Free-Flight & Air Traffic Control - ACCS

FreeFree--Flight & Air Traffic ControlFlight & Air Traffic ControlProf Peter LindsayBoeing Professor of Systems EngineeringDirector, ACCS

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Outline of talk

Some terminology & conceptsThe future of Air Traffic ControlATC as a complex system– Multi Agent System models– Boids & flocking behaviour

Evaluation of ATC design optionsThe ATC Workload project– Modelling operators

Summary & conclusions

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Current Air Traffic Control

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ATC trends & challenges

Changing nature of Air Traffic Control:– Australian system now entirely computer-based– Datalink will (partially) replace radio communications– ADSB + GPS will enable radar-like surveillance of whole

continentAutomated Dependent Surveillance - Broadcast

Massive savings possible if airlines can choose own trajectories (“free flight”)Free flight is a fundamental change to operational conceptHow do we ensure that safety is not compromised, & objectives are achieved?

Free Flight & Air Traffic Control

A simple ATC sector

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Terminology & concepts

En-route flight phase: > 200km from airportsDifferent separation standards apply:– Lateral: 5NM horizontal distance– Vertical: 1000’– Longitudinal (aka “in trail”): 30NM when on same path– Also “soft standards”: eg 10NM intervention standard

separation violation: the separation standard is not metFlight plan: 4D trajectory, including time at waypoints


ATC: The Australian contextAustralian ATM Strategic Plan released 2002– Plan through to 2017 & beyond

Stakeholders include:– AirServices Aust (ASA): ATM providers– CASA: regulators– Airlines – international, domestic, regional,…– Airports– International standards bodies: IATA, ICAO– Dept of Transport & Regional Services (DOTAR)– Lobby groups: Aviation Industry Forum (ASTRA),…


Key drivers for changeReduced infrastructure costs– eg use GPS & satellite comms instead of radar

Reduced flight times, fuel use, noise,…More flexible airline operations– eg negotiate slots on day

Desire for increased system predictability– eg more reliable arrival times;

know how much fuel to carryATM service market becoming global– increased flight ranges– sector charge differentials


Australian ATM Strategic Plan7 key strategies including– User Preferred Trajectories (UPTs)

User = airline– Flexible Use of Airspace (FUA)

civilian use of military airspacereservation system

– Conflict ManagementMove from distance & time separation standards to more flexible risk management approach

– Decision Information NetworkIncreased information sharing, more negotiation

15 year lead times:– 5 ConOps, 5 functional architecture, 5 test & prove


User Preferred Trajectories (UPTs)Goal is to optimise for flight distance, time, fuel usage, weather,…Applies to en-route control rather than airport vicinity– Mainly for international & long domestic (E/W) flights

Plan is for staged introduction– Flex tracks: to take advantage of jet streams

(seasonal)– Full freedom (eg great circle routes) will require total

rethink of separation paradigmNote findings of Tasman simulator trial

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Outline of talk





ATC as a complex systemA non-linear system– Changes at one level can have unanticipated effects

at other levels– Eg conflict alert– Propagation of delays– Effect of weather


Future National Air-Space (NAS) model

Flight parameters

Flight trajectories

Regional traffic patterns

NAS-wide traffic patterns

Flight Deck

AOC

ATC

Traffic Management

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Tackling the free flight problemTackling the free flight problem

ATC

noise

Flight plans


Tackling the free flight problem (2)Approach to Free Flight ATM:– Agents: aircraft(/airlines), weather, airport controllers,

traffic controllers, flow controllers…– Behaviour: flight plans, procedures,…– Connections: proximity, communications,…– Emergent properties: safety, congestion, throughput,…

System safety “managed” primarily through off-line negotiation of flight plans (+ design of procedures etc)– Optimise system “robustness” in pre-flight planning


Tackling the free flight problem (2)Provide tools for situation awareness & decision support – eg visualise downstream effect of changes

Understand how local decisions cascade to global consequences


Flocks of aircraft?

One way to reduce controller workload would be to have aircraft fly in a cluster– “Moving sectors”– Aircraft do their own separation assurance within the cluster– Possible application of flocking behaviour?

Flocking behaviour follows from 3 simple rules– See Craig Reynolds on Boids

www.red3d.com/cwr/boids

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3 simple steering behaviours

Separation: steer to avoid crowding local flockmates

Alignment: steer towards the average heading of local flockmates

Cohesion: steer to move toward the average position of local flockmates

From Reynolds


Predator & school of fish

Can add a 4th rule: steer away from predator

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Outline of talk





Evaluation of ATC design optionsNeed a way of evaluating & predicting risk associated with new operational conceptsHuman operator will play a role in ATC for a long time to come– Role of ground-based controller will change from

active control to passive monitoring– But what exactly should the role be?

How to divide responsibility between ground & air?

– What tools will they need?


How to evaluate HCI design choices?Challenge with existing systems is to evaluate safety of different Human Computer Interaction design choices: eg– Which software tools to make available & when?

What settings?– What procedures & protocols to use?– How to train operators?

