8th USA/Europe ATM R&D Seminar Paper #141: Lateral Intent Error s Impact on Aircraft Prediction Authors: M. Paglione, G. McDonald, Airservices Australia I. Bayraktutar, EUROCONTROL J. Bronsvoort, Airservices Australia Presented to: Presented By: ATM2009 R&D Seminar Mike Paglione, FAA Date:
Presentation Overview Background - Motivation Trajectory Based Operations Trajectory Prediction Process Lateral Intent Flight Examples Lateral Deviation Metrics Measurements on ATC Operational Data Impact on Conflict Predictions Measurements on Airborne Operational Data Conclusions 2 2
Background and Motivation Trajectory Based Operations central to NextGen, SESAR, and Australian ATM Strategic Plan Trajectory Based Operations Requires precise management of aircraft s current & future position Thus, trajectory prediction needs to be more accurate than today Lateral intent is a key component of this TP process Flight Plan: AAA123 B752 0450 310 XXX..ABC..DEF.BUC7.XYZ Initial condition XXX Lateral path initialization Aircraft trajectory modeling ABC Route Conversion x(t),y(t),z(t Constraint Specification XYZ DEF BUC7 3 3
Flight Example 1 Commercial carrier flying a B737-300 From Cleveland Ohio - To Denver Colorado Climbs to FL340 after a series of cleared steps Enters Denver en route center at FL 340 Starts smooth descent into Denver airport after crossing radial distance 2 nm from AMWAY fix follows SAYGE6 arrival 4 4
Flight Example 1 Continued 5 5
Flight Example 1 Continued Focused on arrival phase into Denver ARTCC ATC issues horizontal path stretch not entered into ground automation Y - coord (nm ) 650 600 550 500 450 Actual Path via Radar Reports Predicted Trajectory Trajectory prediction exhibits large cross and along route errors 400 350 450 500 550 600 650 700 750 X-coord (nm) 6 6
Flight Example 1 Continued 480 First Track Point, 83360s 475 Track TimeCoTraj81960 Y - coord (nm ) 470 465 Spatially Coincident Trajectory Point, 83625s SpatialCoTraj81960 460 455 570 575 580 585 590 595 X-coord (nm) Time Coincident Trajectory Point, 83360s HORZ_ERR LAT_ERR LONG_ERR VERT_ERR CROSS_TRK_ERR ALONG_TRK_ERR TIME_ERR 16.9858 16.5285-3.9148 0 16.5347-3.9148-34 7 7
Flight Example 2 Commercial carrier flight recorded in Washington ARTCC (ZDC) Origin: Dallas Fort Worth, Texas Destination: John F. Kennedy International Airport, New York Hand-off into ZDC at 20:14 UTC and outbound to New York ARTCC at 20:56 UTC during brief cruise at FL 240 8 8
Flight Example 2 Cont d TP generated 34 trajectories Sampled 18 of the trajectories to produce 109 measurements Focus on trajectory build 74005 seconds (20:33:25 UTC) with 5 measurements below Sample Time Measurement Time Look Ahead Time Horizontal Error Crosstrack Error Alongtrack Error Vertical Error Clear Flag Seconds Seconds HH:MM:SS Seconds Nautical Miles Nautical Miles Nautical Miles Feet 74040 74040 20:34:00 0 0.4 0.3-0.3 0 0 74040 74340 20:39:00 300 0.1-0.1 0.0 793 1 74040 74640 20:44:00 600 1.2-0.5-1.0 0 1 74040 74940 20:49:00 900 2.1-0.1 2.1 2096 1 74040 75240 20:54:00 1200 34.6 11.9-32.5 6952 1 9 9
Lateral Deviation Metrics Direction of flight track d n β d r α Cleared route d a Threshold D 1 D 2 D 3 P 1 Value (units) 0.5 (nm) 1.5 (nm) 1.0 (nm) 30 (deg) 10 10
Measurements from ATC Operational Data: United States En Route Facilities One day seven hours of traffic All 20 ARTCCs 50,000 flights Over 8M measurements Airspace Source Descriptive Summary Statistics Percentiles (nm) Sample Size 25 th 50 th 75 th United States Airspace: Center Data ZAB 427361-0.399 0.014 0.562 1.352 15.326 ZAU 435974-0.406 0.050 0.846 2.238 15.706 ZBW 303583-0.570 0.081 1.700 3.727 22.329 ZDC 565728-0.319-0.013 0.356 1.795 13.964 ZDV 490275-0.267 0.063 0.765 3.770 29.597 ZFW 384097-0.809 0.039 0.951 1.266 12.797 ZHU 421271-0.607 0.045 0.890 2.151 20.797 ZID 430507-0.376 0.059 0.671 1.371 10.867 ZJX 540701-0.714 0.056 1.100 1.361 11.486 ZKC 443290-0.571 0.041 0.977 2.943 24.252 ZLA 367723-0.337 0.014 0.743 5.652 26.665 ZLC 348567-0.458 0.015 0.545 2.704 22.985 ZMA 377355-1.000 0.068 2.100 6.397 44.761 ZME 437666-0.481 0.034 0.844 2.333 18.434 ZMP 404147-0.417 0.043 0.845 3.548 23.644 ZNY 258725-0.352 0.057 0.723 1.803 13.986 ZOA 227412-0.412 0.037 0.726 4.075 22.201 ZOB 472835-0.418 0.005 0.630 1.356 10.306 ZSE 207031-0.360 0.038 0.536 2.113 20.574 ZTL 566839-0.535 0.048 0.820 1.734 13.964 Avg 405554-0.493 0.039 0.857 2.579 20.816 Mean (nm) Std Dev (nm) 11 11
Measurements from ATC Operational Data: Cluster Analysis Results ZAB ZNY ZID ZOB ZDC ZAU ZTL ZBW ZFW ZJX ZDV ZLA ZLC ZOA ZMP ZSE ZHU ZME ZKC ZMA Standard Deviation (nautical miles) 50 45 40 35 30 25 20 15 10 5 0.5 1 1.5 2 2.5 3 Interquartile Range (IQR - nautical miles) 12 12
