FAA Future Operational Concept Analysis Limited Dynamic Re-Routing (LDRR) software isa

Future Operational Concepts Analysis Background/Observation The ATM system can result in (en-route) penalties to traffic –AOC/Pilot files flight plan according to their own business objective based on predicted weather and current knowledge of prohibited area activity (e.g. Active MUAs) –Pilots and ATC try to respect the operator objectives while maintaining system safety respecting sector/controller load constraints and applying pre-defined operational procedures –Subsequent encounters with additional no-fly zones induce delays unanticipated SUA closure unexpected weather movement turbulence… –ATC Interventions to assure safety etc can result in additional delay level changes vector maneuvers…

Future Operational Concepts Analysis Premise Increased collaboration in the ATM system will help satisfy user objectives more easily in the future –NAS Users dynamically monitor the situation –Users propose/request adaptation in accordance to their business needs –Service provider assures system safety and efficiency –Ground/Air based Collaborative Decision Making (CDM) support tools will further support the ATM process However such systems will be introduced in a evolutionary basis

Future Operational Concepts Analysis Limited Dynamic Re-Routing A client-oriented proposition to reduce penalties to traffic: –Flight experiences en-route delay –AOC reacts to on-going penalties by calculating alterative flight plan to better suit their business objective –Pilot requests revised flight path from ATC –ATC accepts/rejects revised flight path according to safety/capacity constraint

Future Operational Concepts Analysis LDRR - Rules of Application Flight can be optimized if en-route delay > 5 minutes …and not within 200NM of destination/origin …and in the upper (en-route) airspace (> FL240) Assume AOC has limited information –Good knowledge of predicted bad weather areas –Limited knowledge of possible MUA activity –Limited knowledge of turbulence problems –No knowledge of local traffic situation –No knowledge of possible flow/capacity constraints

Future Operational Concepts Analysis Study Objectives Evaluate the benefit and impact of introducing the LDRR concept on the NAS –Overall benefit to traffic (reduction of penalties) –Impact on the ATC Service Provider (safety, loading …) –Direct benefit to sub-set of traffic that was allowed to be optimized –Impact of optimization on other (non-optimized) traffic –Effectiveness of dynamic (in-flight) optimization strategy –Evaluate information needed to support optimization process

Future Operational Concepts Analysis Modeling Approach Problem: Validation of future operational concepts –Existing fast-time models cannot represent new operational concepts without costly modification –Distributed modeling extends existing tools to support new behaviors / additional agents Technical Objective: Case study of distributed modeling of LDRR future concept in a large scale, high fidelity study

Future Operational Concepts Analysis Baseline Scenario ( RAMS Plus ) 4 ARTCCs (Denver, Kansas City, Minneapolis, Cleveland) flights –ETMS first active flight plans for 12/12/2000 –Filed flight plans account for some, not all active SUAs/weather/no fly areas –All flights passing through any one of the selected ARTCCs –Flight profiles between departure and arrival points Moving weather fronts Typical wind patterns ATC diverts traffic according to airspace closures and conflict

Future Operational Concepts Analysis Limited Dynamic Re-Routing Scenario Baseline scenario: ATC model (RAMS Plus) LDRR scenario: ATC model plus additional models –AOC Decision Making Role (Decision Tool) flight delayed more than an additional 5 minutes not permitted within 200NM of origin/destination airport not permitted in lower airspace (below FL240) –AOC CDM tool [business model] (FAA OPGEN) Business strategy modeled is to reduce fuel use first, then reduce delay Optimization uses common wind information Limited knowledge of active SUA and weather movements No knowledge of traffic situation –ATC Model decides on system safety (RAMS Plus) Request refused if immediate conflict results

LDRR Model Logic Flow Delay trigger, sector crossing Optimise trajectory? AOC Model: Decision Tool Models flights, controller teams; performs conflict & SUA avoidance, … ATC Model: RAMS Plus No Optimises trajectory, including known SUAs AOC CDM Tool: OPGEN New trajectory Accepted only if no local conflicts, similar to trial planning Yes Future Operational Concepts Analysis ATMOS Wind Model

