of each table, give the relative 

safety between the environments. Figure 22 plots this relative collision risk. It was found that NextGen 3 

& 4 both feature a very low collision rate. The higher rate in NextGen 4 was due to the latency 

associated with transmitting measurements and alerts to and from the ground-based system. The 

NextGen 2 was much less safe than either NextGen 3 or 4, because some aircraft did not receive alerts 

and did not enjoy the same SA benefits. Still, NextGen 2 was much safer than the baseline, and NextGen 

3 or 4 offer diminishing returns compared to the incremental benefit of NextGen 2. 

Table 10. Relative Collision Risk in NextGen1 Environment for Taxi Crossing Runway Scenario 

Arriver

Class

Taxier

Class

#Collisionsin

Baseline

Alerting

type

Fraction

Avertedby

Alerting

Relative

CollisionRisk

AfterSA

RelativeCollisionRiskAfter

Alerting&SA

New New 220.3 N/A N/A 100.00% 100.00% ± 0.00%

New Retro 224.5 N/A N/A 100.00% 100.00% ± 0.00%

New Non 41.4 N/A N/A 100.00% 100.00% ± 0.00%

Retro New 233.0 N/A N/A 100.00% 100.00% ± 0.00%

Retro Retro 192.3 N/A N/A 100.00% 100.00% ± 0.00%

Retro Non 31.4 N/A N/A 100.00% 100.00% ± 0.00%

Non New 32.9 N/A N/A 100.00% 100.00% ± 0.00%

Non Retro 45.8 N/A N/A 100.00% 100.00% ± 0.00%

Non Non 6.3 N/A N/A 100.00% 100.00% ± 0.00%

TOTAL 1028.0 100.00% ± 0.00%
 57 

Table 11. Relative Collision Risk in NextGen2 Environment for Taxi Crossing Runway Scenario 

Arriver

Class

Taxier

Class

#

Collisions

in

Baseline

Alerting

type

FractionAvertedby

Alerting

Relative

Collision

RiskAfter

SA

RelativeCollisionRisk

AfterAlerting&SA

NewNew220.3both99.66%±0.29%8.00%0.03%±0.02%

NewRetro224.5arriver43.80%±3.45%32.00%17.98%±1.10%

NewNon41.4arriver35.47%±9.82%40.00%25.81%±3.93%

RetroNew233.0taxier99.97%±0.03%20.00%0.01%±0.01%

RetroRetro192.3N/AN/A80.00%80.00%±0.00%

RetroNon31.4N/AN/A100.00%100.00%±0.00%

NonNew32.9taxier100.00%±0.00%20.00%0.00%±0.00%

NonRetro45.8N/AN/A80.00%80.00%±0.00%

NonNon6.3N/AN/A100.00%100.00%±0.00%

TOTAL1028.027.18%±0.29%

Table 12. Relative Collision Risk in NextGen3 Environment for Taxi Crossing Runway Scenario 

Arriver

Class

Taxier

Class

#

Collisions

in

Baseline

Alerting

type

FractionAvertedby

Alerting

Relative

Collision

RiskAfter

SA

RelativeCollision

RiskAfterAlerting&

SA

NewNew220.3both99.66%±0.29%8.00%0.03%±0.02%

NewRetro224.5both99.97%±0.02%8.00%0.00%±0.00%

NewNon41.4both98.21%±1.19%8.00%0.14%±0.10%

RetroNew233.0both99.97%±0.03%8.00%0.00%±0.00%

RetroRetro192.3both100.00%±0.00%8.00%0.00%±0.00%

RetroNon31.4both100.00%±0.00%8.00%0.00%±0.00%

NonNew32.9both100.00%±0.00%8.00%0.00%±0.00%

NonRetro45.8both100.00%±0.00%8.00%0.00%±0.00%

NonNon6.3both100.00%±0.00%8.00%0.00%±0.00%

TOTAL1028.00.01%±0.01% 58 

Table 13. Relative Collision Risk in NextGen4 Environment for Taxi Crossing Runway Scenario 

Arriver

Class

Taxier

Class

#

Collisions

in

Baseline

Alerting

type

FractionAvertedby

Alerting

Relative

Collision

RiskAfter

SA

RelativeCollision

RiskAfterAlerting&

SA

NewNew220.3ground97.15%±0.95%8.00%0.23%±0.08%

NewRetro224.5ground96.37%±0.95%8.00%0.29%±0.08%

NewNon41.4ground96.98%±1.81%8.00%0.24%±0.14%

RetroNew233.0ground97.00%±1.10%8.00%0.24%±0.09%

RetroRetro192.3ground98.04%±1.01%8.00%0.16%±0.08%

RetroNon31.4ground95.35%±2.95%8.00%0.37%±0.24%

NonNew32.9ground99.72%±0.49%8.00%0.02%±0.04%

NonRetro45.8ground100.00%±0.00%8.00%0.00%±0.00%

NonNon6.3ground100.00%±0.00%8.00%0.00%±0.00%

TOTAL1028.00.22%±0.04%

Figure 22. Relative Collision Rates in Four NextGen Environments, Relative to the Baseline 

(NextGen1) for Taxi Crossing Runway Scenario 

6.2.8.1 Dependence on Fleet Composition 

A fleet composition of 45% New Transport, 45% Retrofit Transport, and 10% Non-Transport was 

assumed. A variation was considered in which the composition was 30% New Transport, 50% Retrofit 

Transport, and 20% Non-Transport – overall a less-equipped mixture of aircraft. Table 14 shows the 

relative collision rate based on the effects of situational awareness and alerting in each NextGen 

environment for each fleet mixture. The older fleet increased the collision rate in the NextGen 2 
 59 

environment, where Retrofit Transport and especially Non-Transport were relatively less equipped. 

Otherwise, there was little difference because equipage was similar among the other three classes. 

Table 14. Collision Rate Relative to Baseline in Each NextGen Environment as a Function of Fleet 

Composition for Runway Crossing Scenario 

BaselineFleetOlderFleet

NextGen1100.0%100.0%

NextGen227.2%42.3%

NextGen30.0%0.0%

NextGen40.2%0.2%

6.3 Scenario 2: Intersecting Arrivals 

6.3.1 Overview 

Figure 23 shows a diagram of the scenario. Two arriving aircraft landed on intersecting runways. The 

intersection was located a variable distance (0 to 1000 meters) from each runway threshold (with a 

different distance for each runway). The angle between runways, taken to be 45 degrees, was relatively 

unimportant to the results. 

Enters

intersection

Exits 

intersection

Figure 23. Intersecting Arrivals Scenario 

6.3.2 Scenario Model 

For each of the two arrivers, the same arriver model was used as in the taxi crossing runway scenario. 

6.3.3 Collision Risk With and Without an Alert 

Collision risk was modeled with the same function as in the taxi crossing runway scenario; it was based 

on the distance margin between the two aircraft at the intersection. Collision risk after an alert was issued 
 60 

was modeled by assuming that the alert instructed the arriver to go around (if before threshold) or to halt 

(if after touchdown). Again, this matches the procedure used in the taxi crossing runway scenario. 

6.3.4 Projected Times and Margins 

Times were projected for when each aircraft would enter and leave the intersection, and the two safety 

margins were computed just as in the previous scenario. An analogue of the “rules margin” was not 

calculated, as there was no corresponding rule that must be followed (aside from anticipated separation). 

6.3.5 Alerting Algorithm 

The alerting algorithm was similar to the algorithm for the taxi crossing runway scenario. The same 

requirements on safety margin and arriver position were used. Alerts were generated only if both aircraft 

were less than 30 seconds from threshold and had not yet passed through the intersection. One significant 

difference was that there was no analogue to the “rules margin” requirement. 

For on-board alerts, no coordination was assumed between the two aircraft; each aircraft chose whether to 

alert without knowledge of the other aircraft’s decision (and without even knowing if the other aircraft 

had an alerting system). After both aircraft have touched down, both could be instructed to halt even if it 

would have been advantageous for one to proceed while the other halted. In most cases it was better for 

both to halt than for both to proceed without an alert. 

A ground-based alerting system could choose to alert just one of the two aircraft if it believed that was the 

best response. In the system modeled, if an alert was to be issued and the two aircraft were predicted to 

enter the intersection within 3 seconds of each other, both received an alert. If both aircraft have touched 

down and one entered more than 3 seconds after the other, only the later aircraft received an alert. If one 

or both aircraft were still airborne and could be instructed to go around, both received an alert. It was 

assumed that the two aircraft were always successful in avoiding each other if both went around. 

6.3.6 Nuisance Alert Rate 

The nuisance alert rate was evaluated in an ensemble of encounters taken to represent normal operations. 

In this ensemble, it was assumed that the true safety margin between the two aircraft was 5 to 15 seconds. 

None of these encounters pose a collision risk. 

An ensemble of 400,000 encounters was simulated with a ground-based alerting system. None of these 

encounters generated an alert. Based on a binomial distribution, this shows that the nuisance alert rate 

was less than 0.9 x 10-5 at 95% confidence. Thus, it was validated that the nuisance alert rate was low for 

the ground-based alerting system. While this test was not been performed for on-board alerts, there is no 

reason to suspect a significant difference. 

6.3.7 Alert Effectiveness 

Figure 24 shows collision risk as a function of the true safety margin time between the two arrivals, with 

alerts issued to neither, one, or both aircraft. It was found that no collisions occurred when any alerting 

system was in place; alerts issued to one or both aircraft were very effective. 

The reduction in collision risk came entirely from go-around alerts issued before threshold, rather than 

alerts issued after touchdown. 
 61 

-20 -15 -10 -5 0 5 10 15 20

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time Between A Exiting and B Entering Intersection (sec)

Collision risk

Collision risk vs. Time Between A Exiting and B Entering Intersection (sec)

Without alerting system

With arriverA alert only

With arriverB alert only

With on-board alerts to both

With ground system alert

Figure 24. Collision Risk as a Function of Safety Margin for Intersecting Arrivals Scenario 

6.3.7.1 Dependence on Response time 

As in the previous scenario, dependence on response time was evaluated by doubling and halving it. 

Table 15 shows the fraction of collisions averted as response time varied. It is shown that even when 

response times doubled, the alerting system was very effective. This is because alerts are typically issued 

long before the aircraft is at threshold. 

