Airport resilience

One corner of the performance diamond

Dr Michael Fairbanks10 November 2011

Before we start, some preliminary definitions

1

• Resilience• …the abilities to anticipate, withstand and recover quickly from

difficult conditions (derived from: Oxford English Dictionary)• best balance of buffer (spare capacity) & utilisation

• Punctuality• the difference between the actual time & the scheduled time for a

flight• early is as bad as late (at least for some stakeholders)

• Delay (the other side of the capacity coin)• wasted time associated with queuing• many components (ATFM, airborne holding, taxi, start-up…)• different components can be traded-off

• Cancellations

Statistics gives us a convenient way of analysing the situation

2

• System is not deterministic –suffers from random fluctuations

• Performance indicators are in effect probability density functions

• Describe performance in terms of the statistical parameters defining the distributions• mean• standard deviation• skewness/kurtosis• mode

• Often have to apply first principles, generalised analysis because distributions are non-normal

Idealised punctuality distribution

Real punctuality distribution

REAL EXAMPLE

REAL EXAMPLE

Different performance indicators have very different distributions

3

Airborne holding distribution Ground holding distribution

• “Simple” statistical definitions – e.g. mean average – might not be meaningful

• Understanding of both operations and statistics is essential

REAL EXAMPLE

REAL EXAMPLE

REAL EXAMPLE

REAL EXAMPLE

Using cancellations as a KPI, we can categorise resilience into three main regimes

4

Number of days

Can

cela

tions

Green days(normal operations ~300 day)

Amber days(moderate disruption ~ 50 days

Red days(severe disruption ~ 15 days)

Characterisation of resilience regimes for a busy airport

Focus on the severe days is anticipation and recovery

5

• Anticipate the event and prepare

• Graceful and equitable degradation of service to minimise disruption

• Focus on recovering as quickly as possible

• Passenger welfare is paramount

€€3.5 billion in

3.5 billion in

the first week

the first week

Source: Flightpath 2050, Europe’s vision for aviation

Numerous hotspots cause delay and uncertainty in the flight even under normal conditions

6

Apron and Stands

Destination(s)

SIDs

ATFM at origin(s)

Airborne holding(stacks, trombones,

vectoring)

Taxi-out

Other airports’SIDs

Constraining airspace blocks

Schematic of hotspots in aircraft flows

Taxi-in

• Outstation performance• Airspace/ATC• Arrival runway capacity

• ATFM• airborne holding

• Stands

• Outstation performance• Airspace/ATC• Arrival runway capacity

• ATFM• airborne holding

• Stands

Arrivals

• Turnaround• Runway capacity• Taxiway capacity/congestion• SIDs• En route airspace/ATC• Destination airports

• Turnaround• Runway capacity• Taxiway capacity/congestion• SIDs• En route airspace/ATC• Destination airports

DeparturesLandside

•Passenger flow•Baggage flow

Constraining airspace blocks

Analysis confirms the queuing theory relationship between delays & the demand/capacity ratio

7

Delay distribution for airborne holding Delay curve for airborne holding

Applies to ATFM, airborne & ground holding, start-up & taxi delays(Also applies to other queues: pax security screening, control posts, etc)

The hotspots are therefore a set of queues

8

Contribution of different components into the overall flight time

AverageStandard deviation

ATFMATFM

3 minutes±3 minutes

Taxi-outTaxi-out

15 minutes±4 minutes

Airborneholding

Airborneholding

5 minutes±7 minutes

Taxi-inTaxi-in

8 minutes±2 minutes

FlyingFlying

48 minutes±5 minutes

TurnaroundTurnaround

40 minutes±14 minutes

Illustrative probability density

functions

Combined

μ =79 minutes

σ =11 minutes

9

Probability of success is increased by including buffers in block times

Relationship between undelayed, average and planned gate-to-gate timesfor flights arriving at a busy airport at capacity

Buffering against delays becomes self-defeating though

10

Off blocks

On blocks

Tim

e

Flight stage

TaxiTaxi FlyFly HoldHold TaxiTaxi

Impact of buffering by flight stageon block time

Pla

nned

blo

ck-ti

me

with

buf

fers

Del

ta

Vicious circle of increasingblock times

Mos

t lik

ely

bock

-tim

e

Most likely time

Time with buffer

Taxi time

Flying time

Holding tim

e

Taxi time

As demand/capacity increases, unpredictability increases faster than average delay

11

Delay curves for average delay per flight and standard deviation of delay per flight