Future Operational Concepts Analysis Results: Comparison of Traffic Penalties Scenario%Fuelburn Penalty/flight %Delay Penalty/flight %Distance Penalty/flight Average Delay/flight BASELINE2.00%1.71%1.88%1.85 min Impact+15.0%+8.2%-2.1%+0.15 min LDRR2.30%1.85%1.84%2.00 min …LDRR results in an overall increase in penalties to traffic (38.5 hours additional delay - all flights combined) Figure: Flight Penalties – all simulated flights

Future Operational Concepts Analysis Results: Impact on Service Provider #1 ScenarioZDV Average nFlights/15-min ZKC Average nFlights/15-min ZMP Average nFlights/15-min ZOB Average nFlights/15-min BASELINE Difference-4.7%-4.6%-3.5%-1.4% LDRR Figure: Average Flights in Centers – all simulated flights

Future Operational Concepts Analysis Results: Impact on Service Provider #2 ScenarioNo of conflicts (all recorded conflicts) No of conflicts (core area only) No of conflicts (outside core area) ATC Tasks (core area only) BASELINE Difference-0.6%-1.9%+0.4%+1.3% LDRR Figure: Conflicts / ATC Tasks – all simulated flights

ISA Software “ Allowing flights to optimize had no significant impact on the ATC Service Provider in the study region” Note 1 –Reduction (-4.5%) and better balanced traffic loads in the region –Lower number of conflicts in the region (-1.9%) –Reduced number of “Point-Outs” (-0.24%) –Higher no (+1.3%) of Controller tasks in the region (new tasks dealing with LDRR request replacing some traditional tasks) Statement: Note 1 : Impact on Service Provider in neighboring regions could not be assessed in this study

Question: ISA Software Consider possible influences: Rules to qualify for optimization –Are they too stringent? –Were optimization requests refused? Penalties to traffic that was not optimized –Did non-optimized traffic receive unreasonable impacts? Advantages gained through optimization –Was the optimization process successful? –Do we need better information as a basis for our optimization? …to find out why the results are not as anticipated. Why the unexpected increase in penalties to traffic?

ISA Software Consequence of LDRR Rules of Application #1 Total% of all Flights No of baseline flights with > 5 min en-route delay % No of LDRR flights requesting 1+ optimization (% of flights delayed > 5 min) 1584 (92.6%) 10.3% No of (non-qualifying) flights not satisfying rules % No of flights with 1+ requests accepted (% flights that requested and had 1+ accept) 1448 (91.4%) 9.4% No of flights with all requests rejected (% flights with all requests rejected) 136 (8.6%) 0.9% Figure: Summary of flights qualifying for optimization

ISA Software Consequence of LDRR Rules of Application #2 Total%Rate Number of optimization requests considered by AOC model3700n/a Number of optimization requests accepted by ATC % Number of optimization requests rejected % No of rejections by ATC because of immediate conflict848.1% No of rejections for other reasons – AOC internal reason – ATC recommendation not to take given route – … % Figure: Summary of optimization request acceptance

ISA Software “ The rules to qualify for optimization and acceptance of optimization requests did not appear to have an adverse effect on the potential benefits to traffic ” –Majority of flights that were delayed > 5 min qualified for optimization (92.6%) –Few flights had all requests for optimization refused (8.6%) –High proportion of all optimization requests were accepted (72%) Statement:

Question: ISA Software Consider possible influences: Rules to qualify for optimization –Are they too stringent? –Were optimization requests refused? Penalties to traffic that was not optimized –Did non-optimized traffic receive unreasonable impacts? Advantages gained through optimization –Was the optimization process successful? –Do we need better information as a basis for our optimization? …to find out why the results are not as anticipated. Why the unexpected increase in penalties to traffic?