Measurements from ATC Operational Data: Geography of Clusters ZMP ZSE ZOA ZLC ZDV ZKC ZAU ZID ZOB ZNY ZBW ZDC ZLA ZAB ZFW ZME ZTL ZJX ZHU ZMA 13 13
Measurements from ATC Operational Data: Precipitation Weather Map of Same Date 14 14
Measurements from ATC Operational Data: Distributions of Three ARTCCs 15 15
Measurements from ATC Operational Data: Three ARTCC s Lateral Adherence States Frequency of Measurements 240000 220000 200000 180000 160000 140000 120000 100000 80000 60000 40000 20000 49% 47% 49% 32% 25% 30% 16% 13% 12% 9% 8% 9% 0 innerinconf midinconf midnonconf outernonconf innerinconf midinconf midnonconf outernonconf innerinconf midinconf midnonconf outernonconf ZID ZM P ZM A Lateral Adherence Status within ARTCC 16 16
Measurements from ATC Operational Data: Lateral Deviation Statistics from Europe EUROCONTROL s Flight Data Management Metrics Project published report July 2007 Analyzed data set from November 2006 Approximately 27,000 flights from EUROCONTROL s Central Flow Management Unit Supplied by 31 European Air Traffic Service Providers (ANSPs) Utilized a software tool called EUROCONTROL Flight Information Consistency Analysis Tool (EFICAT) Results for two dimensional route analysis Grouped into major deviations > 50 nm off route and minor deviations between 20 and 50 nm Of 27,300 sample measurements 19% - minor deviations with average lateral deviation of 30 nm 3% - major deviations with average lateral deviation of 73 nm 17 17
Impact on Conflict Predictions Conflict Event Counts For Each Alert Lateral Adherence State State In Out Alert No Alert Totals 106 102 111 97 12 12 7 27 118 114 χ 2 =5.04, df=1; p-value=0.025 Both conflict and non-conflict predictions effected by lateral adherence state. Alert State Alert No Alert Totals Totals 208 24 232 In 7645 Beyond paper applied method to operational conflict probe Encounter Event Counts For Each Lateral Adherence State 232 7413 Out 137 307 444 7508 6654 14162 6961 χ 2 =84.742, df=1; p-value=0.000 212 6749 Totals 14606 18 18
Impact on Conflict Predictions: Sensitivity Analysis - Terms Probability of Alert ratio of: Number of conflict predictions (a.k.a. alerts) for min-max-ratio range to Number of non-conflict events for same min-max-ratio range Min-max-ratio Unit-less distance combines both dimensions of separation and directly corresponds to standard separations. λi Calculated on each position for each aircraft pair combo, such that: = ( ) [ ] ρ = min max λ, π i k i i ( x a i x b i ) + ( y a i y b i ) π i = zi a 2 2 δi zi b υ i where th δi = horizontal separation standard for the i synchronized track data point; a th xi = x position of the i track point of aircraft a in nautical miles; b th xi = x position of the i track point of aircraft b in nautical miles; a b and yi, yi are the corresponding y positions th υi = vertical separation standard for the i synchronized track data point; a th zi = altitude position of the i track point of aircraft a in feet; b th zi = altitude position of the i track point of aircraft b in feet. th i = current i track point ; k = total number of track points 19 19
Impact on Conflict Predictions: 1 Sensitivity Analysis 0.9 Probability of Alert (#Alerts/#Encounters) 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 All InConf OutConf FitCurve(OutConf) FitCurve(All) FitCurve(InConf) 0 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 Min-Max-Ratio Separation Factor 20 20
Measurements from Airborne Operational Data From Airservices Australia ADS-C data 778 flights 58 flights of type Airbus A330-300 168 flights of type Airbus A340-500 258 flights of type Boeing 747-400 294 flights of type Boeing 777-300 Avg. 34.4 reports/flight COUNT 2000 1500 1000 500 0-0.3-0.25-0.2-0.15-0.1-0.05 0 0.05 0.1 0.15 0.2 LATERAL DEVIATION [nm] 21 21
Measurements from Airborne Operational Data: Summary from Both U.S. & Australia Descriptive Summary Statistics Airspace Sourc e Percentiles (nm) Sample Size 25 th 50 th 75 th Mean (nm) Std Dev (nm) United States Airspace: ADS-C Data a U.S. 39012-0.018-0.002 0.007-0.001 0.184 Australian Airspace: ADS-C Data A.A. 26731-0.019-0.002 0.015-0.003 0.026 22 22
Conclusions TBO concepts will require accurate TP Missing lateral intent is significant TP error source Metrics defined to capture lateral intent state Large samples of ground automation measurements analyzed in both U.S. and Europe U.S. reported lateral errors with an overall standard deviation of about 21 nm and IQR of 1.35 nm Europe reported 19% of their flight sample had lateral errors of 30 nm on average Impact on conflict predictions significant Airborne ADS-C data measures from Australia and U.S. much more precise 100 to 1000 times! Global challenge needs global collaboration 23 23