Table 15. Fraction of Collisions Averted by Alerting as a Function of Response time 

Responsetimesx1⁄2BaselineResponsetimesx2

ArriverAAlertOnly100.0±0.0%100.0±0.0%99.9±0.1%

ArriverBAlertOnly100.0±0.0%100.0±0.0%99.5±0.2%

OnBoardAlertstoBoth100.0±0.0%100.0±0.0%100.0±0.0%

GroundBasedAlertstoBoth100.0±0.0%100.0±0.0%100.0±0.0%

6.3.7.2 Dependence on Position Measurement Precision 

A one-sigma position measurement accuracy of 5 m was assumed. To explore the sensitivity of alert 

effectiveness to this parameter, new ensembles were generated with accuracies of 2.5 m and 10 m. Table 

16 shows the fraction of collisions averted as measurement precision varied. The alerting system was 

always 100% effective, even at 10 m precision. 
 62 

Table 16. Fraction of Collisions Averted by Alerting as a Function of Position Measurement 

Precision 

2.5mPrecision5mPrecision10mPrecision

ArriverAAlertOnly100.0±0.0%100.0±0.0%100.0±0.0%

ArriverBAlertOnly100.0±0.0%100.0±0.0%100.0±0.0%

OnBoardAlertstoBoth100.0±0.0%100.0±0.0%100.0±0.0%

GroundBasedAlertstoBoth100.0±0.0%100.0±0.0%100.0±0.0%

6.3.7.3 Dependence on ADS-B Velocity Measurement 

The alerting algorithm was assumed to have access to an independent ADS-B measurement of velocity, 

rather than being limited to a velocity estimated from position measurements. The assumed accuracy of 

this measurement, either 0.03 m/s or 0.10 m/s according to the equipage model described in Section 6.1.3, 

was much better than the accuracy for velocity derived from smoothing recent position measurements. 

To explore the sensitivity of alert effectiveness to the availability of this measurement, alert effectiveness 

was evaluated with and without it. Table 17 shows the results. Alerting was always 100% effective in 

both cases, and so the high-precision ADS-B velocity measurement did not improve alert effectiveness. 

As in the previous scenario, the alert rate did increase for encounters that were safe but were also close to 

a collision risk. 

Table 17. Fraction of Collisions Averted by Alerting with and without ADS-B Velocity 

Measurement 

WithADSBVelocityWithoutADSBVelocity

ArriverAAlertOnly100.0±0.0%100.0±0.0%

ArriverBAlertOnly100.0±0.0%100.0±0.0%

OnBoardAlertstoBoth100.0±0.0%100.0±0.0%

GroundBasedAlertstoBoth100.0±0.0%100.0±0.0%

6.3.8 Comparison of NextGen Environments 

Safety was evaluated in the four NextGen environments using the same technique as in the taxi crossing 

runway scenario. For SA, it was assumed that CDTI enabled a pilot to avoid 60% of blunders. It was 

also assumed that non-traffic alerting offered no benefit in this scenario. Alerts were generated for all 

aircraft with comprehensive or basic runway alerting or for all aircraft if a ground-based system was 

present. 

Tables 18 through Table 21 show the calculations and results. Figure 25 plots relative safety in the four 

environments. As in the taxi crossing runway scenario, NextGen 3 or 4 equipages eliminated almost all 

collisions, while NextGen 2 equipage eliminated a majority of collisions. 
 63 

Table 18. Relative Collision Risk in NextGen1 Environment for Intersecting Arrivals Scenario 

Arriver

AClass

Arriver

BClass

#Collisions

inBaseline

Alerting

type

Fraction

Averted

byAlerting

Relative

CollisionRisk

After

Situational

Awareness

RelativeCollisionRisk

AfterAlerting&SA

NewNew82.7x ± 100.00% 100.00%± 0.00%

NewRetro77.0x ± 100.00% 100.00%± 0.00%

NewNon20.2x ± 100.00% 100.00%± 0.00%

RetroNew89.7x ± 100.00% 100.00%± 0.00%

RetroRetro96.8x ± 100.00% 100.00%± 0.00%

RetroNon13.4x ± 100.00% 100.00%± 0.00%

NonNew12.1x ± 100.00% 100.00%± 0.00%

NonRetro17.3x ± 100.00% 100.00%± 0.00%

NonNon2.0x ± 100.00% 100.00%± 0.00%

TOTAL411.2100.00%± 0.00%

Table 19. Relative Collision Risk in NextGen2 Environment for Intersecting Arrivals Scenario 

Arriver

AClass

Arriver

BClass

#

Collisions

in

Baseline

Alerting

type

FractionAverted

byAlerting

Relative

CollisionRisk

After

Situational

Awareness

RelativeCollision

RiskAfterAlerting

&SA

NewNew82.7both100.00%±0.00%16.00%0.00%±0.00%

NewRetro77.0A100.00%±0.00%40.00%0.00%±0.00%

NewNon

20.2A100.00%±0.00%40.00%0.00%±0.00%

RetroNew89.7B100.00%±0.00%40.00%0.00%±0.00%

RetroRetro96.8x±100.00%100.00%±0.00%

RetroNon13.4x±100.00%100.00%±0.00%

NonNew12.1B100.00%±0.00%40.00%0.00%±0.00%

NonRetro17.3x±100.00%100.00%±0.00%

NonNon2.0x±100.00%100.00%±0.00%

TOTAL411.231.50%±0.00% 64 

Table 20. Relative Collision Risk in NextGen3 Environment for Intersecting Arrivals Scenario 

Arriver

A

Class

Arriver

BClass

#

Collisions

in

Baseline

Alerting

type

FractionAverted

byAlerting

Relative

CollisionRisk

After

Situational

Awareness

RelativeCollision

RiskAfterAlerting

&SA

NewNew82.7both 100.00% ± 0.00% 16.00% 0.00%± 0.00%

NewRetro77.0both 100.00% ± 0.00% 16.00% 0.00%± 0.00%

NewNon20.2both 100.00% ± 0.00% 16.00% 0.00%± 0.00%

RetroNew89.7both 100.00% ± 0.00% 16.00% 0.00%± 0.00%

RetroRetro96.8both 100.00% ± 0.00% 16.00% 0.00%± 0.00%

RetroNon13.4both 100.00% ± 0.00% 16.00% 0.00%± 0.00%

NonNew12.1both 100.00% ± 0.00% 16.00% 0.00%± 0.00%

NonRetro17.3both 100.00% ± 0.00% 16.00% 0.00%± 0.00%

NonNon2.0both 100.00% ± 0.00% 16.00% 0.00%± 0.00%

TOTAL411.20.00%± 0.00%

Table 21. Relative Collision Risk in NextGen4 Environment for Intersecting Arrivals Scenario 

Arriver

AClass

Arriver

BClass

#

Collisions

in

Baseline

Alerting

type

FractionAvertedby

Alerting

Relative

CollisionRisk

After

Situational

Awareness

RelativeCollision

RiskAfterAlerting

&SA

NewNew82.7ground 100.00% ± 0.00% 16.00% 0.00%± 0.00%

NewRetro77.0ground 100.00% ± 0.00% 16.00% 0.00%± 0.00%

NewNon20.2ground 100.00% ± 0.00% 16.00% 0.00%± 0.00%

RetroNew89.7ground 100.00% ± 0.00% 16.00% 0.00%± 0.00%

RetroRetro96.8ground 100.00% ± 0.00% 16.00% 0.00%± 0.00%

RetroNon13.4ground 100.00% ± 0.00% 16.00% 0.00%± 0.00%

NonNew12.1ground 100.00% ± 0.00% 16.00% 0.00%± 0.00%

NonRetro17.3ground 100.00% ± 0.00% 16.00% 0.00%± 0.00%

NonNon2.0ground 100.00% ± 0.00% 16.00% 0.00%± 0.00%

TOTAL411.20.00%± 0.00%
 65 

Figure 25. Relative Collision Rates in Four NextGen Environments, Relative to the Baseline 

(NextGen1) for Intersecting Arrivals Scenario 

6.3.8.1 Dependence on Fleet Composition 

A fleet composition of 45% New Transport, 45% Retrofit Transport, and 10% Non-Transport was 

assumed. A variation was considered in which the composition is 30% New Transport, 50% Retrofit 

Transport, and 20% Non-Transport – overall a less-equipped mixture of aircraft. Table 22 shows the 

relative collision rate based on the effects of the combination of situational awareness and alerting in each 

NextGen environment for each fleet mixture. The older fleet increased the collision rate in the NextGen 2 

environment, where Retrofit Transport and Non-Transport were relatively less equipped. Otherwise, 

there was little difference because equipage was similar among the other three classes. 

Table 22. Collision Rate Relative to Baseline in Each NextGen Environment as a Function of Fleet 

Composition for Intersecting Arrivals Scenario 

BaselineFleetOlderFleet

NextGen1100.0%100.0%

NextGen231.5%49.1%

NextGen30.0%0.0%

NextGen40.0%0.0% 66 

6.4 Scenario 3: Taxi Following 

6.4.1 Overview 

Figure 26 shows a diagram of the scenario. A lead aircraft taxied at constant speed (or stopped with zero 

speed) while a follower approached at a greater speed. At some distance the follower began to brake at a 

fixed deceleration. Braking continued until the follower matched speed with the arriver. At this point the 

aircraft moved together with a fixed separation distance between them. 

Vfollower Vleader

Following 

distance

Overtake

Follow

Figure 26. Taxi Following Scenario 

6.4.2 Scenario Model 

Aircraft movement was modeled along a straight line (or a fixed route). The leader maintained constant 

speed. The follower approached at constant speed, decelerated to match the leader’s speed, and then 

continued at constant speed. Figure 27 shows the position and speed profiles of the two aircraft in an 

example encounter. 

6.4.3 Collision Risk With and Without an Alert 

Collision risk was defined as a function of the minimum separation between the follower’s nose and the 

leader’s tail. If this separation was safely positive, there was no collision risk. If it was negative, there 

was high collision risk. As in the previous scenarios, a transition between these regimes was also 

captured; here the function was a linear transition from zero collision risk at +5 m or greater to certainty 

of collision at -5 m or worse. 
 67 

-15 -10 -5 0 5 10 15 20 25

-500

-400

-300

-200

-100

0

100

Time (sec)

Position (meters)

Measured nose position of follower

True nose position of follower

Measured tail position of leader

True tail position of leader

True tail position of leader minus

separation buffer

Follower notices leader

Follower begins braking

Follower follows at leader's speed

-15 -10 -5 0 5 10 15 20 25

2

4

6

8

10

12

14

16

18

20

22

Time (sec)

Speed (meters/sec)

Measured speed of follower

True speed of follower

Measured speed of leader

True speed of leader

Follower notices leader

Follower begins braking

Follower follows at leader's speed

Figure 27. Position and Speed of Aircraft in Taxi Following Scenario 
 68 

6.4.4 Projected Separation 

The distance of closest point of approach was calculated if the follower initiated a comfortable braking 

deceleration (-1 m/s2

) at the current time. Figure 28 shows an example of this projected distance as a 

function of time (in this example, the true separation is 50 m). The deceleration assumed in this 

projection may not match the true braking deceleration; -1 m/s2

 is the strongest deceleration that an 

aircraft would choose if it did not have to stop urgently. 

