HIGH DENSITY EN ROUTE AIRSPACE SAFETY LEVEL AND COLLISION RISK ESTIMATION BASED ON STORED AIRCRAFT TRACKS

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1 HIGH DENSITY EN ROUTE AIRSPACE SAFETY LEVEL AND COLLISION RISK ESTIMATION BASED ON STORED AIRCRAFT TRACKS (EIWAC 2010) + E. Garcia*, F. Saez**, R. Arnaldo** * CRIDA (ATM Research, Development and Innovation Reference Centre) Madrid, Spain egarcia@crida.es **Air Navigation Department Universidad Politécnica de Madrid (UPM) Madrid, Spain franciscojavier.saez@upm.es rosamaria.arnaldo@upm.es

2 CRIDA an Initiative from Aena (ATM Research, Development and Innovation Reference Centre) University Spanish Air Navigation Service Provider Result Oriented Scientific Rigour Flexibility Transport Engineering Company CRIDA. CRM 3D. ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010) 11/10/2010 2

WORK IN PROGRESS CRIDA. CRM 3D. ENRI Int.

3 Outline INTRODUCTION NEED FOR A NEW COLLISION RISK MODEL BACKGROUND 3D CRM GOAL 3D CRM GENERAL DESCRIPTION RADAR DATA PROCESSING SAFETY METRICS CONCLUSIONS AND WORK IN PROGRESS CRIDA. CRM 3D. ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010) 11/10/2010 3

INTRODUCTION EUROCONTROL has worked in the last years to develop the 3-D collision risk model (CRM).

continental en-route airspace, where controllers monitor air traffic by means of radar surveillance and provide

produce an estimation of the level of safety achieved in the airspace under assessment but also to provide

4 INTRODUCTION EUROCONTROL has worked in the last years to develop the 3-D collision risk model (CRM). The 3-D collision risk model was developed as a general mathematical framework to assess the level of safety in continental en-route airspace, where controllers monitor air traffic by means of radar surveillance and provide aircraft with tactical instructions ATCO The objective of the software prototype tool is not only to eventually produce an estimation of the level of safety achieved in the airspace under assessment but also to provide safety-related metrics and trends, which can be monitored over time. CRIDA. CRM 3D. ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010) 11/10/2010 4

NEED FOR A NEW COLLISION RISK MODEL Previous models no not appropriate assess and monitor the level of safety in high density en-route radar airspaces using as a sole source of input data the

Traditional approaches to Collision Risk Models (CRM), do not capture the complexity inherent to an operational radar environment like the one in Europe, with high amount of traffic, a large number

5 NEED FOR A NEW COLLISION RISK MODEL Previous models no not appropriate assess and monitor the level of safety in high density en-route radar airspaces using as a sole source of input data the recorded aircraft trajectories. Traditional approaches to Collision Risk Models (CRM), do not capture the complexity inherent to an operational radar environment like the one in Europe, with high amount of traffic, a large number of crossings tracks, climbing and descending aircrafts and complicated route structure. PHASE EN-ROUTE TMA MODELO Airway structure consisting of one or more parallel routes. No radar surveillance. Improvement of Collision Risk Models Parallel routes. Surveillance base don ADS. CRM Reich (1960) Extended Reich (1993) CRM ADS (1993) Precision approaches CRM ILS (1980) Landing on closely and ultra closely spaced runways Future operational concepts BRM (1990) MIT (1997) NLR (2001) CRM CRIDA. CRM 3D. ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010) 11/10/2010 5

6 BACKGROUND Nowadays, ANSP and Civil Aviation Authorities (CAA) mainly use ATM accident and incident databases to monitor and provide evidence of levels of safety. However, although these databases are very powerful tools and are improving constantly, they still have some weak points that need to be considered: Not all incidents are reported by pilots and air traffic controllers. In fact, it is very difficult to infer how many real incidents have occurred for each one that is reported. Incident severity is generally ranked solely on how close aircraft get, without considering the geometry of the event or other parameters, e.g. closure rate. Incident Classification is not homogeneous in all databases. Furthermore, special care has to be taken to train database personnel so that the same classification criteria always apply. CRIDA. CRM 3D. ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010) 11/10/2010 6