ISA Software Penalties to Non-Optimized Traffic #1 Figure: Performance Measures for Non-Optimized Traffic (n=13839) Scenario%Fuelburn Penalty/flight %Delay Penalty/flight %Distance Penalty/flight Average Delay/flight BASELINE1.17%0.96%1.05%.96 min Impact-0.02%-0.04%-0.19%-.04 min LDRR1.15%0.92%0.86%.92 min

ISA Software Penalties to Non-Optimized Traffic #2 Figure: Conflict Measures for Non-Optimized Traffic (n=13839) Scenario% Flights in Conflict No of Conflict Avoidance Maneuvers No of Restricted Airspace Avoidances BASELINE35.9% Difference-0.2%-88 (-2.17%) -60 (-1.01%) LDRR35.7%

ISA Software “ Optimization of a sub-set of traffic had no adverse effect on other traffic in the region. Other traffic showed a slight benefit as remaining (non-optimized) flights had less chance of encountering traffic related problems and therefore fewer avoidance maneuvers.” –Lower number of flights in conflict (-0.2%) –Reduced conflict avoidance maneuvers (-2.2%) –Slightly fewer encroachments of prohibited airspace (-1%) –Fuel, Delay and Distance benefits (between 0.02 & 0.2%) Statement:

Question: ISA Software Consider possible influences: Rules to qualify for optimization –Are they too stringent? –Were optimization requests refused? Penalties to traffic that was not optimized –Did non-optimized traffic receive unreasonable impacts? Advantages gained through optimization –Was the optimization process successful? –Do we need better information as a basis for our optimization? …to find out why the results are not as anticipated. Why the unexpected increase in penalties to traffic?

ISA Software Analysis of Optimized Traffic #1 Figure: Performance Measures for All Optimized Traffic (n=1448) Scenario%Fuelburn Penalty/flight %Delay Penalty/flight %Distance Penalty/flight Average Delay/flight BASELINE5.8%5.4%5.8%10.4 min Impact+1.5%+1.1%+0.7%+2.0 min LDRR7.3%6.5% 12.4 min

Analysis of Optimized Traffic #2 ISA Software Optimization was fruitful in some cases: 261 (18%) flights successfully reduced fuel use/delay But 774 (53%) flights got worse And 442 (29%) flights had no significant change

ISA Software Analysis of Optimized Traffic #3 Figure: Performance Measures for sub-group of Optimized Traffic with Improved Delay over Baseline (n=261) Scenario%Fuelburn Penalty/flight %Delay Penalty/flight %Distance Penalty/flight Average Delay/flight BASELINE10.1%8.8%9.1%16.3 min Impact-2.2%-2.1%-1.2%-4.0 min LDRR7.9%6.7%7.9%12.3 min

ISA Software Analysis of Optimized Traffic #4 Figure: Performance Measures for sub-group of Optimized Traffic with Deterioration in Delay over Baseline (n=774) Scenario%Fuelburn Penalty/flight %Delay Penalty/flight %Distance Penalty/flight Average Delay/flight BASELINE5.1%4.4%4.8%8.9 min Difference+2.9%+2.5%+1.7%+5.1 min LDRR8.0%6.9%6.5%14.0 min

ISA Software Analysis of Optimized Traffic #5 Figure: Performance Measures for sub-group of Optimized Traffic with No Significant Delay over Baseline (n=413) Scenario%Fuelburn Penalty/flight %Delay Penalty/flight %Distance Penalty/flight Average Delay/flight BASELINE5.1%5.4%5.3%9.6 min Difference+0.1%0.0% 0.0 min LDRR5.2%5.4%5.3%9.6 min

ISA Software “ Optimization had considerable positive benefits in a few cases (18%), but 53% of flights that requested and received re-optimized profiles ended up with higher delays (and fuel burn) than in the baseline case” –18% of optimized flights achieved good benefits (fuel use reduced by 2.2%) (delay reduced by 2.1%) –29% of optimized flights had no significant change (+/-0.25%) –53% of optimized flights had significant increase in penalty (fuel use increased by 2.9%) (delay increased by 2.5%) Statement:

Question: ISA Software Is there any explanation why Optimization results in a majority of the optimized traffic receiving increased penalties? Recalling that: The AOC Business model chosen Optimizes for Fuel then Delay –taking account of prevailing wind conditions –and can be requested for any flight in the upper (>FL240) airspace that is outside a 200NM radius of the departure or destination airport The Optimizer will avoid any known “no-fly areas” However The Optimizer has limited knowledge of the currently active no-fly areas And no knowledge of the local traffic situation

ISA Software An example from the model

ISA Software Summary “ As a first step we can already see that allowing users to react to the dynamic situation in the airspace system could provide them with substantial economical benefits with no major impact on the service provide or safety in the NAS” BUT: it is imperative to provide sufficient access to important NAS data to improve the chance of success.