Sector Workload Model for Benefits Analysis and Convective Weather Capacity Prediction

Transcription

1 Sector Workload Model for Benefits Analysis and Convective Weather Capacity Prediction J.D. Welch, J.Y.N. Cho, N.K. Underhill, and R.A. DeLaura 1 June 213 This work is sponsored by the Federal Aviation Administration under Air Force Contract #FA C-2. Opinions, interpretations, recommendations and conclusions are those of the authors and are not necessarily endorsed by the United States Government Sector Workload Model 1

2 Introduction En route sector capacity definition Peak safe instantaneous traffic count based on controller workload Capacity estimates determined from three sector attributes Introduced model in 27, extended to handle weather in 211 New results Focus: En Route Airspace Sector Attributes Transit Sector Alt Chng Time Volume Fraction T (sec) Q (nm 3 ) Fca 583 5, , ,651.7 Regression against definitive peak sector count data Improved modeling of aircraft with vertical rates Ambiguities limit use in benefits analyses Simple normalized model for convective weather Sector Workload Model 2

3 Outline Current FAA Capacity Model Full Capacity Model Benefits Analysis Hazardous Weather NAS Monitor Sector Workload Model 3

4 21361 Sector Workload Model 4 NAS* Monitor Capacity Monitor Alert Parameters (MAP) Sector capacities Advantages Simple based on transit (hand-off) workload one workload type one sector attribute one unknown Accurate where transit workload dominates Disadvantages Sector capacities must be manually tuned Other workload types important in benefits analyses hazardous weather * NAS = National Airspace System NMAP = T/t t

5 Aircraft Count Workload Basis of MAP Rule Transit Service Time Transit Workload Intensity Gt = t t [ N/T ] (Fraction of controller time) Occurrence Rate 3 NAS To calculate capacity set G t = 1 25 MAP Operational Settings Nmap + 3 Nmap adjusted upper traffic limit solve for N: 2 N MAP = T/t t 15 Transit Service Time specified by MAP slope 1 68 NAS Sectors t t = 36 s Sector Workload Model Transit Time (sec)

6 Aircraft Count MAP Upper Bound and NAS Peak Traffic 4 SDAT* NAS MAP(tt= 36, NASCap= 19.6, %= 97.1) 35 MAP Model Capacity Peak Count 3 25 Observed peak traffic counts Low demand sectors Transit Time (seconds) MAP model fits best in linear growth range Upper limit adjusted to observed peak count saturation Not accurate for sectors with capacity reduced by other workload types Sector Workload Model 6 * SDAT = Sector Design Analysis Tool

7 Throughput, aircraft/hour Throughput, aircraft/hour Throughput (Flow Rate) In linear growth region: N MAP = T/36 F MAP = N MAP /T = 1 /hr MAP Peak throughput, F MAP = N MAP /T F MAP = 1 aircraft/hour Peak Daily Throughput of NAS Sectors vs Transit time MAP Throughput FMAP Observed Throughput 6 4 Observed Flow Rates at time of peak traffic MAP rule constrains flow Transit Time, T (sec) NAS 14 NAS Model Throughput Observed Throughput Full Model Flow Capacity Sector Workload Model Transit time (sec)

8 Aircraft Count Outline Current FAA Capacity Model Full Capacity Model Benefits Analysis Hazardous Weather 4 35 SDAT NAS(tt= 9, dc= 1, dcmax= 2.4, tr= 13, trmax= 2.8, NASCap= 17.1, %= 95.2) Model Capacity Peak Count Sector Workload Model Sector Volume

9 Workload Intensity Sector workload intensity Full Capacity Model G = G b + G c + G r + G t Background Conflict Recurring Transit Fraction of controller time Service times (unknown) G c = t c [(2 N 2 /Q) M h M v V 21 ] G r = t r [N/P] G t = t t [N/T] Occurrence rates Attributes T = 87 s Q = 29, nmi 3 Fca =.5 Unknowns Gb =.1 t c V 12 = 1.5 nmi t r /P=.15 t t = 14 s NAS Mean Sector Human Workload Limit Gh Background Gb Capacity 17.8 aircraft Aircraft Count Unknown parameters determined by regression Sector Workload Model 9

10 Aircraft Count New Regression Database FAA SDAT* data for all 2 centers in NAS From Calendar Year 27 (1 peak demand-days for each center) Data Sample New field Sector Peak Transit Sector Alt Chng ID Count Time Volume Fraction Np T(sec) Q (nm 3 ) Fca ZAB , ZAB ,46.75 ZAB ,651.7 ZAB , ZAB ,78.26 ZAB , ZAB , ZAB , ZAB ,72.43 ZAB , ZAB , ZAB , ZAB , ZAB , ZAB ,83.24 ZAB ,61.26 ZAB , ZAB , ZAB , Example Peak Counts Np versus Altitude Change Fraction Fca 917 total sector-days SDAT NAS Peak Daily Counts Altitude Change Fraction Database includes highcapacity terminal sectors Peak count frontier decreases with Fca Sector Workload Model 1 (daily fraction of aircraft with 2 ft altitude change) * Sector Design Analysis Tool (SDAT)

11 Regression Score Regression Cost Function and Percentile Fit Regression Objective Function 1 5 Reward - TERMINAL LOW DEMAND To compare model accuracies - use identical percentiles To control the percentile - adjust the Penalty cost Penalty Difference Between Model Capacity and Sector Peak Count Penalty = -9, 95% Penalty = -15, 99% Penalty = -5, 9% Sector Workload Model 11

12 Aircraft Count Aircraft Count Vertical Miss Distance Mv (ft) Recurring Service Time tr (s) Accounting for Altitude Change Fraction (Fca) Altitude changes cause uncertainties: Start time? Climb rate? Final altitude? 18 BEFORE: Old Fca mechanism (see paper) Increase Vertical Miss Distance 25 AFTER: Add new Fca mechanism Increase Recurring Service Time M vmax = 16 ft (from regression) t rmin = 13 s t rmax =2.8 s (from regression) Altitude Change Fraction Fca BEFORE SDAT NAS(tt= 14, dc= 1.5, dcmax= 2.4, tr= 9, trmax= 9, NASCap= 17.8, %= 94.9) 4 Model Capacity 35 Peak Count Altitude Change Fraction Fca AFTER SDAT NAS(tt= 9, dc= 1, dcmax= 2.4, tr= 13, trmax= 2.8, NASCap= 17.1, %= 95.2) 4 Model Capacity 35 Peak Count Altitude Change Fraction Sector Workload Model Altitude Change Fraction Increasing t r with Fca reduces model error

13 Workload Intensity Workload Intensity Impact of New Fca Mechanism on Workload Balance NAS Mean Sector Before Human Workload Limit Gh Sector Workload Model Background Gb Aircraft Count After NAS Mean Sector Human Workload Limit Gh Background Gb Aircraft Count Transit workload dominates Recurring workload dominates

14 Instance Count Model Performance (New Fca Mechanism) 1 9 SDAT NAS (Full,tt=9,dc=1-2.4,tr=13-2.8,%=95.2,NASCap=17.1) REWARD sectors PENALTY sectors Difference between Model Capacity and Peak Count (Nm - Np) Statistics REWARD sector count Cr = 225 PENALTY sector count Cp = 43 Cr/Cp = 5.93 (Nm-Np) mean = Sector Workload Model 14

15 Accuracy Comparison MAP model, Old Fca Model, New Fca Model Instance Count All Models: Error relative to SDAT NAS data, ~ 95-Percentile Fit MAP, Cr/Cp=4.46, m=5.99 Old Fca, Cr/Cp=5.6, m=5.55 New Fca, Cr/Cp=5.93, m= Difference between Model Capacity and Peak Count (Nm - Np) MAP model provides useful estimates near capacity bound Model accuracy increases with complexity Sector Workload Model 15

16 Outline Current FAA Capacity Model Full Capacity Model Benefits Analysis Hazardous Weather Sector Workload Model 16

17 NextGen benefits Benefits Analysis Digital Data Com, ADS-B, Decision Support Tools,... Example: Conflict rate proportional to miss distances M h and M v Conflict rate l c = (2 N 2 /Q) M h M v V Sector Workload Model 17 If ADS-B or Digital Data are shown to reduce M h and M v, model can compute resulting increase in sector capacity

18 Conflict Service Distance d (nautical miles x 1) Regression Ambiguity Problem Causes of ambiguity Excess degrees of freedom T and Q correlated Gt, Gc, Gr correlated (all increase with N) Example: objective function scores Contour plot - two variable parameters Local peaks have ~identical - reward/penalty - capacity But workload ratio (Gt/Gc) - varies by factor of two Regression Score Contour Map (Recurring Service Time tr = 9 seconds) 5,84 5,921 5,926 Gt/Gc =1.53 5,84 5,84 6,2 5,84 5,84 Gt/Gc =2.35 Gt/Gc =1.85 5,93 5,933 Gt/Gc =3.8 Gt/Gc =2.23 5,93 5,84 5, Transit Service Time tt (seconds) Ambiguity does not affect capacity estimate Ambiguity causes uncertainty in relative workload magnitudes Sector Workload Model 18

19 Outline Current FAA Capacity Model Full Capacity Model Benefits Analysis Hazardous Weather ZDC Sector Workload Model 19

20 Weather Blockage Modification to Model F w = fraction of sector airspace volume blocked by weather (derived from CIWS WAF*) G G b t r P No Weather N t T N t cbn Q 2 G G b ( t r t wrf P added recurring time w ) N Weather ( t t wtf T added hand-off time w ) N t cbn Q(1 F 2 ) w reduced volume Fit observed sector counts during weather to obtain t wt and t wr Sector Workload Model 2 * Corridor Integrated Weather System Weather Avoidance Field

21 Model Validation ZDC32 ZDC32 Fair weather capacity Weather-impacted capacity Actual traffic count Difficult to validate because truth is not available Sector Workload Model 21

22 Overall Validation 28 days 8 ARTCCs Most frequently blocked ZOB ZDC Typical blockage << 5% Model closely bounds observed counts Sector Workload Model 22

23 Normalized Capacity vs. Blockage Fraction Normalized capacity (relative to fair weather capacity) Normalized capacity is insensitive to sector transit time T Simplifies its operational use Sector Workload Model 23

24 Example: Two-Threshold Capacity Alert Model Capacity Procedure Simulation Add Weather alerts to existing Demand-based alerts Forecast capacity reduction 2-hour CIWS horizon 15-min peak aircraft count relative to fair weather capacity 2-hr Forecast % capacity loss ---- >5% capacity loss 1-hr Forecast Real Time Sector Workload Model 24

25 Conclusions Current MAP model is subset of full workload model Provides good fair-weather capacity estimates for many sectors Full model with improved vertical rate mechanism Increases capacity accuracy for all sectors Allows automatic capacity estimation Workload type ambiguities limit use in benefits analyses May require controller workload observations Normalized hazardous weather model Can provide simple threshold alerts Sector Workload Model 25