BACKGROUND Airspace information E.g. AIS, Capacity, Demand Meteo Information E.g.: METAR, Airports Data E.

gg: RTS, FTS, Aviation accidents and Incidents Aviation Maintenance Incident Database

:radar data, ADS, CRM 3D ANALYSIS, ASSESSMENT AND MONITORING OF OPERATIONAL SAFETY CRIDA.

7 BACKGROUND Airspace information E.g. AIS, Capacity, Demand Meteo Information E.g.: METAR, Airports Data E.g.: AIS, ATC Procedures Aircraft Performances E.g.. BADA, MANAGEMENT AND ANALYSIS OF INFORMATION Aircraft trajectories from Simulations E.gg: RTS, FTS, Aviation accidents and Incidents Aviation Maintenance Incident Database Real Aircraft Trajectories E.g. :radar data, ADS, CRM 3D ANALYSIS, ASSESSMENT AND MONITORING OF OPERATIONAL SAFETY CRIDA. CRM 3D. ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010) 11/10/2010 7

3D CRM GOAL A major objective of the 3-D CRM tool is to complement the information collected in the accident and incident databases, thereby providing: 1.

8 3D CRM GOAL A major objective of the 3-D CRM tool is to complement the information collected in the accident and incident databases, thereby providing: 1. Identification of all proximate events based on radar data. 2. Complete classification of all proximate events using clear and consistent criteria. 3. Detailed information on the evolution of each proximate event. 4. Collision risk estimate CRIDA. CRM 3D. ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010) 11/10/2010 8

9 Traffic Sample 3D CRM GENERAL DESCRIPTION 3D CRM Analytical Model Airspace Scenario 3D CRM Radar Analysis Tool Safety Level Estimation Safety Event Reports CRIDA. CRM 3D. ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010) 11/10/2010 9

3D CRM GENERAL DESCRIPTION Radar Data Processing Module Parameter Estimation Module Airspace Scenario Radar Data Files Identify Proximate Events Analyse and

10 3D CRM GENERAL DESCRIPTION Radar Data Processing Module Parameter Estimation Module Airspace Scenario Radar Data Files Identify Proximate Events Analyse and Classify Events Probability of Risk Other Safety Metrics Segmentation Coarse Filter CRIDA. CRM 3D. ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010) 11/10/

11 Y (m) Y (m) Y RADAR DATA PROCESSING The radar data processing module comprises two different : decoding and storage of radar track and flight plan files, and track segmentation of radar data. 5 x x X X (m) x X x 10 5 X (m) CRIDA. CRM 3D. ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010) 11/10/

12 RADAR DATA PROCESSING GOAL ->Identify Proximate Events: Conflicts Potential Conflicts Potential Collisions Collision: Aircraft physical separation < aircraft physical sizes R Conflict: Aircraft physical separation < intended margin (D, H) 2 H CRIDA. CRM 3D. ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010) 11/10/

13 RADAR DATA PROCESSING Look-ahead time (LAT): is the time horizon within which all aircraft positions are projected to explore existence of potential conflicts/collision. -> 10 minutes D* CPA CRIDA. CRM 3D. ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010) 11/10/

14 RADAR DATA PROCESSING MAP of HOT SPOTS CRIDA. CRM 3D. ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010) 11/10/

Systems Identification of ATM System Weaknesses CRIDA. CRM 3D.

