Predictability in Air Traffic Management

Transcription

1 Predictability in Air Traffic Management Mark Hansen, Yi Liu, Lu Hao, Lei Kang, UC Berkeley Mike Ball, Dave Lovell, U MD Bo Zou, U IL Chicago Megan Ryerson, U Penn FAA NEXTOR Symposium 5/28/15 1

2 Outline Motivation Defining Predictability Measuring Predictability Valuing Predictability Achieving Predictability Conclusions 2 2

3 Motivation One of our most complex challenges today is meeting the expectations for all system users for their operational needs, increasing capacity, efficiency, and predictability, while enhancing safety, mitigating environmental impacts, and operating in a seamless global environment. FAA, Destination 2025 Improve flight predictability by reducing variances in flying time between core airports based on a 2012 baseline. FAA, Destination

4 Motivation 4 4

5 Motivation Survey of Airline ATC Coordinators on criteria for assessing GDP performance 1 (Not at all important) to 5 (Extremely important) 5 5

6 Outline Motivation Defining Predictability Measuring Predictability Valuing Predictability Achieving Predictability Conclusions 6 6

7 Predictability in ATM Ability to accurately predict ATM actions and operational outcomes Realized Block times Airborne times Effective flight time Defined at different time scales Strategic several months out, when schedule is set Tactical day of operation, when flight plan is created 7 7

8 Outline Motivation Defining Predictability Measuring Predictability Valuing Predictability Achieving Predictability Conclusions 8 8

9 Approaches to Measuring Predictability Flight time variability Behavioral response Cognitive 9 9

10 Flight Time Variability What is a flight? Airline, OD, Dep. Hr, AC Type Airline, OD, Flt. No Flight time components to consider? (Gate Delay), Taxi-out, Airborne, Taxi-in How to measure variability? 2 nd Moment-based (variance and std. dev) Percentile-based 10 10

11 Trends of Variance of 250 Flight Time Components Trends 2004 to Variance (min 2 ) FT Taxi-out Airborne Taxi-in 0 04_1 04_2 04_3 04_4 05_1 05_2 05_3 05_4 06_1 06_2 06_3 06_4 07_1 07_2 07_3 07_4 08_1 08_2 08_3 08_4 09_1 09_2 09_3 09_4 10_1 10_2 10_3 10_4 Quarter 11 11

12 Trends in 70 th -50 th Percentile ABT Based on 586 unique combinations of Airline,O,D,Eq,Hr At least 10 flights in each quarter Benchmark OEP Airports Difference between 70th and 50th Actual Block Time 70th Percentile Actual Block Time 50th Percentile Actual Block Time Q1,2012 Q2,2012 Q3,2012 Q4,2012 Q1,2013 Q2,2013 Q3,2013 Q4,2013 Q1,2014 Q2,2014 Q3,

13 Behavioral Metrics Reflect flight operator adaptations to (un)predictability Lagging Metrics Scheduled Block Time Turn Times Fuel Loading 13 13

14 Overall Average SBT Change for Domestic and Weekdays Flights Quarter 1 Quarter 2 Quarter 3 Quarter

15 Delta SBT change Top 15 Major Drivers ( ) DAL q1 q2 q3 q4 Dep Arr type hour Difference JFK SFO B Dep Arr type hour Difference DTW MSY A Dep Arr type hour Difference ROA ATL A Dep Arr type hour Difference PHL ATL B DTW PHX B ATL LAX B LGA MSY A JFK LAX B ATL SLC B HNL LAX B LAX BOS B PDX JFK B ATL FNT MD BNA LAX B JFK SFO B ATL HNL A JFK SLC B ATL MLB MD SFO JFK B JFK AUS B ATL ORD B ATL ORF MD JFK LAS B CRW ATL A SAN JFK B ATL MLB MD JFK LAS B DEN ATL MD LAS JFK B JFK SLC B LGA ATL MD ATL LAX B EWR SLC B ATL GSO MD JFK LAX B SEA JFK B BUF ATL MD ATL MCO B CRW ATL A JFK SAN B ATL MCI MD ATL STL B ATL ROC MD BDL ATL MD JFK LAS B ATL PHX B JFK SFO B DTW LAX B ATL SLC B ATL PHX B ATL MSP B BOS MSP B SFO JFK B MSN DTW MD MSY LAX A ATL PHL MD JFK SFO B MSP SFO B SJU ATL B CAK ATL MD