-15 -10 -5 0 5 10 15 20 25

0

50

100

150

200

250

300

Time (sec)

Closest separation (m)

Closest separation (m) if follower brakes at -1m/s2

Follower notices leader

Follower begins braking

Follower follows at leader's speed

Figure 28. Projected Minimum Separation as a Function of Time 

6.4.5 Alerting Algorithm 

Alerts were issued only to the follower; an alert instructed the follower to brake. Alerts were not issued to 

the leader prescribing an increase in speed. 

The alert variables were the current separation between aircraft and the distance of closest point of 

approach if the follower initiated a comfortable braking deceleration (-1 m/s2

) at the current time. If the 

current separation was less than 200 m and the distance of closest approach was smaller than a 

conventional distance between taxiing aircraft (50 m), an alert was issued. In fact it was required that the 

distance of closest point of approach was below 50 m by more than twice its uncertainty; this ensured that 

an alert was issued only when a violation of conventional separation was nearly certain. This criterion 

prevented nuisance alerts. No additional criterion was added requiring a significant collision risk, as 

violations of conventional separation usually present significant risk and are worthy of an alert. 

6.4.6 Nuisance Alert Rate 

The nuisance alert rate was evaluated in an ensemble of encounters taken to represent normal operations. 

In this ensemble, it was assumed that the follower noticed the leader at a distance no less than 200 m, and 

braked gently to match speed with no collision risk. 

An ensemble of 400,000 encounters was simulated with a ground-based alerting system. None of these 

encounters generated an alert. Based on a binomial distribution, this shows that the nuisance alert rate 
 69 

was less than 0.9 x 10-5 at 95% confidence. Thus, it is validated that the nuisance alert rate was 

acceptably low for the ground-based alerting system. While this test was not performed for on-board 

alerts, there is no reason to suspect a significant difference. 

6.4.7 Alert Effectiveness 

Collision risk in the absence of an alerting system depended strongly on the speed difference between 

aircraft and the distance between aircraft when the follower noticed the leader and began braking. Figure 

29 and Figure 30 show the collision risk with and without alerting as a function of these variables. As 

expected, a large speed difference or small distance increased the collision risk. Alerting was effective, 

with greater effectiveness for on-board alerting due to the reduced latency. 

Alert effectiveness depended strongly on pilot response time. Figure 31 shows collision risk as a function 

of response time, derived empirically from the Monte Carlo trials. Without an alerting system, the 

collision risk was insensitive to response time, as expected. However, with an alert the collision risk 

increased rapidly with response time up to approximately 6 seconds reaction time. The small number of 

cases with a larger reaction time made the success ratio an unreliable indicator beyond this point. 

4 6 8 10 12 14 16

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Difference in Taxi Speeds (m/s)

Collision risk

Collision risk vs. Difference in Taxi Speeds (m/s)

Without alerting system

With on-board alert to follower

With ground system alert

Figure 29. Collision Risk vs. Speed Difference for Taxi Following Scenario 
 70 

20 40 60 80 100 120 140 160 180 200

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Distance Where Follower Notices Leader (m)

Collision risk

Collision risk vs. Distance Where Follower Notices Leader (m)

Without alerting system

With on-board alert to follower

With ground system alert

Figure 30. Collision Risk vs. Braking Initiation Distance for Taxi Following Scenario 

0 2 4 6 8 10 12 14

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

Follower Reaction Time (sec)

Collision risk

Collision risk vs. Follower Reaction Time (sec)

Without alerting system

With on-board alert to follower

With ground system alert

Figure 31. Collision Risk vs. Follower Response Time for Taxi Following Scenario 
 71 

6.4.7.1 Dependence on Response time 

As in the previous scenarios, dependence on response time was evaluated by doubling and halving it. 

Table 23 shows the fraction of collisions averted as response time varied. It is shown that alert 

effectiveness was very sensitive to increased response time. The reduced effectiveness of ground-based 

alerts, relative to on-board, was due to the additional latency in the ground-based system. 

Table 23. Fraction of Collisions Averted by Alerting as a Function of Response time 

Responsetimesx1⁄2BaselineResponsetimesx2

OnboardAlert100.0±0.0%99.1±0.3%67.2±0.9%

GroundBasedAlert100.0±0.0%93.6±1.1%42.7±0.7%

6.4.7.2 Dependence on Position Measurement Precision 

A one-sigma position measurement accuracy of 5 m was assumed. To explore the sensitivity of alert 

effectiveness to this parameter, new ensembles were generated with accuracies of 2.5 m and 10 m. Table 

24 shows the fraction of collisions averted as measurement precision varied. Modest improvement was 

seen in alert effectiveness as measurement precision improved. 

Table 24. Fraction of Collisions Averted by Alerting as a Function of Position Measurement 

Precision 

2.5mPrecision5mPrecision10mPrecision

OnboardAlert99.2±0.4%98.7±0.4%96.9±0.7%

GroundBasedAlert95.7±1.0%92.5±1.0%91.5±0.9%

6.4.7.3 Dependence on ADS-B Velocity Measurement 

The alerting algorithm was assumed to have access to an independent ADS-B measurement of velocity, 

rather than being limited to a velocity estimated from position measurements. The assumed accuracy of 

this measurement, either 0.03 m/s or 0.10 m/s according to the equipage model described in Section 6.1.3, 

was much better than the accuracy for velocity derived from smoothing recent position measurements. 

To explore the sensitivity of alert effectiveness to the availability of this measurement, alert effectiveness 

was evaluated with and without it. Table 25 shows the results. The alerting algorithm benefitted 

significantly from the high-precision ADS-B velocity measurement, especially for a ground-based alert 

system. 

Table 25. Fraction of Collisions Averted by Alerting with and without ADS-B Velocity 

Measurement 

WithADSBVelocityWithoutADSBVelocity

OnboardAlert99.7±0.3%87.7±1.8%

GroundBasedAlert96.3±1.0%46.5±2.1%

6.4.8 Comparison of NextGen Environments 

Safety was evaluated for the four NextGen environments using the same technique as in the previous 

scenarios. For situational awareness, it was assumed that CDTI enabled a pilot to avoid 80% of blunders. 

It was also assumed that non-traffic alerting offered no benefit in this scenario. Alerts were generated for 

all aircraft with comprehensive alerting (but not basic runway alerting), or for all aircraft if a groundbased system was present. 
 72 

Tables 26 through Table 29 show the calculations and results. Figure 32 plots relative collision risk in the 

four environments. 

Table 26. Relative Collision Risk in NextGen1 Environment for Taxi Following Scenario 

Follower

Class

Leader

Class

#

Collisions

inBaseline

Alerting

type

Fraction

Averted

by

Alerting

Relative

CollisionRisk

After

Situational

Awareness

RelativeCollisionRisk

AfterAlerting&SA

NewNew68.3x ± 100.00% 100.00%± 0.00%

NewRetro72.4x ± 100.00% 100.00%± 0.00%

NewNon12.9x ± 100.00% 100.00%± 0.00%

RetroNew70.2x ± 100.00% 100.00%± 0.00%

RetroRetro67.3x ± 100.00% 100.00%± 0.00%

RetroNon17.4x ± 100.00% 100.00%± 0.00%

NonNew20.6x ± 100.00% 100.00%± 0.00%

NonRetro12.7x ± 100.00% 100.00%± 0.00%

NonNon2.0x ± 100.00% 100.00%± 0.00%

TOTAL343.7100.00%± 0.00%

Table 27. Relative Collision Risk in NextGen2 Environment for Taxi Following Scenario 

Follower

Class

Leader

Class

#Collisions

inBaseline

Alerting

type

Fraction

Averted

by

Alerting

Relative

CollisionRisk

After

Situational

Awareness

RelativeCollisionRisk

AfterAlerting&SA

NewNew68.3x±20.00%20.00%±0.00%

NewRetro72.4x±20.00%20.00%±0.00%

NewNon

12.9x±20.00%20.00%±0.00%

RetroNew70.2x±100.00%100.00%±0.00%

RetroRetro67.3x±100.00%100.00%±0.00%

RetroNon17.4x±100.00%100.00%±0.00%

NonNew20.6x±100.00%100.00%±0.00%

NonRetro12.7x±100.00%100.00%±0.00%

NonNon2.0x±100.00%100.00%±0.00%

TOTAL343.764.26%±0.00% 73 

Table 28. Relative Collision Risk in NextGen3 Environment for Taxi Following Scenario 

Follower

Class

Leader

Class

#

Collisions

in

Baseline

Alerting

type

FractionAverted

byAlerting

Relative

CollisionRisk

After

Situational

Awareness

RelativeCollision

RiskAfterAlerting

&SA

NewNew68.3follower 99.76% ± 0.31% 20.00% 0.05%± 0.06%

NewRetro72.4follower 99.00% ± 0.64% 20.00% 0.20%± 0.13%

NewNon12.9follower 95.43% ± 2.84% 20.00% 0.91%± 0.57%

RetroNew70.2x 0.00% ± 0.00% 20.00% 20.00%± 0.00%

RetroRetro67.3x ± 20.00% 20.00%± 0.00%

RetroNon17.4x ± 20.00% 20.00%± 0.00%

NonNew20.6x 0.00% ± 0.00% 20.00% 20.00%± 0.00%

NonRetro12.7x ± 20.00% 20.00%± 0.00%

NonNon2.0x ± 20.00% 20.00%± 0.00%

TOTAL343.711.15%± 0.04%

Table 29. Relative Collision Risk in NextGen4 Environment for Taxi Following Scenario 

Follower

Class

Leader

Class

#

Collisions

in

Baseline

Alerting

type

FractionAvertedby

Alerting

Relative

Collision

RiskAfter

Situational

Awareness

RelativeCollision

RiskAfterAlerting

&SA

NewNew68.3ground 96.37% ± 2.99% 20.00% 0.73%± 0.60%

NewRetro72.4ground 93.91% ± 2.42% 20.00% 1.22%± 0.48%

NewNon12.9ground 91.61% ± 4.53% 20.00% 1.68%± 0.91%

RetroNew70.2ground 93.78% ± 2.50% 20.00% 1.24%± 0.50%

RetroRetro67.3ground 96.65% ± 2.32% 20.00% 0.67%± 0.46%

RetroNon17.4ground 94.05% ± 4.62% 20.00% 1.19%± 0.92%

NonNew20.6ground 90.09% ± 4.10% 20.00% 1.98%± 0.82%

NonRetro12.7ground 96.99% ± 2.49% 20.00% 0.60%± 0.50%

NonNon2.0ground 50.00% ± 50.00% 20.00% 10.00%± 10.00%

TOTAL343.71.11%± 0.23%
 74 

Figure 32. Relative Collision Rates in Four NextGen Environments Relative to the Baseline 

(NextGen1) for Taxi Following Scenario 

6.4.8.1 Dependence on Fleet Composition 

A fleet composition of 45% New Transport, 45% Retrofit Transport, and 10% Non-Transport was 

assumed. A variation was considered in which the composition is 30% New Transport, 50% Retrofit 

Transport, and 20% Non-Transport—overall a less-equipped mixture of aircraft. Table 30 shows the 

relative collision rate based on the effects of the combination of situational awareness and alerting in each 

NextGen environment for each fleet mixture. The older fleet increased the collision rate in the NextGen 2 

and NextGen 3 environments, where Retrofit Transport and Non-Transport were relatively less equipped. 