15 SAFETY METRICS ESTIMATION Analysis of Proximate Events Classification of Proximate Events Detection of Activated Alert Systems Identification of ATM System Weaknesses CRIDA. CRM 3D. ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010) 11/10/

16 SAFETY METRICS ESTIMATION Analysis of Proximate Events CPA(t cpa,[r 12 ] cpa,[h 12 ] cpa ) v 2 t cpa : time at CPA [r 12 ] cpa : horizontal separation at CPA [h 12 ] cpa : vertical separation at CPA v 1 [r 12 ] cpa <R & [h 12 ] cpa <H t CI t CI : time of identification of potential conflict CRIDA. CRM 3D. ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010) 11/10/

SAFETY METRICS ESTIMATION Analysis of Proximate Events New CPA*(t* cpa,[r 12 ]* cpa,[h 12 ]* cpa ) v 2 Change in the Rate of Climb (ROC) v 1 * t cpa *: new time at CPA [r 12 ] cpa *: new horizontal

17 SAFETY METRICS ESTIMATION Analysis of Proximate Events New CPA*(t* cpa,[r 12 ]* cpa,[h 12 ]* cpa ) v 2 Change in the Rate of Climb (ROC) v 1 * t cpa *: new time at CPA [r 12 ] cpa *: new horizontal separation at CPA [h 12 ] cpa *: new vertical separation at CPA [r 12 ] cpa * <R & [h 12 ] cpa * <H t CI t m t m : time of maneuvre CRIDA. CRM 3D. ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010) 11/10/

18 SAFETY METRICS ESTIMATION Analysis of Proximate Events v 2 Climb->Level v 1 [h 12 ] cpa : vertical separation at CPA [h 12 ] cpa >H t CI t m t CR t m : time of a conflict resolution manoeuvre CRIDA. CRM 3D. ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010) 11/10/

19 SAFETY METRICS ESTIMATION Analysis of Proximate Events H r 12 : actual horizontal separation h 12 : actual vertical separation r 12 <R & h 12 =H t CI t m t CF t m : time to entry into conflict volume CRIDA. CRM 3D. ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010) 11/10/

SAFETY METRICS ESTIMATION Classification of Proximate Events CRITERIA PROXIMATE EVENT NATURE TRAFFIC TYPE VERTICAL REGIME RELATIVE HEADING A/C REACTION ACTIVATED ALERT SYSTEM Passing Event Civil

20 SAFETY METRICS ESTIMATION Classification of Proximate Events CRITERIA PROXIMATE EVENT NATURE TRAFFIC TYPE VERTICAL REGIME RELATIVE HEADING A/C REACTION ACTIVATED ALERT SYSTEM Passing Event Civil Traffic Level-Level (LL) Same Change Vertical Profile TCAS RA Conflict Both Military Aircraft Climb-Level(CL) Opposite Modify Heading TCAS TA Potential Conflict One Military Aircraft Descend-Level(DL) Crossing Change of Speed STCA Potential Collision Climb-Climb(CC) No Reaction No Alert False Detection Climb-Descend(CD) Descend-Descend(DD) CRIDA. CRM 3D. ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010) 11/10/

21 SAFETY METRICS ESTIMATION Detection of Activated Alert Systems t 3 :Time-to-go to CPA t 2 :Time to Conflict Aircraft 2 t CI t CR t 1 : Overall Reaction Time t CF t CPA [r 12 ] CPA Aircraft 1 R t CI : identification of potential conflict t CR : start of a conflict resolution manoeuvre t CF : predicted time of entry into conflict volume t CPA : predicted time of CPA [r 12 ] cpa : horizontal separation at CPA Predicted trajectory t 1 =t CR -t CI t 2 =t CF -t CI t 3 =t CPA -t CI CRIDA. CRM 3D. ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010) 11/10/

