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Presentation at GAD2011, Barcelona, November 2011 Presenter: Mike Fairbanks of Helios [email protected] _______________________________________________________________________ Follow Helios via Linkedin, www.twitter.com/askhelios and www.facebook.com/askhelios
Citation preview
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