In NextGen 1 and 4, there was little difference because equipage was similar among both classes. 

Table 30. Collision Rate Relative to Baseline in Each NextGen Environment as a Function of Fleet 

Composition for Taxi Following Scenario 

BaselineFleetOlderFleet

NextGen1100.0%100.0%

NextGen264.3%76.1%

NextGen311.2%14.1%

NextGen41.1%1.3% 75 

6.5 Scenario 4: Arriver Following Lander 

6.5.1 Overview 

Figure 33 shows a diagram of the scenario. An arriver approached the runway as the previous aircraft, 

referred to as the “lander,” was on the runway. There were two exits from the runway that were relevant. 

The expectation of the arriver, and the controller who issued clearance, was that the lander would leave 

the runway at the first exit with time to spare between its tail clearing the hold line and the arriver’s nose 

crossing threshold. The lander decelerated with the intention of using the first exit, but at the last moment 

decided to take the second exit instead. As a result, the lander taxied along the runway at relatively low 

speed and then took the second exit. The lander therefore failed to clear the runway before the arriver 

crossed threshold, and there could have been a risk of collision. 

Planned

Actual

Arriver

Lander

Figure 33. Arriver Following Lander Scenario 

6.5.2 Scenario Model 

For both aircraft, the same arriver model was used as in the previous scenarios. After landing, the arriver 

braked just as in the other scenarios. The lander braked in order to reach the rollout speed when its nose 

reached the center of the first intersection. If it had taken the first exit with this rollout speed, it would 

have cleared the hold line with some positive time margin before the arriver’s nose crossed threshold. 

However, the lander instead proceeded at the rollout speed to the second exit, turned, and preceded off the 

runway with the same speed. Figure 34 shows the position and speed of the two aircraft as a function of 

time in a representative encounter. In this example, the arriver landed while the lander was still on the 

runway, but the two aircraft did not come close enough to collide. 

6.5.3 Collision Risk with and without an Alert 

Collision risk was modeled with the same function as in the taxi following scenario. This function was 

based on the distance of closest approach between the lander’s tail and the arriver’s nose, while both were 

on the runway. Collision risk after an alert was issued was modeled by assuming that the alert instructed 

the arriver to go around (if before threshold) or to halt (if after touchdown). This matched the procedure 

in the taxi crossing runway scenario. If an alert was issued to the lander, the lander increased speed to 

exit the runway earlier; collision risk was measured using the same function based on distance of closest 

approach. 
 76 

Figure 34. Position (Top) and Speed (Bottom) of the Two Aircraft in the Arriver Following Lander 

Scenario

-30 -20 -10 0 10 20 30 40 50 60

-2000

-1500

-1000

-500

0

500

1000

1500

2000

2500

3000

Time (sec)

Position (meters)

Measured nose position of arriver

True nose position of arriver

Measured tail position of lander

True tail position of lander

Lander passes first intersection

Lander exits at second intersection

Lander clears hold line

Arriver at threshold

Arriver enters second intersection

-30 -20 -10 0 10 20 30 40 50 60

10

20

30

40

50

60

70

Time (sec)

Speed (meters/sec)

Measured speed of arriver

True speed of arriver

Measured speed of lander

True speed of lander

Lander passes first intersection

Lander exits at second intersection

Lander clears hold line

Arriver at threshold

Arriver enters second intersection
 77 

6.5.4 Projected Times and Margins 

The times when each aircraft would cross various points were projected. For the lander, when it would 

exit the second intersection and when it would clear the hold line were projected. For the arriver, when it 

would cross threshold and when it would enter the second intersection were projected. The difference 

between intersection exit and entry times was the safety margin, and the difference between hold line 

clearance and threshold crossing was the rules margin. Note that, in contrast with the taxi crossing 

runway and intersecting arrivals scenarios, there was only one conceivable hypothesis about which 

aircraft was in the intersection first, and so the safety margin only tested the difference between arriver 

entry time and lander exit time. 

6.5.5 Alerting Algorithm 

The alerting algorithm was similar to the algorithm for the taxi crossing runway and intersecting arrivals 

scenarios. The same requirements on safety margin and rules margin were used. As in the other 

scenarios, alerts were not issued when the arriver was more than 30 seconds from threshold. 

The most significant change in this scenario was that alerts could not be issued before confidence was 

established that the lander had passed the first intersection. Without this requirement, there would have 

been many nuisance alerts issued in encounters where the lander was going to exit at the first intersection. 

In order to issue an alert, it was required that the lander’s nose had passed beyond the center of the first 

intersection by a distance of 50 meters plus twice the position measurement uncertainty. This 

requirement was the most significant in the alerting process. After it was determined that the lander did 

not exit at the first intersection, it was very clear that a rules violation had occurred, and if there were 

significant collision risk that would also be very clear. 

As a result, the performance of the alerting system, and the amount of warning time it gave, could be 

understood by analyzing the requirement on lander position. The plan was for the lander to exit at the 

first intersection’s hold line with some amount of time to spare before the arriver crossed threshold. The 

lander was at the first intersection some time before the planned clearance time – the difference was the 

time required to travel from intersection to hold line at the rollout speed. When it instead proceeded 

along the runway, an alert was issued at a later time when it had traveled 50 meters plus twice the position 

measurement uncertainty. Therefore the expectation for the time when the alert was issued was: 

Talert = Tthres – ( tplanned + (dhold + Lac – 50m – 2*p)/vrollout ) 

Here, Tthres was the time when the arriver crossed threshold, tplanned was the planned time between that 

event and the lander clearing at the first exit, dhold was the distance between the center of the first 

intersection and the exit hold line, Lac was the aircraft length, p was the position measurement 

uncertainty, and vrollout was the speed of the lander after it reached the first intersection. Using typical 

parameter values tplanned = 5 sec, dhold = 120 m (relatively long due to an assumed 45 degree angle 

between runway and exit), Lac = 40 m, p = 5 m, and v_rollout = 20 m/s, an alert was typically issued 

about 10 seconds before the arriver crossed threshold. Unless pilot response time was very long, this was 

typically enough time for the arriver to execute a go-around. Even if the arriver could not go around, it 

could brake harder on the runway to prevent a collision. Also, the taxier could speed up to exit the 

runway sooner. From this analysis, it was expected that the alert system would be very effective in 

preventing collisions. 

6.5.6 Nuisance Alert Rate 

The nuisance alert rate in an ensemble of encounters taken to represent normal operations was evaluated. 

This ensemble was identical to the blunder ensemble, except the lander exited at the first intersection (as 

the arriver expected). It was assumed that the alerting system determined that the lander was turning off 

when its nose was at a position near the center of the first intersection. The deviation from the center was 

randomly drawn from a Gaussian distribution with standard deviation of 15 m, but truncated at 30 m. 
 78 

An ensemble of 400,000 encounters with a ground-based alerting system was simulated. None of these 

encounters generate an alert. Based on a binomial distribution, this shows that the nuisance alert rate was 

less than 0.9 x 10-5 at 95% confidence. Thus it was validated that the nuisance alert rate was low for the 

ground-based alerting system. While this test was not performed for on-board alerts, there is no reason to 

suspect a significant difference. 

6.5.7 Alert Effectiveness 

Figure 35 shows collision risk as a function of the distances to the two runway exits, with alerts issued to 

neither, one, or both aircraft. 

It is a general feature of this scenario that collisions are rare even if a blunder occurred and no alerting 

system was present, because the two aircraft started far from each other and moved in the same direction. 

A collision occurred only if all of the scenario parameters were unfavorable. For example, it can be seen 

in the figure that collisions occurred only when the first exit was close to threshold and the second 

intersection was far from the first. 

Very few collisions occurred when an alerting system was in place; alerts issued to one or both aircraft 

were very effective. When the arriver received an alert, it usually went around, but even if that failed it 

could brake harder on the runway to avoid the lander. When the lander received an alert, it increased 

speed and exited the runway sooner. 

1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

Distance from Threshold to First Intersection (m)

Collision risk

Collision risk vs. Distance from Threshold to First Intersection (m)

Without alerting system

With arriver alert only

With lander alert only

With on-board alerts to both

With ground system alert

300 400 500 600 700 800 900 1000 0

0.01

0.02

0.03

0.04

0.05

0.06

Distance Between Intersections (m)

Collision risk

Collision risk vs. Distance Between Intersections (m)

Without alerting system

With arriver alert only

With lander alert only

With on-board alerts to both

With ground system alert

Figure 35. Collision Risk as a Function of Distance to First Intersection (left) and Distance between 

Intersections (right) for Arriver Following Lander Scenario 
 79 

6.5.7.1 Dependence on Response time 

As in the previous scenarios, dependence on response time was evaluated by doubling and halving it. 

Table 31 shows the fraction of collisions averted as response time varied. It can be seen that alerting was 

very effective even if response times were doubled; this scenario was insensitive to pilot response time. 