22 Risk) SAFETY METRICS ESTIMATION Detection of Activated Alert Systems TIME TO CPA Potential Collision TIME TO CPA STRATEGIC Yes TACTICAL 4 mins No P(Pre Tactical Failure ) Yes No P( Tactical Failure ) TACTICAL BASED ON STCA Safety Barriers Yes 2 mins No P( STCAFailure ) TCAS TA Yes No P( TCAS TAFailure ) 48 sec TCAS RA (See & Avoid,..) Yes 35 sec No P( TCAS RAFailure ) No loss of Sep. No loss of Sep. No loss of Sep. Loss of Separation? Loss of Separation Collision 4 mins 2 mins CRIDA. CRM 3D. ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010) 11/10/ sec 35 sec

23 VERTICAL SEPARATION (feet) SAFETY METRICS ESTIMATION Detection of Activated Alert Systems 2 min 48 sec Strategic ATC Action Tactical ATC Action (STCA) Traffic Advisory (TA) 35 sec H Resolution Advisory (RA) VERTICAL CLOSURE RATE (fpm) CRIDA. CRM 3D. ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010) 11/10/

Y Y Altitude (FL) SAFETY METRICS ESTIMATION Detection of Activated Alert Systems HORIZONTAL PLANE VERTICAL PLANE x 10 5 380 1 370 0.5 360 0.0 fpm 0 RA 350 RA -0.5 340 1875.0 fpm 330-1 320-5 -4.

24 Y Y Altitude (FL) SAFETY METRICS ESTIMATION Detection of Activated Alert Systems HORIZONTAL PLANE VERTICAL PLANE x fpm 0 RA 350 RA fpm X x kts time (h) Time (h) CRIDA. CRM 3D. ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010) 11/10/

25 Proximate Event Data: AC 1 - AC 2 Tm [r 12 ] cpa (NM) [h 12 ] cpa (ft) TTC (min) Dur (min) TCPA rel Manoeuvre AC1 Manoeuvre AC2 Legend T >Conv(85º)< T Turn 5.7º AC1 Turn ; >< T TCAS RA CL FL AC2 CL FL ; VER SEP; >Conv(88º)< T CONFLICT RESOLVED-VERTICAL PLANE >Conv(93º)< T MIN REAL SEP: 0.3 NM 1000 ft <Div(99º)> T CRIDA. CRM 3D. ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010) 11/10/

26 Horizontal Distance (nm) Vertical Separation (ft) Resolution Advisory Traffic Advisory Tactical ATC Action (STCA) Strategic ATC Action T Resolution Advisory Traffic Advisory Tactical ATC Action (STCA) Strategic ATC Action T1 5 T3 T Horizontal Closure rate (kts) T4 T1 T Vertical Closure Rate (fpm) As an example, this figure represents in the TCAS and STCA diagram the evolution of the encounter described beforte. A TCAS RA is activated if the kinetic and geometric characteristics of the event in the horizontal plane and in the vertical plane are in the red area at the same time. CRIDA. CRM 3D. ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010) 11/10/

27 SAFETY METRICS ESTIMATION Identification of ATM System Weaknesses Risk Context Qualification of Hazards Effectiveness of Safety Barriers Initiating events leading to potential collisions Detection / Resolution of potential collisions Traffic Density + Traffic complexity + ATC/Pilot Errors + Technical Failures HAZARD (potential collision) ATCO STCA TCAS No Safety Effect Conflict Collision CRIDA. CRM 3D. ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010) 11/10/

28 SAFETY METRICS ESTIMATION Identification of ATM System Weaknesses Risk Context (within selected area/timeframe) Effectiveness of Safety Barriers Flight-Time Hot Spots Number of Real Conflicts Number of Movements Kinematics (Speed, Type of Aircraft) Classification of Proximate Events: Nature Overall Reaction Time Time Margin Nº of Entries and Exits Route structure Traffic Type Vertical regime Relative heading A/C Reaction Time to Conflict Traffic Density Activated Alert System (TCAS, STCA) Number of Routes Correlation between Hot Spots and Traffic Density Maps. % Evolving (non-level) Aircraft Nº of potential collisions Separation Rules Qualification of Hazards Time-to-go to the CPA % of Potential Conflicts solved in: vertical plane and horizontal plane Average Nº of Aircraft near a Proximate Event CRIDA. CRM 3D. ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010) 11/10/

