Cross-sectional time-series analysis of airspace capacity in Europe

Transcription

1 Cross-sectional time-series analysis of airspace capacity in Europe Dr. A. Majumdar Dr. W.Y. Ochieng Gerard McAuley (EUROCONTROL) Jean Michel Lenzi (EUROCONTROL) Catalin Lepadatu (EUROCONTROL) 1

2 Introduction Content The en-route airspace capacity problem: workload Simulation modelling: RAMS Cross-sectional time-series analysis of controller workload Framework Controller interviews Two studies Research update 2

3 Introduction Rapid growth in air traffic in Europe & USA: Consequences, e.g.us$ 5bn. predicted traffic growth; Airspace capacity needs to be increased: Runway capacity limitations en-route controller workload New CNS/ATM concepts - new technologies and procedures, e.g. direct routes There is a need to: Understand the drivers of airspace capacity; Develop a consistent method to estimate airspace capacity. 3

4 What is en-route capacity? ATC sector capacity is defined as: The maximum number of aircraft that are controlled in a particular ATC sector in a specified period, while still permitting an acceptable level of controller workload. 4

5 Three questions on controller workload What is controller workload? Confusing term. How is it measured? Many methods. What is an acceptable level? Lyons and Shorthose (1993) : shortcomings of capacity measures 5

6 What affects controller workload? SOURCE FACTORS MEDIATING FACTORS RESULT QUALITY OF EQUIPMENT ATC COMPLEXITY: AIR TRAFFIC PATTERN AND SECTOR CHARACTERISTICS INDIVIDUAL DIFFERENCE S CONTROLLER WORKLOAD CONTROLLER COGNITIVE STRATEGIES FACTORS AFFECTING CONTROLLER WORKLOAD Source : Mogford et al. (1995), page 5 6

7 ATC complexity variables Previous research indicates: Air Traffic Factors Total number of aircraft Peak hourly count Traffic mix Climbing/ descending aircraft Aircraft speeds Horizontal separation standards Vertical separation standards Average flight duration in sector Total flight time in sector Average flight direction Sector Factors Sector size Sector shape Boundary location Number of intersection points Number of flight levels Number of facilities Number of entry and exit points Airway configuration Proportion of unidirectional routes Number of surrounding sectors See: Hilburn (2004); DD (Kopedekar et al.). Q. What are the affect of variables on workload in sectors throughout the day? Use RAMS simulation based methodology 7

8 Why RAMS? RAMS not overtly cognitive, but : - captures observable tasks - also mental tasks e.g. resolution - workload thresholds - controller based RAMS: - > 25 years use in European airspace planning Controller: - task input - realistic conflict detection and resolution - simulation & output verification 8

9 En-Route Capacity Estimation I INPUTS SECTOR Sector corner points Sector boundaries Number of flight levels Number of navigation aids Number of airports AIR TRAFFIC Aircraft type Aircraft performance Flight plan of aircraft Rules for "cloning" aircraft CONTROLLER TASKS Controller tasks Task categories Task timings Conflict resolution strategies RAMS Simulation model OUTPUTS FLIGHT HISTORY Actual flight profiles flown ATC interventions to flights WORKLOAD W orkload recorded for controlling each flight, per controller W orkload discriminated by category CONFLICT HISTORY Aircraft involved in conflict Type of conflict Resolution applied 9

10 Panel data methodology 10

11 Methodology - II Fixed Effects Time-Series Cross-Sectional Model (sector level) w it = α i + x' it β + ε it w it = workload in sector i at time t α i = effects of var. peculiar sector i, constant over time X' it = variables in sector i at time t β = coefficients ε it assumed: i.i.d. over individuals i (the sectors) and time; mean zero and variance σ 2 ε Estimators from T-S C-S model are more accurate: Greater efficiency cf. c-s or t-s; Note estimated α i Model specification - test residuals: Temporal correlation; 11

12 CEATS Airspace Region 12

13 From RAMS Output: 5400 flights; CEATS taskbase; 46 Sectors; 20 Hours. Data Q. What factors affect controller workload during the day? Test variables against workload: Aircraft and airspace geometry. Total Workload = (Planning+ Tactical)/ hour. All 20 hours/ sector not just peak. Relationships to define subset of variables for analysis. 13