Table 31. Fraction of Collisions Averted by Alerting as a Function of Response time 

Responsetimesx1/2BaselineResponsetimesx2

ArriverAlertOnly100.0±0.0%100.0±0.0%100.0±0.0%

LanderAlertOnly99.0±0.8%99.0±0.8%99.0±0.8%

GroundBasedAlertstoBoth100.0±0.0%100.0±0.0%100.0±0.0%

6.5.7.2 Dependence on Position Measurement Precision 

A position measurement precision of 5m was assumed. To explore the sensitivity of alert effectiveness to 

this parameter, new ensembles with precisions of 2.5m and 10m were generated. Table 32 shows the 

fraction of collisions averted as measurement precision varies. It can be seen that the alerting system was 

always very effective, even at 10m precision. 

Table 32. Fraction of Collisions Averted by Alerting as a Function of Position Measurement 

Precision 

2.5mPrecision5mPrecision10mPrecision

ArriverAlertOnly100.0±0.0%100.0±0.0%100.0±0.0%

LanderAlertOnly98.6±1.3%98.6±1.3%98.6±1.3%

GroundBasedAlertstoBoth100.0±0.0%100.0±0.0%100.0±0.0%

6.5.7.3 Dependence on ADS-B Velocity Measurement 

The alerting algorithm was assumed to have access to an independent ADS-B measurement of velocity, 

rather than being limited to a velocity estimated from position measurements. The assumed accuracy of 

this measurement, either 0.03 m/s or 0.10 m/s according to the equipage model described in Section 6.1.3, 

was much better than the accuracy for velocity derived from smoothing recent position measurements. 

To explore the sensitivity of alert effectiveness to the availability of this measurement, alert effectiveness 

was evaluated with and without it. Table 33 shows the results. Alerting was always very effective in 

both cases, and so the high-precision ADS-B velocity measurement did not improve alert effectiveness. 

As in the previous scenarios, the alert rate did increase for encounters that were safe but contained a rules 

violation. In this scenario approximately 20% of such encounters produced an alert when the ADS-B 

velocity measurement was available, but without that measurement 80% of these encounters produced an 

alert. Such alerts are not considered nuisance alerts; they occur in cases where the arriver passed the 

threshold while the lander was still on the runway. 
 80 

Table 33. Fraction of Collisions Averted by Alerting with and without ADS-B Velocity 

Measurement 

WithADSBVelocityWithoutADSBVelocity

ArriverAlertOnly100.0±0.0%100.0±0.0%

LanderAlertOnly98.6±1.3%98.6±1.3%

GroundBasedAlertstoBoth100.0±0.0%100.0±0.0%

6.5.8 Comparison of NextGen Environments 

Safety in the four NextGen environments was evaluated using the same technique as in the previous 

scenarios. For situational awareness, it was assumed that CDTI enabled the pilot of either aircraft to 

avoid 60% of blunder-induced collisions; either the lander cleared at the first exit as planned, the lander 

moved faster to the second exit, the arriver went around, or the arriver braked harder. It was assumed that 

non-traffic alerting allowed the lander to better know its position along the runway and relative to the 

exits, and this technology was credited with preventing 20% of blunders. Alerts were generated for all 

aircraft with comprehensive or basic runway alerting or for all aircraft if a ground-based system was 

present. 

Tables 34 through Table 37 show the calculations and results. Figure 36 plots relative safety in the four 

environments. As in the taxi crossing runway and intersecting arrivals scenarios, NextGen 3 or 4 

equipages eliminated almost all collisions, while NextGen 2 equipage eliminated a majority of collisions. 

Table 34. Relative Collision Risk in NextGen1 Environment for Arrival Following Lander Scenario 

Arriver

AClass

Arriver

BClass

#

Collisions

inBaseline

Alerting

type

Fraction

Averted

by

Alerting

Relative

CollisionRisk

After

Situational

Awareness

RelativeCollisionRisk

AfterAlerting&SA

NewNew20.2x±100.00%100.00%±0.00%

NewRetro21.3x±100.00%100.00%±0.00%

NewNon6.0x±100.00%100.00%±0.00%

RetroNew17.9x±100.00%100.00%±0.00%

RetroRetro16.7x±100.00%100.00%±0.00%

RetroNon4.0x±100.00%100.00%±0.00%

NonNew6.1x±100.00%100.00%±0.00%

NonRetro8.0x±100.00%100.00%±0.00%

NonNon1.0x±100.00%100.00%±0.00%

TOTAL101.1100.00%±0.00% 81 

Table 35. Relative Collision Risk in NextGen2 Environment for Arrival Following Lander Scenario 

Arriver

AClass

Arriver

BClass

#

Collisions

in

Baseline

Alerting

type

FractionAvertedby

Alerting

Relative

Collision

RiskAfter

Situational

Awareness

RelativeCollision

RiskAfterAlerting&

SA

NewNew20.2both100.00%± 0.00%16.00%0.00%±0.00%

NewRetro21.3arriver100.00%± 0.00%32.00%0.00%±0.00%

NewNon6.0arriver100.00%± 0.00%40.00%0.00%±0.00%

RetroNew17.9lander100.00%± 0.00%40.00%0.00%±0.00%

RetroRetro16.7x± 80.00%80.00%±0.00%

RetroNon4.0x± 100.00%100.00%±0.00%

NonNew6.1lander100.00%± 0.00%40.00%0.00%±0.00%

NonRetro8.0x± 80.00%80.00%±0.00%

NonNon1.0x± 100.00%100.00%±0.00%

TOTAL101.124.46%±0.00%

Table 36. Relative Collision Risk in NextGen3 Environment for Arrival Following Lander Scenario 

Arriver

A

Class

Arriver

BClass

#

Collisions

in

Baseline

Alerting

type

FractionAvertedby

Alerting

Relative

Collision

RiskAfter

Situational

Awareness

RelativeCollision

RiskAfterAlerting

&SA

NewNew20.2both100.00%±0.00%16.00%0.00%±0.00%

NewRetro21.3both100.00%±0.00%16.00%0.00%±0.00%

NewNon6.0both100.00%±0.00%16.00%0.00%±0.00%

RetroNew17.9both100.00%±0.00%16.00%0.00%±0.00%

RetroRetro16.7both100.00%±0.00%16.00%0.00%±0.00%

RetroNon4.0both100.00%±0.00%16.00%0.00%±0.00%

NonNew6.1both100.00%±0.00%16.00%0.00%±0.00%

NonRetro8.0both100.00%±0.00%16.00%0.00%±0.00%

NonNon1.0both100.00%±0.00%16.00%0.00%±0.00%

TOTAL101.10.00%±0.00% 82 

Table 37. Relative Collision Risk in NextGen4 Environment for Arrival Following Lander Scenario 

Arriver

AClass

Arriver

BClass

#

Collisions

in

Baseline

Alerting

type

FractionAvertedby

Alerting

Relative

Collision

RiskAfter

Situational

Awareness

RelativeCollision

RiskAfterAlerting

&SA

NewNew20.2ground100.00%±0.00%16.00%0.00%±0.00%

NewRetro21.3ground100.00%±0.00%16.00%0.00%±0.00%

NewNon6.0ground100.00%±0.00%16.00%0.00%±0.00%

RetroNew17.9ground100.00%±0.00%16.00%0.00%±0.00%

RetroRetro16.7ground100.00%±0.00%16.00%0.00%±0.00%

RetroNon4.0ground100.00%±0.00%16.00%0.00%±0.00%

NonNew6.1ground100.00%±0.00%16.00%0.00%±0.00%

NonRetro8.0ground100.00%±0.00%16.00%0.00%±0.00%

NonNon1.0ground100.00%±0.00%16.00%0.00%±0.00%

TOTAL101.10.00%±0.00%

Figure 36. Relative Collision Rates in Four NextGen Environments, Relative to the Baseline 

(NextGen1) for Arrival Following Lander Scenario 
 83 

6.5.8.1 Dependence on Fleet Composition 

A fleet composition of 45% New Transport, 45% Retrofit Transport, and 10% Non-Transport was 

assumed. Then a variation was considered in which the composition was 30% New Transport, 50% 

Retrofit Transport, and 20% Non-Transport – overall a less-equipped mixture of aircraft. Table 38 shows 

the relative collision rate based on the effects of situational awareness and alerting in each NextGen 

environment for each fleet mixture. The older fleet increased the collision rate in the NextGen 2 

environment, where Retrofit Transport and Non-Transport were relatively less equipped. Otherwise, 

there was no difference because alerts were available, and 100% effective, for all three classes. 

Table 38. Collision Rate Relative to Baseline in Each NextGen Environment as a Function of Fleet 

Composition for Intersecting Arrivals Scenario 

BaselineFleetOlderFleet

NextGen1100.0%100.0%

NextGen224.5%41.4%

NextGen30.0%0.0%

NextGen40.0%0.0%

6.6 Scenario 5: RNAV / RNP Approach 

6.6.1 Overview 

Figure 37 shows a diagram of the parallel RNAV / RNP approach scenario which was described in 

Section 4.6.3. In this scenario a better-equipped aircraft, with RNP of 0.3 nm or better, approached on a 

short trajectory, while a lesser-equipped aircraft, with RNP of 1.0 nm or worse, approached on a longer 

outside trajectory. The two aircraft’s paths merged at a point just short of final approach. The intent was 

that the aircraft must maintain lateral separation while their paths were separate, and they should have 

crossed the merge point with sufficient longitudinal separation (corresponding to a sufficiently long time 

interval between when each aircraft crosses the merge point). 

In operation this scenario would be used with a continuous stream of aircraft on both trajectories, merging 

to land on a common runway. Just two aircraft were modeled here, with the idea that the alerting system 

would be applied to each aircraft. 

It was assumed that only the aircraft following the outer track could successfully respond to an alert. The 

inner aircraft was “boxed in” by the outer aircraft and had no feasible route by which to avoid a conflict, 

other than by landing as planned. The outer aircraft, on the other hand, could break off away from the 

inner aircraft. 

This scenario differed from the previous scenarios in two important ways. First, the concept of collision 

risk was replaced with loss-of-separation risk, as it would be difficult to construct a blunder scenario 

leading to a collision. Loss of separation may trigger a time-consuming correction such as a missed 

approach, and in a congested airspace an avoidance maneuver could lead to conflicts with other aircraft. 

Second, this scenario differed in the degree of information-sharing required and the complexity of 

assumptions regarding each aircraft’s behavior. The alerting system must know the trajectories that have 

been assigned to the two aircraft. It was assumed that it would not be feasible to provide such 

information to an on-board system. Given that the aircraft following the outside track was assumed to 

have lesser equipage, it was not reasonable to assume that it would be able to receive a planned trajectory 

for the other aircraft. The better-equipped inner aircraft would likely have such a capability, but as noted 

above it did not have a feasible route to escape from a conflict. 
 84 

Figure 37. RNAV / RNP Approach Scenario 

Therefore, it was assumed that only a ground-based alerting system was feasible. This system would 

receive the planned trajectories from the controller who issued clearance. That controller would monitor 

the aircraft for conformance with the plan, issuing speed adjustments as necessary. The alerting system 

served as a back-up for the controller. Note that in practice the design of the alerting system would be 

highly intertwined with the design of the controller’s planning and guidance system. 