29 SAFETY METRICS ESTIMATION Identification of ATM System Weaknesses Risk Context (within selected area/time-frame) Side Length=7.5 NM CRIDA. CRM 3D. ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010) 11/10/

30 SAFETY METRICS ESTIMATION Qualification of Hazards x x Total Number of Proximate Events: CIVIL-MILITARY TRAFFIC 5.6% MILITARY TRAFFIC 3.2% Total Number of Proximate Events: DESCENT-DESCENT 1.2% CLIMB-DESCENT 6.4% LEVEL-LEVEL 13.8% CLIMB-CLIMB 11.9% 3 Total Number of Proximate Events: PASSING EVENT 0.46% 2 FALSE DETECTION 14.59% LEVEL-DESCENT 7.1% 1 1 ACTUAL CONFLICT 0.08% CIVIL TRAFFIC 91.2% 0 0 LEVEL-CLIMB 59.6% POTENTIAL CONFLICT 84.86% CIVIL-MILITARY TRAFFIC MILITARY TRAFFIC CIVIL TRAFFIC TCAS RA x 10 5 x 10 5 CRIDA. CRM 3D. ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010) 11/10/

31 Time-to-go to the CPA (min) SAFETY METRICS ESTIMATION Effectiveness of Safety Barriers TCAS TA 2.1% TCAS RA 0.3% Time-to-go Overall Reaction to CPA Time (t3) (t1) STCA 28.0% ATC STRATEGY 69.6% Overall Time-to-go Overall Reaction Reaction to CPA Time Time (min) (min) CRIDA. CRM 3D. ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010) 11/10/

CONCLUSIONS The 3-D CRM tool has been designed to complement the information collected in the accident and incident databases, thereby providing the following information inferred from the in depth

32 CONCLUSIONS The 3-D CRM tool has been designed to complement the information collected in the accident and incident databases, thereby providing the following information inferred from the in depth assessment of proximate events: Identification of all proximate events based on radar data. Complete classification of all proximate events using clear and consistent criteria. Detailed information on the evolution of each proximate event. Safety metrics and other air traffic factors CRIDA. CRM 3D. ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010) 11/10/

WORK IN PROGRESS Apply the 3-D CRM too to traffic samples of different airspaces and extend the principles of 3-D CRM from en-route to Terminal Manoeuvring Area (TMA)

barriers, using all the information provided by the 3-D CRM tool that could be used by ATM service providers to monitor and improve safety levels in their operation;

33 WORK IN PROGRESS Apply the 3-D CRM too to traffic samples of different airspaces and extend the principles of 3-D CRM from en-route to Terminal Manoeuvring Area (TMA) scenarios; Develop a methodology to provide a complete risk picture of the scenario, identifying the ATM system weakness and characterizing the performance of the safety barriers, using all the information provided by the 3-D CRM tool that could be used by ATM service providers to monitor and improve safety levels in their operation; Complete an analytical model based on the 3-D CRM tool to provide true collision risk values. CRIDA. CRM 3D. ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010) 11/10/

34 CRIDA. CRIDA: CRM General 3D. DescriptionENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010) 11/10/ Questions CRIDA (Centro de Referencia I+D+i ATM)

A Bayesian. Network Model of Pilot Response to TCAS RAs. MIT Lincoln Laboratory. Robert Moss & Ted Londner. Federal Aviation Administration

A Bayesian. Network Model of Pilot Response to TCAS RAs. MIT Lincoln Laboratory. Robert Moss & Ted Londner. Federal Aviation Administration A Bayesian Network Model of Pilot Response to TCAS RAs Robert Moss & Ted Londner MIT Lincoln Laboratory ATM R&D Seminar June 28, 2017 This work is sponsored by the under Air Force Contract #FA8721-05-C-0002.