Note: existing Human Reliability Assessment techniques are inadequate for this purpose– Mostly designed for well-designed sequences of

essentially independent activities– ATC task is highly interleaved, concurrent &

memory-based


ATC software tools include…

Short route probe: indicates predicted position of aircraft at selected time intervals into the futureBearing & range lines: shows aircraft's distance & bearing from selected points– can be another aircraft

Estimated time of passing: shows time & point where aircraft will come closestConflict alert warning: indicates that separation violation will occur if aircraft maintain current speed & bearing…


Overview of SafeHCI approach

Our approach to comparing HCI design choices: understand & model operator’s cognitive processes as stochastic processes– hypothesize what factors affect likelihood and

duration of activities within the task– conduct experiments to calibrate the models

hypothesize effects of design interventions on individual activitiespredict system-level effects of the design interventions


ATC-operator cognitive processes


“Operator Choice Model”


Calculating system risk

Design individual experiments to calibrate the different parts of the model as functions of the environment & history (= memory)– probability of transitions – duration of transitions

Have developed tool that will calculate overall likelihood of user-specified indicators for user-supplied scenarios– e.g. whether a separation violation occurs

Use to estimate system-level effect of proposed design interventions– vary task & predict/estimate effect on individual transitions– calculate effect on indicators


Modelling the conflict detection taskA conflict is a pair of aircraft that will come within 5NM while “at same Flight Level” unless controller intervenesWhat follows is a “proof of concept” study still underwayObjective is to develop a model that emulates operator performance & effect of different tools

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The different tools

The 4 operational concepts being modelled:– Baseline: unaided– Conflict alert: automated tool that indicates conflict

when 50NM apart– DOMS Predictor tool: user-invoked tool that

calculates Distance Of Minimum Separation (DOMS)– Both tools in use


Operators as stochastic processes

Model probability & duration of transitions, & probability of outcomes– Use experiments to develop formulae for the above– Main factors: DOMS, angle,

time to min separation (ttms)Eg Timing for the baseline model (in minutes):

scan attendi

classifyi

0.25

1 if IC0 if NC

0.1

0.25


Postulated effect of tools on timingTiming for the Conflict Alert case:

scan attendi

classifyi

0.25

1 if IC0 if NC

0.1

0.1 if alerting0.25 otherwise

Timing for the DP tool case:scan attendi

classifyi0.5


Postulated effect of ttms on accuracyttms = time to minimum separation– Roughly corresponds to urgency

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0.2

0.4

0.6

0.8

1

ttms:2, 5, 10

DOMS=5DOMS=0 DOMS=10


Predicted effect on operator performance

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0.2

0.4

0.6

0.8

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0 2 4 6 7 8 9

baseline conflict_alert doms both

DOMS

model

Likelihood that operator will classify the pair as being in conflict

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Outline of talk


Evaluation of ATC design optionsQuick introduction to the ATC Workload project– Modelling operators



The ATC Workload projectCollaboration with UQ’s Key Centre for Human Factors & Applied Cognitive Psychology & Airservices AustraliaAim: To develop a model that can:– Measure the flow of traffic through an air sector– Predict the level of workload that an average

controller will experienceThe challenge: model the effect of controller interventions on traffic– Also, controllers adapt their behaviour to moderate

future workload


High fidelity simulations


Video-cued recall

Medium workload – about to decrease


Video-cued recall (2)

Medium workload – about to increase


Agent-based modellingWe are developing stochastic agent-based models of the full task


Agent-based modelling (2)Conflict detection in 3D


Agent-based modelling (3)Conflict resolution


ATC simulator architectureATCSimulator- updates the position of aircraft over time according to their specified flight plans

Aircraft characteristics

Flight plans

Prerecordedaircraft

positions

ATC Engine

Aircraft Agents

Controller Agents

Workload and Traffic Flow metrics Visualisations

Track data and amended flight plans

Pre-recorded Controller

outputs

Sector, route and Waypoint database

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Air Traffic Control is undergoing fundamental changes– Free flight has the potential for substantial savings &

efficiency gains, provided it can be made safe– The technology is available, but a lot of research is

still needed before a workable operational concept can be implemented

Nature can give us inspirationsModelling & simulation has advantages over than experimentationBut how can we be sure our models are valid?


Summary & conclusions (2)

ARC Centre for Complex Systems– Theme: computation in and by networks

Simple agent behaviour+ connection topology = complex system behaviour (emergent properties)Emergent properties in this case are safety, efficiency, orderliness, predictabilty,…

– Methods & tools for understanding, managing & controlling complex systems

Evaluation of system design optionsLater: decision support systems

– Application of complex systems science to ATCeg flocking behaviour


AcknowledgementsAir Traffic Control program:– ATC Workload project is a collaboration between ACCS, UQ’s

Key Centre for Human Factors & Applied Cognitive Psychology & Airservices Australia (Andrew Neal, Project Leader)

– Simon Connelly & Junhua Wang– stochastic modelling tool– Scott Boland – traffic replay tool– Penny Sanderson & Martijn Mooij – hi-fi simulator experiments– Katie Duzcmal & Peter Robinson – agent model in Prolog– Colin Ramsay – trajectory modelling– Tim Rudge – 3D visualiser– Jacki Wicks & Rachel Chitoni – calibration of conflict detection

models– Ariel Liebman – risk-based conflict management

Plus many more

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Free-Flight & Air Traffic Control - ACCS