Because on-board alerting was probably not feasible in this scenario, only NextGen environments 1 and 4 

were considered. NextGen 1 was the baseline, and NextGen 4 showed the effect of the ground-based 

alerting system. 

This scenario was also insensitive to the type or class of each aircraft, as each class has the same 

equipage, and the scenario did not depend on aircraft size parameters. 

In the model, only the longitudinal position of the aircraft was considered and longitudinal conflicts were 

assessed. It was assumed that lateral separation was provided by a separate alerting function. 

RNAV 2 nm Radar

RNP 0.3 nm Procedural Separation

With ATC Conformance Monitoring

(CDA)

RNP 1.0 nm Radar

RNP 0.3 nm Procedural Separation

With ATC Conformance Monitoring

(CDA)

RPI (Ghost)
 85 

6.6.2 Scenario Model 

Aircraft trajectories were modeled as straight-line paths with a semi-circular turn to the final landing 

approach. When the aircraft was on final, its speed was constant, but as it neared final approach it slowed 

gradually to the final approach speed. This was modeled as a constant deceleration. The merge point 

between the two trajectories was 1 nm before the beginning of final approach. 

It was assumed that controllers were aiming for 60 seconds between the times when the two aircraft cross 

the merge point. A separation of 55 seconds or greater was considered acceptable. That is, alerts in 

encounters where the true spacing is 55 seconds or greater were considered nuisance alerts. “Loss of 

separation” was defined as occurring when the temporal spacing was 50 seconds or less. If it was 

between 50 and 55 seconds, no loss of separation had occurred, but an alert was not considered to be a 

nuisance. The scenario results were not sensitive to the absolute level of these temporal spacing 

thresholds, but only to the difference between them (5 seconds in this case). 

Either aircraft could cross the merge point first; in the ensembles, half of the encounters had the inner 

aircraft crossing first and half had the outer aircraft crossing first. 

Figure 38 shows the position and speed of the two aircraft as a function of time in a representative 

encounter. In this example, the outer aircraft crossed the merge point 56 seconds after the inner aircraft. 

6.6.3 Loss-of-Separation Risk with and without an Alert 

As discussed above, the risk of loss of separation was examined, rather than the risk of collision. Loss of 

separation occurred if the temporal spacing at the merge point was less than 50 seconds. If an alert was 

issued (to the outer aircraft only), it was assumed that the outer aircraft first attempted to adjust speed to 

restore acceptable spacing. It was also assumed that a speed change of up to 2.5% is possible. (This 

corresponds to a 5-knot speed adjustment at a typical speed of 200 knots.) The speed change happened 

after the alert was issued and a random response time has passed. If the speed change provided spacing 

better than 50 seconds at the merge point, the alert prevented loss of separation. Otherwise, the conflict 

was not resolved. It was likely in this case that the controller would have to order a more extreme evasive 

maneuver. 

6.6.4 Projected Times and Margins 

The time when each aircraft would cross the merge point was projected. The difference between these 

two times was the projected temporal spacing at the merge. Figure 39 shows an example of the evolution 

of this projected time margin as the scenario progressed. 

When projecting crossing times, an uncertainty was applied to account for the degree to which the 

aircraft’s path deviated from the nominal trajectory. The aircraft tended to “wobble” around the nominal 

trajectory due to random events such as gusts of wind. These wobbles increased the effective path length 

and hence the time required to traverse a route. It was assumed that the well-equipped inner aircraft (with 

RNP 0.3) followed an uncertain path and the relative uncertainty was 0.5%. The outer aircraft (with RNP 

1.0) deviated to a greater degree, resulting in a greater uncertainty of 1.25%. 

6.6.5 Alerting Algorithm 

The alerting algorithm compared the projected temporal separation to two thresholds. First, to prevent 

nuisance alerts, the projected separation must be less than 55 seconds, minus 4 times the uncertainty. 

Second, to result in a significant risk of loss of separation, it must be less than 50 seconds, plus 4 times 

the uncertainty. 

It was also required that the outer aircraft was within 4 minutes of the merge point, and had not already 

passed it. 
 86 

-250 -200 -150 -100 -50 0 50 100

-3

-2.5

-2

-1.5

-1

-0.5

0

0.5 x 104

Time (sec)

Position (meters)

Measured position of outer AC

True position of outer AC

Measured position of inner AC

True position of inner AC

Inner aircraft begins deceleration

Inner aircraft crosses merge point

Inner aircraft is on final (constant speed)

Outer aircraft begins deceleration

Outer aircraft crosses merge point

Outer aircraft is on final (constant speed)

Alert checking begins

-250 -200 -150 -100 -50 0 50 100

60

70

80

90

100

110

120

130

140

150

Time (sec)

Speed (meters/sec)

Measured speed of outer AC

True speed of outer AC

Measured speed of inner AC

True speed of inner AC

Inner aircraft begins deceleration

Inner aircraft crosses merge point

Inner aircraft is on final (constant speed)

Outer aircraft begins deceleration

Outer aircraft crosses merge point

Outer aircraft is on final (constant speed)

Alert checking begins

Figure 38. Position (Top) and Speed (Bottom) of the Two Aircraft in the RNAV / RNP Approach 

Scenario
 87 

-250 -200 -150 -100 -50 0 50 100

0

10

20

30

40

50

60

70

80

90

Time (sec)

Safety Margin at Merge (sec)

Safety Margin at Merge

Predicted Safety Margin at Merge

True Safety Margin at Merge

Inner aircraft begins deceleration

Inner aircraft crosses merge point

Inner aircraft is on final (constant speed)

Outer aircraft begins deceleration

Outer aircraft crosses merge point

Outer aircraft is on final (constant speed)

Alert checking begins

Figure 39. Projected Time Margin at Merge Point, as a Function of Time when Projection 

Occurred 

6.6.6 Nuisance Alert Rate 

The nuisance alert rate in an ensemble of encounters taken to represent normal operations was evaluated. 

In this ensemble, the time margin at the merge point ranged from 55 to 65 seconds. The most likely value 

was 60 seconds. 

An ensemble of 400,000 encounters was evaluated. Of these, 71 encounters generated an alert, giving a 

nuisance alert rate of (1.8 ± 0.4)x10-4. 

6.6.7 Alert Effectiveness 

The performance of the alerting system depended critically on 3 parameters: the time at which the alert 

system began checking for a potential conflict, the relative uncertainty in distances due to deviations from 

the ideal trajectory, and the relative change in speed that was feasible in response to an alert. If the 

relative speed change was significantly greater than the relative distance uncertainty, an alert always came 

with enough time to perform a speed adjustment. The only possible failure in this case came when the 

adjustment needed to begin before the alerting system had begun checking for conflicts. 

The alerting system responded effectively to minor blunders, where the true temporal spacing was just 

short of 50 seconds. For more extreme blunders, it was ineffective. This can be seen in Figure 40, which 

shows the risk of loss of separation as a function of the temporal spacing. Adjusting the scenario 

parameters shifted the point at which the alerting system became ineffective. For example, if a greater 

speed adjustment was possible in response to an alert, blunders of greater magnitude could be corrected. 
 88 

40 42 44 46 48 50 52 54 56 58 60

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time Between Arrival of Aircraft at Merge Point (sec)

Risk of Loss of Separation

Without alerting system

With alerting system

Figure 40. Risk of Loss of Separation as a Function of the Time Margin between the Two Aircraft 

at the Merge Point 

Alert effectiveness was modeled using a blunder ensemble in which a merge time margin as low as 40 

seconds, and as high as 60 seconds, was possible. The distribution of the merge time margin within the 

blunder ensemble is shown in Figure 41. 

35 40 45 50 55 60 65

0

10

20

30

40

50

60

70

80

90

100

Time Margin at Merge (sec)

Encounters / (1 second)

Merge Time Margin Distribution for Ensemble

Safe

Rules Violation

Unsafe

Figure 41. Merge Time Margin Distribution in Blunder Ensemble 
 89 

6.6.7.1 Dependence on Response time 

It was assumed that the median pilot response time was 5 seconds. As in the previous scenario, the 

dependence on response time was evaluated by doubling and halving it. Table 39 shows the fraction of 

blunder encounters corrected as response time varied. Modest dependence on response time was found. 

Table 39. Fraction of Blunder Encounters Corrected by Alerting as a Function of Response time 

Responsetimesx1/2BaselineResponsetimesx2

GroundBasedAlerttoOuterAircraft61.3±2.2%59.4±2.4%55.2±2.8%

6.6.7.2 Dependence on Position Measurement Precision 

A one-sigma position measurement accuracy of 5m was assumed. To explore the sensitivity of alert 

effectiveness to this parameter, new ensembles with accuracies of 2.5m and 10m were generated. Table 

40 shows the fraction of blunder encounters corrected as measurement precision varied. There was no 

detectable effect; even a 10 meter uncertainty was insignificant compared to the other uncertainties in this 

scenario. 

Table 40. Fraction of Blunder Encounters Corrected by Alerting as a Function of Position 

Measurement Precision 

2.5mPrecision5mPrecision10mPrecision

GroundBasedAlerttoOuterAircraft61.5±3.4%59.4±2.4%59.8±2.0%

6.6.7.3 Dependence on ADS-B Velocity Measurement 

The alerting algorithm was assumed to have access to an independent ADS-B measurement of velocity, 

rather than being limited to a velocity estimated from position measurements. The assumed accuracy of 

this measurement, either 0.03 m/s or 0.10 m/s according to the equipage model described in Section 6.1.3, 

was much better than the accuracy for velocity derived from smoothing recent position measurements. 

To explore the sensitivity of alert effectiveness to the availability of this measurement, alert effectiveness 

was evaluated with and without it. Table 41 shows the results. The ADS-B velocity measurement did 

have a significant effect. 

Table 41. Fraction of Blunder Encounters Corrected by Alerting With and Without ADS-B 

Velocity Measurement 

WithADSBVelocityWithoutADSBVelocity

GroundBasedAlerttoOuterAircraft59.4±2.4%38.4±2.9%

6.6.7.4 Dependence on Track Deviation Uncertainty 

It was assumed that there was a 1.25% uncertainty in distance to the merge point due to deviations of the 

outer aircraft’s true path from the ideal path. Two other values for this parameter, 0.75% and 2.5%, were 

tested. Table 42 shows the results; alert effectiveness depended strongly on this uncertainty. 