Derived by application of queuing theoryApplies to ATFM, airborne & ground holding, start-up & taxi delays(Also applies to other queues: pax security screening, control posts, etc)

Demand/capacity ratio

Del

ay (m

inut

es p

er fl

ight

)

DERIVED FROM

REAL DATA

DERIVED FROM

DERIVED FROM

REAL DATA

REAL DATA

12

Increasing capacity at constant demand realises benefits in both average delay and buffer

Impact of lowering the demand/capacity ratio

The cost of failure savings achieved by increasing capacity might be expected to be significant

13

Based on the approach defined in the UK CAA’s 2008 runway resilience report assuming average aircrafttype is typically B737

There are also benefits of de-risking the last rotations of the day

14

Curfew

Outbound ReturnTurn Buffer

• Illustrative example• based on above block times• assumes 15 minute buffer in schedule between last arrival and

curfew• At 100% demand/capacity there is a 5% chance of the return

arriving after the start of the curfew• At 85% demand/capacity there is a 0.2% chance of the return

arriving after the start of the curfew

We need, however, to consider a three-way balance: increased demand, cost of prevention & costs of failure

15

Benefits from increased demand

Cost of prevention

Cost of failure

• Revenue• aeronautical• non-aeronautical

• Consumer surplus• Slots• APD• Connectivity

• Compensation• Poor use of resource

• Aircraft & crew• Handling• Airport resources

• Pax value of time• Emissions• Competitive

disadvantage

• Schedule buffers• block times• MCT• Pax

• System solutionse.g. CDM

• Process improvement• strategic• tactical

Illustration of the balance of the benefits of additional capacityagainst the associated costs

(assuming no additional infrastructure)

Resilience, therefore, is a key component of the tensioned framework of competing objectives

16

Some of the tensions between an airport’s objectives

Efficiency

Ideally achieve optimum balance And..

deliver high quality, cost effective, passenger experience

Environmental impact

ResiliencePerformance

Capacity utilisation

e.g. Completed departures vs night jet movements

e.g. Schedule buffers

e.g. Spare capacity vsaverage throughput

e.g. Departure punctualityvs taxi time

Environmental impact• Noise• Night jet movements• Track adherence• Emissions (ground & air)

Environmental impact• Noise• Night jet movements• Track adherence• Emissions (ground & air)

Performance• Punctuality• Service delivery (queues &

baggage)• Connectivity• Infrastructure condition

Performance• Punctuality• Service delivery (queues &

baggage)• Connectivity• Infrastructure condition

Resilience• Programme completion• Recovery

Resilience• Programme completion• Recovery

Capacity utilisation• Runway• Terminal utilisation• Aircraft utilisation• Airspace utilisation

Capacity utilisation• Runway• Terminal utilisation• Aircraft utilisation• Airspace utilisation

Concept courtesy of XPX Consulting

Improved performance can only be sustained by a wide range of coordinated actions

17

SchedulingScheduling

• Optimise the schedule considering:• all delays• resilience• design/masterplanning• environment• commercial implications• slot value• consumer surplus

• In addition to on/off-blocks, schedule to other milestones in the flight over which the airline has more control, e.g. stack fix

• Optimise the schedule considering:• all delays• resilience• design/masterplanning• environment• commercial implications• slot value• consumer surplus

• In addition to on/off-blocks, schedule to other milestones in the flight over which the airline has more control, e.g. stack fix

Performance managementPerformance management

• All stakeholders must be aligned

• Graceful and planned degradation in the case of disruption

• Ongoing measurement

• Identification of offenders

• Establishment of root causes• directly from data, e.g.

delay codes• in dialogue with airlines

• Incentives & penalties• no perverse incentives• contractual• regulatory

• All stakeholders must be aligned

• Graceful and planned degradation in the case of disruption

• Ongoing measurement

• Identification of offenders

• Establishment of root causes• directly from data, e.g.

delay codes• in dialogue with airlines

• Incentives & penalties• no perverse incentives• contractual• regulatory

ProcessesProcesses

• Command & control

• Better balance of ATFM, local & tactical measures

• Improved predictability of flows at critical points• approach fix (arrival

management)• start-up• line-up (pre-departure

management)

• CDM

• Command & control

• Better balance of ATFM, local & tactical measures

• Improved predictability of flows at critical points• approach fix (arrival

management)• start-up• line-up (pre-departure

management)

• CDM

Potential actions for improving punctuality, reducing delays & adding resilience