16 Cognitive Metrics What did they know and when did they know it Important to flight operators but difficult to track Possible metrics Wheels-off times TMI lead times and revisions 16 16

17 Wheels-Off Time Predictability Change from Use of Spot and Runway Departure Advisor (SARDA) 6 5 High Traffic, Advisory Scheduled pushback 6 5 High Traffic, Baseline s.d. (min) αα uuuu = Actual pushback Taxiway entry time (min) Queue entry Runway entry s.d. (min) αα uuuu = time (min) 17 17

18 Outline Motivation Defining Predictability Measuring Predictability Valuing Predictability Achieving Predictability Conclusions 18 18

19 Scheduled Block Time (SBT) Model Investigate relationship between realized block times and scheduled block times Combine study of airline SBT setting process with econometric analysis Hao and Hansen, Block time reliability and scheduled block time setting Transportation Research Part B: Methodological, Volume 69, November 2014, Pages

20 Percentile Model for SBT Setting Relate SBT to historical block time Predictability is depicted by segmenting the historical block time distribution Treat different segment of the distribution differently Allow for seeing the contribution of each segment 20 20

21 Percentile Phase-Specific Model Used percentiles by flight phase Included gate delay Coefficient TO nonto Gate Delay 0 p50 d56 d67 d78 d89 d Variable 21 21

22 Cost of Scheduled Block Time Statistical cost estimation: cost=g(output,factor prices, time variables,other) Airline quarterly data from Form 41, Time variables include Positive delay against schedule Schedule buffer B. Zou and M. Hansen, Impact of Operational Performance on Air Carrier Cost Structure: Evidence from US Airlines, Transportation Research Part E, Vol. 48, pp

23 Estimation Results 23 23

24 Predictability and Fuel Loading In the flight planning process, airline dispatchers load discretionary (i.e., nonmission fuel) to hedge against uncertainty Airport outages Weather events Possible re-routes What is the cost of carrying discretionary fuel? Hao, Lu, M. Hansen, M. Seelhorst, and M. Smirti, Impact of Operational Flight Predictability on Airline Fuel Cost, TRB Annual Meeting, January, 2014, paper _B

25 What is Additional Fuel, and What is the Cost to Carry this Additional Fuel? Two definitions of additional fuel Fuel on arrival definition: Total Fuel on Arrival with Tankering, Reserve, and 1 st Alternate Fuel Removed Contingency fuel definition: Additional Contingency Fuel (fuel above SCF 99) plus 2 nd Alternate Fuel 25 25

26 Cost to Carry Factors Convert additional fuel loaded into fuel burned Fuel burned per pound of fuel carried per mile using PIANO model Special recognition for: 26 26

27 Annual Cost to Carry Across our Study Airline for All Domestic Flights Fuel on Arrival Contingency Fuel Cost to Carry (lbs) Cost to $2/gallon ($) Cost to $3/gallon ($) Cost to $4/gallon ($) CO 2 (lbs) 1.86* * * * * * * * * *10 8 We aggregate the yearly cost to carry fuel across the entire domestic aviation system (assuming all other carriers behave like our study airline) The fuel on arrival benefit pool is 1.9 billion lbs of fuel (~$835 million) The contingency fuel benefit pool is 946 million lbs of fuel (~$424 million) 27 27

28 Flight Operator Survey Investigate flight operator preferences in traffic management initiative decision-making process 23 ATC coordinators from several airlines Stated preference questions 28 28

29 Stated Preference Analysis Airline ATC Coordinators asked to choose between a set of hypothetical GDPS Attributes of GDPs chosen to reveal utility functions Ordered probit model used for function estimation 29 29

30 Estimation Results Unpredictability premium is about 15% (.012/.078) Other predictability effects have expected signs but are insignificant Maximum delay does not matter 30 30

31 Outline Motivation Defining Predictability Measuring Predictability Valuing Predictability Achieving Predictability Conclusions 31 31

32 Achieving Predictability NEXTGEN Surface Traffic Management TMI Strategies GDPs instead of Ground Stops NAS Vision 15 Balance predictability and throughput objectives Sequencing policies that favor right tail flights 32 32

33 Outline Motivation Defining Predictability Measuring Predictability Valuing Predictability Achieving Predictability Conclusions 33 33

34 Conclusions Predictability is a thing It has both a strategic and a tactical aspect It is measurable It is monetizable It can be improved 34 34