Table 42. Fraction of Blunder Encounters Corrected by Alerting as a Function of Track Deviation 

Uncertainty

0.75%1.25%2.5%

GroundBasedAlerttoOuterAircraft77.2±1.9%59.4±2.4%25.9±2.7% 90 

6.6.7.5 Dependence on Relative Speed Change in Response to an Alert 

It was assumed that upon receiving an alert, the outer aircraft could adjust speed by up to 2.5%. Two 

other values for this upper limit were tested, 1.25% and 5%. Table 43 shows the results. Alerting was 

much more effective when the aircraft performed a greater speed adjustment in response. 

Table 43. Fraction of Blunder Encounters Corrected by Alerting as a Function of Maximum 

Relative Speed Adjustment 

1.25%2.5%5%

GroundBasedAlerttoOuterAircraft25.3±2.7%59.4±2.4%99.2±0.5%

6.6.7.6 Dependence on Time at which Conflict Checking Begins 

It was assumed that the alerting system began checking for conflicts 4 minutes before the outer aircraft 

crossed the merge point. Two other values for this parameter were tested, 3 and 5 minutes. Table 44 

shows the results. Alert effectiveness was fairly insensitive to this parameter. Typically an alert was not 

issued until the outer aircraft was within 3 minutes, so alert checking before that time had little effect. 

Table 44. Fraction of Blunder Encounters Corrected by Alerting as a Function of Time When 

Conflict Checking Begins 

3MinutesPrior4MinutesPrior5MinutesPrior

GroundBasedAlerttoOuterAircraft59.6±2.2%59.4±2.4%60.7±1.9%

6.6.8 Comparison of NextGen Environments 

With the default parameter values, it was found that the alerting system successfully corrected 59.4 ± 2.4 

% of blunder encounters, meaning that 40.6% remain unresolved. Since only a ground-based system was 

tested, only the NextGen 1 and NextGen 4 environments were evaluated. In these environments, there 

was no difference in capabilities between aircraft classes. Therefore, performance in the NextGen 4 

environment is described by the fraction of blunder encounters that remain unresolved in the default case 

(see Figure 42). As was seen above, this result was very sensitive to the assumed parameter values. 

Figure 42. Relative Collision Rates in NextGen 1 & 4 Environments, Relative to the Baseline 

(NextGen1) for RNAV / RNP Approach Scenario 

6.6.8.1 Dependence on Fleet Composition 

This scenario was insensitive to the class of each aircraft, and so it was insensitive to fleet composition. 
 91 

7. Conclusions and Recommendations 

The major conclusions of the modeling analysis are listed below. 

Developing a quantitative analytical model for overall CD&R effectiveness in a future environment that 

addresses variations in surveillance quality, CD&R equipage, encounter parameters, and flight crew 

response time is challenging, but feasible. 

The assumption that CD&R performance is limited primarily by kinematic state estimation and prediction 

rather than correctly classifying the encounter is important to the study conclusions. This assumption can 

be assured by the provision of appropriate air-ground data links for information on critical operational 

transitions, particularly intended runway, acceleration, and turns. 

CD&R effectiveness should be modeled against a representative ensemble of encounters, rather than a 

small number of point scenarios, because the chance of successful response may depend critically on the 

time at which an error is made. Systematic or random (Monte Carlo) variation of the relative timing of 

encounters is essential to assess effectiveness. 

The size of the margin between an acceptable operation and an encounter with collision risk is critical to 

the ability to achieve the desired nuisance alert rate while maintaining safety. Low nuisance alert rates are 

achievable under the assumption that occasional alerts for procedural violations of those safety margins 

are acceptable (i.e., not considered nuisance alerts). If operational safety margins are not much larger than 

the uncertainty in the CD&R prediction, either compromised detection or high nuisance alerts are 

inevitable. Therefore, if new NextGen procedures assume reductions in these margins, the CD&R 

capabilities assumed might not be achievable. 

By expressing alert thresholds as functions of the uncertainties on decision variables, an alerting system 

that automatically adapts to the precision of surveillance and prediction can be created. Using separate 

thresholds to test for rules compliance and risk of collision or incursion, this system can be designed to 

keep nuisance alert rates sufficiently low while achieving as much safety benefit as the precision allows. 

In the scenarios studied, operational rules provided safety margins that, in general, were large enough that 

the alert system could discriminate cleanly between high-risk encounters and those that comply with 

operational rules. Intermediate encounters, where a rule was violated but risk remained low, separated 

these extreme cases. Improved measurement precision typically had a modest effect on the alert rate in 

high-risk encounters, but allowed for a reduction in the alert rate on the intermediate encounters. This 

may allow for a reduction in the margins afforded by operational rules, with no loss of safety. 

GPS-derived ADS-B velocity should be used in CD&R systems if it is as accurate as promised, but will 

only change alert effectiveness significantly where the margin between acceptable and unsafe behavior is 

relatively small and dependent on velocity measurement. In particular, it was found that using this 

measurement improved safety in the overtaking-taxi scenario, but not in the runway crossing, intersecting 

arrivals, or arriver following lander scenarios. However, the improved precision of this measurement did 

reduce the alert rates in encounters where an operational rule was violated but there was no significant 

collision risk. 

The combination of object size uncertainty and residual error in ADS-B position in a multipath 

environment limits the ability to detect low-speed and static conflict situations (such as stationary targets 

slightly over the hold line.) However, if reasonable position margins are maintained, the collision risk 

from undetected violations of this type is small. Specifically, typical runway hold lines are 175 feet (53 

m) from the runway edge, and collision risk should be considered high if an object comes within 10 m of 

the edge (with the risk becoming a function of the wingspan and flight technical error of the aircraft using 

the runway), giving an effective position margin of approximately 40 m between a correct and safe 
 92 

position and a highly dangerous position for a stationary aircraft waiting to cross. Therefore, if residual 

size uncertainty and multipath result in reported position error that only rarely exceeds a fraction of this 

margin (for example, greater than 20 m in fewer than 10-5 of operations), the risk of undetected violations 

is small even if the threshold for alerting requires a reported position well past the hold line. The risk 

could be much greater at smaller or older airports with hold lines closer to the runway, although the 

dimensions of the runway and the aircraft using them must be considered to determine the actual risk. 

Often the largest contribution to the uncertainty in predictions of future state is the assumed behavior of 

an aircraft after an operational transition (for example, how hard an arriver will brake after landing). 

When these uncertainties dominate, improvements in surveillance precision offer no benefit. To the 

extent that future behavior can be predicted more precisely, the alerting system may become more 

effective. 

The results of perturbation analysis, which quantified the top-level performance impact of changes to key 

parameters such as median response time and surveillance position error, showed that the analytical 

model developed could be useful in making technology investment decisions. 

Based on the simple models used for the benefits of passive situation awareness (provided by a CDTI), 

there is a substantial difference in overall safety capability between assumed NextGen environments. 

Under these models, investing in either comprehensive aircraft-based alerting or a ground-based alerting 

system would reduce the collision risk by multiple orders of magnitude compared to NextGen relying 

primarily on situation awareness. The degree to which initial lapses will be prevented by improved 

situation awareness is unknown. This factor, combined with increased demands in the NextGen 

environment, will determine whether the assumed NextGen baseline is significantly safer than current 

operations. 

The assumed situational awareness benefit of cockpit displays such as CDTI strongly impacts the 

difference in safety between NextGen environments. Further research is needed to measure the extent to 

which these technologies help pilots to avoid or recover from lapses. 

In scenarios requiring a prompt evasive maneuver to resolve a conflict, ground-based alerting is less 

effective due to the latency in communication between the aircraft and the alerting system. However, if a 

significant fraction of aircraft does not equip an on-board system, a ground-based system that issues alerts 

to all aircraft offers superior safety. Also, in some scenarios, the ground-based system may be able to 

coordinate alerts and resolution advisories among multiple aircraft to a degree that on-board systems 

cannot. 

The sometimes-critical dependence of the conflict encounter outcome on flight crew response time 

demonstrates the need to consider the combined effect of situation awareness, workload, indications, and 

multi-level alerts on outcomes, and to validate the response time models assumed for the study. 

The modeled safety benefits of CD&R depend on the ability to develop crew-vehicle interfaces that 

facilitate quick execution of the appropriate response, matching or exceeding the assumed response time 

distributions under realistic conditions. Integrating the capability for alerts with displays and indications 

that direct attention to the significant threats before they become acute should be a primary research 

focus. 

Among the scenarios examined, performance depended most strongly on the level of equipage for the 

overtaking-taxi scenario, because taxiway CD&R was assumed to be provided only by high-end on-board 

or ground-based alerting systems. For runway collision hazards, partial equipage, as in the NextGen 2 and 

3 environments, offered sufficient benefits because runway alerting was provided at a lower level of 

equipage. 
 93 

7.1 Relation of Safety Modeling Results to CVI 

The modeling analysis results for the Taxi Crossing / Arrival Runway and the Two Aircraft Taxiing 

Along Same Path scenarios demonstrated the potential criticality of pilot response to safety levels for 

NextGen TMA CD&R. The fact that three of five scenarios evaluated were sensitive to pilot response 

time suggests the following: 

All the hypothetical CVI and pilot performance issues described in Section 4.3 are important 

to analyze further. 

CVI and pilot performance issues may be more or less important depending on the 

operational scenario; cases where lead time for alerting are inherently shorter are the most 

critical to empirically analyze. 

CVI equipage intended to enhance SA and pilot response should improve pilot performance, 

but will not necessarily prevent the possibility that other pilot performance issues such as 

high workload or distractions will lead to unsafe CD&R outcomes. 

The pilot response time effects found here need to be validated with empirical studies. Further, the 

relationships between operational situational factors, equipage, and human factors such as workload and 

distraction need to be analyzed more deeply to understand unintended as well as intended effects of 

improvements in CVI equipage. 

Many of the other CVI issues described in Section 4.3 need to be explored with more in-depth modeling 

analyses as well as with empirical study. These include: 

The effects of pilot errors on safety levels; the analyses performed here only addressed 

response times. 

The effects of nuisance alerts, prescriptive commands, etc., on pilot response times and 

errors, especially when there is no visual information (zero-zero visibility) to confirm or 

disconfirm conflict information. 

The effects of strategic and predictive information pertaining to ownship and potential 

conflicts on CD&R safety levels. 

The effects of expected versus unexpected conflict events on pilot response times and errors. 

The trade-offs between better SA displays and the potential to overload pilots with too much 

information. 

Other questions and issues for further research include: 

1. Further research is needed to determine if the log normal curves for pilot response time used in 

the modeling effort accurately reflect the likelihood of response delays and errors under 

potentially higher workload conditions than are typical in today’s environment. 

2. An important research issue to address is pilot response to conflict situations when surveillance 

automation and various information sources add an automation management/awareness task to 

the primary task of assessing the conflict situation. 

3. Research is needed to assure that the aggregate of new systems, equipage, and procedures don’t 

overwhelm pilots with system interaction and training requirements. 

4. Research is needed to address how specific tasks such as CD&R are performed in a full mission 

environment, especially if the conflict is completely unexpected. 

5. A parametric study is needed to determine what distances should be designed into the path 

separations in order that the cross-track conflicts due to highly unlikely errors would be kept to an 

acceptable level of safety. 
 94 

6. NextGen procedures may create new correlation patterns, for example, by reserving a runway or 

an approach corridor for a better-equipped class of aircraft. These interactions should be analyzed 

if the approach used in this study is extended to a more detailed analysis of safety capabilities. 

More specific definitions of assumed procedures, particularly those linked to equipage, and the 

assumed changes in the population of aircraft operations by class or type would be needed to 

perform such an analysis. 

7. Further research is strongly recommended to validate the assumptions used in these scenarios. For 

example, the rate at which blunders are avoided due to improvements in general situation 

awareness strongly affects the incremental benefits to be derived from providing CD&R coupled 

with alerts. 

8. Further research is recommended to determine whether there are “common mode” effects 

reducing the assumed benefit of independent conflict prevent actions. For example, if both A and 

B rely on a single source of information for a critical parameter such as B’s position, and that 

source is incorrect, the probability of a correct response between the two is correlated, violating 

the assumption of independent performance. 

9. Conflicting scenarios, in terms of potential blunder and deviations probabilities, need to be 

addressed empirically as well as with parametric analyses. 
 95 

8. References 

FAA, NextGen Integration and Implementation Office, Implementation Plan 2009, February 10, 2009 (a). 

FAA, Air Traffic Organization, “Annual Runway Safety Support,” 2009 (b). 

FAA, NextGen Implementation Plan, June 2008. 

FAA Notice No. 7–15, “Automatic Dependent Surveillance—Broadcast (ADS–B) Out Performance 

Requirements to Support Air Traffic Control (ATC) Service; Notice of Proposed Rulemaking”, 

October 2007. 

Foyle, David C., Hooey, Becky L., Byrne, Michael D., Corker, Kevin M., Deutsch, Stephen, Lebiere, 

Christian, Leiden, Ken, and Wickens, Christopher D., “Human Performance Models of Pilot Behavior,” 

Proceedings of the Human Factors and Ergonomics Society 49th Annual Meeting, 2005. 

Funk, Ken, Mauro, Robert, and Birdseye, Harvey, “Identifying and Addressing Human Factors Issues of 

ADS-B and Cockpit Displays of Traffic Information,” Presentation at Human Factors and NextGen: 

The Future of Aviation, University of Texas, May 2008. 

Glenn, Floyd, Stokes, James, Neville, Kelly, and Bracken, Kevin, “An Investigation of Human 

Performance Model Validation,” Final Report, Air Force Research Laboratory, 2005. 

Hooey, Becky L., Foyle, David C., and Andre, Anthony D., “A Human-Centered Methodology for the 

Design, Evaluation, and Integration of Cockpit Displays,” Proceedings of the NATO RTO SCI and 

SET Symposium on Enhanced and Synthetic Vision Systems, September 2002. 

Hooey, Becky L., Foyle, David C., and Andre, Anthony D., “Integration of Cockpit Displays for Surface 

Operations: The Final Stage of a Human-Centered Design Approach,” SAE Transactions: Journal of 

Aerospace, 109, 2000. 

Joint Planning and Development Office, Next General Air Transportation System, NextGen Avionics 

Roadmap, Version 1.0, October 24, 2008. 

Joint Planning and Development Office, “Concept of Operations for the Next Generation Air 

Transportation System,” Version 2.0, June 13, 2007. 

Jones, Denise R., Presentation, “Airport Safety,” National Aeronautics and Space Administration, 

NASA/FAA Runway Incursion Prevention System Project, October 2008. 

Kuchar, J. K. and Hansman, R. J., “Probabilistic Analysis of Hazard Situations,” Research Paper, MIT 

Aeronautical Systems Laboratory, 1996. 

Kuchar, James K. and Yang, Lee C., “A Review of Conflict Detection and Resolution Modeling 

Methods,” IEEE Transactions of Intelligent Transportation Systems, Vol. 1, No. 4, December 2000. 

Lee, Seung Man, Remington, Roger, Ravinder, Ujwala, and Matessa, Michael, “Developing Human 

Performance Models Using APEX/CPM-GOMS for Agent-Based Modeling and Simulation,” 

Military Government and Aerospace Simulation Symposium, Society for Computer Simulation, 2004. 

NextGen Mid-Term Implementation Task Force, NextGen Mid-Term Implementation Task Force Report, 

September 9, 2009. 

Pritchett, A. R., Lee, S.M., Abkin, M. H., Gilgur, A. Z., Bea, R. C., Corker, K. M., Verma, S., Jadhav, A., 

Reynolds, T. G., Vigeant-Langlois, L., and Gosling, G., “Examining Air Transportation Safety Issues 

Through Agent-Based Simulation Incorporating Human Performance Models,” IEEE, 2002. 

Reising, Dal Vernon C., Downs, Joshua L., and Bayn, Danni, “Human Performance Models for Response 

to Alarm Notification in the Process Industries: An Industrial Case Study,” Proceedings of the 

Human Factors and Ergonomics Society 48th Annual Meeting, September 2004. 
 96 

RTCA, DO-260A, Minimum Operational Performance Standards for 1090 MHz Automatic Dependent 

Surveillance – Broadcast (ADS-B) and Traffic Information Services (TIS-B), 2003. 

Stanton, N.A., “Human Alarm Handling Response Times,” in Karwowski (ed.): International 

Encyclopedia of Ergonomics and Human Factors, Vol 1. Taylor and Francis, Boca Raton, FL, 2006. 

Tyler, Sherman W., Neukom, Christian, Logan, Michael, and Shively, Jay, “The MIDAS Human 

Performance Model,” Proceedings of the Human Factors and Ergonomics Society, 42nd Annual 

Meeting, 1998. 

Zeitlin, Dr. Andrew, Presentation, “Examination of TCAS II for Collision Warning on Airport Surface,” 

The Mitre Corporation, Center for Advanced Aviation System Development, Case Number: 07-0740, 

January 2006. 
 97 

9. Acronyms and Abbreviations 

ACAS: Airborne Collision Avoidance System 

ADS-B: Automatic Dependent Surveillance—

Broadcast 

ADS-R: Automatic Dependent SurveillanceRebroadcast 

AIA: Alarm Initiated Activity (model) 

ANSP: Air Navigation Service Provider 

ASDE-X: Airport Surface Detection 

Equipment—Model X 

ASR: Airport Surveillance Radar 

ATC: Air Traffic Control 

ATCRB: Air Traffic Control Radar Beacon 

ATM: Air Traffic Management 

CCPC: Conditional Collision Prevention 

Capability 

CDA: Continuous Descent Approaches 

CDTI: Cockpit Display of Traffic Information 

CD&R: Conflict Detection and Resolution 

CVI: Crew Vehicle Interface 

DGPS: Differential GPS 

EFB: Electronic Flight Bags 

EVO: Equivalent Visual Operations 

EVS: Enhanced Visual System 

FAA: Federal Aviation Administration 

FAR: Federal Aviation Regulations 

FIS-B: Flight Information Services –Broadcast 

GA: General Aviation 

GBAS: Ground-based Augmentation System 

GNSS: Global Navigation Satellite System 

GPS: Global Positioning System 

HUD: Head Up Display 

LCGS: Low-Cost Ground Surveillance 

m: meters 

MASPS: Minimum Aviation System 

Performance Standards 

MFD: Multifunction Display 

MLAT: Multi-lateration 

NAC: Navigation Accuracy Category 

NAS: National Airspace System 

NextGen: Next Generation Air Transportation 

System 

NIC: Navigation Integrity Category 

NPRM: Notice of Proposed Rule Making 

R&D: Research and Development 

RAAS: Runway Awareness & Advisory System 

RMS: Root Mean Square 

RNAV: Area Navigation 

RNP: Required Navigation Performance 

RT: Response Time 

RTA: Required Time of Arrival 

RTCA: Radio Technical Commission for 

Aeronautics 

RWSL: Runway Status Lights 

SA: Situation Awareness 

SMR: Surface Movement Radar 

STBO: Surface Trajectory Based Operations 

SURF: Surface 

SURF IA: Surface Indications and Alerting 

SVS: Synthetic Vision System 

TBO: Trajectory Based Operations 

TCAS: Traffic Alert and Collision Avoidance 

System 

TIS-B: Traffic Information Services -Broadcast 

TMA: Terminal Maneuvering Area 

TRACON: Terminal Radar Control 

UAT: Universal Access Transceiver 

VFR: Visual Flight Rules 

VOR: VHF Omnidirectional Range 

WAM: wide area multi-lateration 
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Parametric Modeling of the Safety Effects of NextGen Terminal 

Maneuvering Area Conflict Scenarios

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Rogers, William H.; Waldron, Timothy P.; Stroiney, Steven R.

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14. ABSTRACT

The goal of this work was to analytically identify and quantify the issues, challenges, technical hurdles, and pilot-vehicle

interface issues associated with conflict detection and resolution (CD&R)in emerging operational concepts for a NextGen

terminal aneuvering area, including surface operations. To this end, the work entailed analytical and trade studies focused on

modeling the achievable safety benefits of different CD&R strategies and concepts in the current and future airport

environment. In addition, crew-vehicle interface and pilot performance enhancements and potential issues were analyzed

based on review of envisioned NextGen operations, expected equipage advances, and human factors expertise. The results of

perturbation analysis, which quantify the high-level performance impact of changes to key parameters such as median

response time and surveillance position error, show that the analytical model developed could be useful in making technology

investment decisions.

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Conflict Detection, Resolution, Modeling Analysis, Terminal Maneuvering, NextGen, Aviation Safety 

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