14 CEATS Major Findings Flight profiles significant: Cruise-descend => +37 secs Cruise-climb => secs Climb-climb => +49 secs 1 sec of total flight time => secs workload; Increase of 1 FL => -1 second workload; 1 nm/h speed diff => secs workload; Neighbouring sectors entry: Entry may be significant; Exit NOT significant. Entry attitudes significant BUT not exit attitudes: Similar values to entry attitudes. Temporal correlation statistics: Includes temporal autocorrelation. N.B. Results only valid for CEATS tasks, traffic and sector patterns 14

15 Predictions How good are model predictions? Actual vs. predicted workload for all sectors through day: 45 deg line Actual vs. Predicted workload for 46 sectors in CEATS region using panel data model Actual workload, seconds Predicted workload, seconds Investigate major differences N.B. Internal check 15

16 Two stage process CSTS Analysis: Refine factors before analysis. Two - stages Controller interviews Controller workload factors CSTS Analysis 16

17 Summary Factor Effect 1 Effect 2 Aircraft speeds Entry and exit points Number of surrounding sectors Speed difference between the fastest and slowest aircraft over the entry points AND at the same Flight Level The combined number of entry and exit points Number weighted by flow Number of routes The actual number of bidirectional/ unidirectional routes Intersection points Number of intersection The ratio of entry to exit points Parallel distance of route to the sector boundary Flight Levels Number of Flight Levels (FLs) Sector Geometry Transit times of 5-20 minutes 17

18 MFF Airspace Regions 18

19 CEATS vs. MFF Feature CEATS MFF Traffic Small-Med Many Transit times Short (5mins) Long (15 mins) Sector Size Small Very Large Neighbours Many Few 19

20 RAMS Output New Independent Variables New independent variables for analysis: Speed differential at entry points AND at same FL Surrounding sectors weighted by flow Number of exit points per FL weighted by flow 20

21 RAMS Output New Independent Variables I Speed differential at entry points AND at same FL Macedonia sector Hour 4 number of aircraft and max speed difference per FL No. and nm/hr Aircraft Speed Diff FL 285 FL 290 FL 300 FL 310 FL 320 FL 330 FL 340 FL 350 FL 360 FL 370 FL 380 FL 390 FL 400 FL

22 RAMS Output New Independent Variables II New variable for complexity of speeds at different FLs each hour: n i N i S i N = no. of ac at FL i S = Max speed difference at FL i 22

23 RAMS Output New Independent Variables III Variable reflects complexity of control for speeds Speed Differential Quotient for Macedonia Sector Hour No. of ac x NM/h FL 285 FL 290 FL 300 FL 310 FL 320 FL 330 FL 340 FL 350 FL 360 FL 370 FL 380 FL 390 FL 400 FL

24 MFF Results Model indicates: Flight profiles significant: Cruise-Cruise => secs Cruise-climb => secs Cruise-descend => secs Climb-climb => secs Speed difference quotient => secs workload No FL effects; No exit point effects No Neighbouring sectors entry/ exit effect: Could be MFF region effect Not many neighbours No Temporal Effect? 24

25 Good fit except at high workloads? Model Fit "Actual" vs Calculated Workload Actual workload, secs Calculated workload, secs 25

26 Panel data issues Example MFF variables => not same as CEATS MFF analysis indicates: Flights profiles significant speed differential quotient significant Sectors neighbouring not significant Sectors - FL differences not significant MFF airspace area characteristics? Flights profiles significant BUT not sector 26

27 Incorporating variables: Intersection points; Route lengths; Sector geometry. Further controller interviews: +4 ACCs in Europe 4 FIRs in India 2 FIRs in South Africa Current status Panel data analysis: Non-linearities Level of detail trade-off 27

28 Conclusions RAMS Simulation methodology: CEATS and MFF Region; Variable outputs; Better geographical output. Hour-by-hour analysis more complicated than peak hour: Greater dispersion in data. Panel data analysis: More variables than for peak hour; Aircraft and sector variables; New variables. Panel Data Methodology valid Variables differ between CEATS and MFF Regions External validity 28

29 Websites Dr Arnab Majumdar Dr Washington Y. Ochieng Centre for Transportation Studies web-sites: