The National Airspace System as a Cyber-Physical SystemHamsa Balakrishnan

Massachusetts Institute of Technology

Joint work with: Diana Michalek, Harshad Khadilkar, Hanbong Lee,

Ioannis Simaiakis and Varun Ramanujam

NSF−UC Berkeley−MIT ActionWebs Summer Meeting

July 23, 2010

Research objectives

Increase efficiency of operations Multi-objective control techniques for balancing tradeoffs Decrease fuel burn and environmental impact Increase operational robustness in the presence of

weather More flexible and dynamic trajectories More decentralized decision-making

The use of onboard sensing in ActionWebs, combined with the development of hybrid systems models of aircraft trajectories can help us achieve these objectives

Motivation

In 2007, domestic air traffic delays [JEC, US Senate] Had a $41 billion impact on the US economy Cost airlines an additional $1.6 billion in fuel costs Consumed an additional 740 million gallons of jet fuel Released an additional 7.1 million tons of CO2 into the atmosphere

In 2007, aircraft in the U.S. spent over 63 million minutes taxiing in to their gates, and over 150 million minutes taxiing out for departure [FAA]

Taxiing aircraft burn fuel, and contribute to surface emissions of CO2, hydrocarbons, NOx, SOx and particulate matter

An estimated 6 million tons of CO2, 45,000 tons of CO, 8,000 tons of NOx and 4,000 tons of hydrocarbons are emitted annually by aircraft taxiing out

In Europe, aircraft are estimated to spend 10-30% of their time taxiing

A short/medium range A320 expends as much as 5-10% of its fuel on the ground [Airbus]

Boston Logan airport, 1830hrs, 01/06/2010

Efficient and equitable arrival/departure runway scheduling

Given a set of flights with estimated arrival times at the airport, the aircraft need to be sequenced into the landing (takeoff) order, and the landing (takeoff) times need to be determined Minimum separation between operations for wake avoidance

(wt. class dependent) (Safety)

Currently FCFS; resequencing could increase throughput (Efficiency)

Separation H L/B757 S

H 96 157 196

L/757 60 69 131

S 60 69 82

Trailing a/c

Lea

din

g a

/c

Separation in secondsH SH S196 s 60 s 196 s

452 s

S H60 s 96 sS 60 s H216 s

*[Dear 1976, Psaraftis 1980, Garcia 1990, Volckers 1990, Neuman and Erzberger 1991, Venkatakrishnan, Barnett and Odoni 1993, Trivizas 1998, Beasley 2000, Anagnostakis 2001, Carr 2004]

Fairness: Constrained Position Shifting*

FCFS… …

… …k = 2

6 7 8 9 10

Runway scheduling under Constrained Position Shifting (CPS)

[Balakrishnan and Chandran AIAA GNC 2006; Operations Research (2010, to appear)]

Key result: We have proved that this is not true

Basic idea: We propose a way to represent solution space as a network whose size is linear in the number of aircraft

[USA/Europe ATM R&D Seminar 2007; Amer. Control Conf. 2007, Amer. Control Conf. 2008, Proceedings of the IEEE (2008)]

Well-studied problem, using various (mostly heuristic) approaches Challenge: Scheduling under CPS has been conjectured to have

exponential computational complexity in the number of aircraft [Carr 2004]

Various interesting extensions can be solved in (pseudo-)polynomial time as shortest-path problems on variations of this network Multiple objectives: Average delay, max. delay, sum of delay costs, fuel costs,

operating costs, robustness to uncertainties, etc. Evaluation of tradeoffs between multiple objectives

CPS network

Size of network is linear in the number of aircraft and exponential in k (≤3) Easy to incorporate arrival time windows and precedence constraints Precedence constraints make problem easier to solve

n = 6, k = 1

ts

3-4-53-4-64-6-54-5-65-4-63-5-63-6-5

1-2-31-2-41-3-21-3-42-1-32-1-42-3-4

1-21-32-12-3

12

2-3-42-3-52-3-62-4-52-4-62-5-42-5-63-4-53-4-6

3-5-64-3-54-3-64-5-6

3-5-4

1-2-31-2-41-2-51-3-41-3-51-4-31-4-52-3-42-3-5

2-4-53-2-43-2-53-4-5

2-4-3

Stage 1 Stage 2 Stage 3 Stage 4 Stage 5 Stage 6

Length = (2k+1)

Length = min(Stage#,2k+1)

# of arcs

[Balakrishnan and Chandran, AIAA GNC 2006; Operations Research (2010, to appear)]

Tradeoffs between throughput and delay costs

Randomly generated sequence, 40% Heavy, 40% Large, 20% SmallDelay costs of Heavy and Large are 9x (Delay cost of Small aircraft)

This gain in throughput comes at a very high cost

[Lee and Balakrishnan, Proceedings of the IEEE, 2008]

throughput gain

increase in cost

Is speeding up necessarily a good idea?

Accelerating from the nominal speed requires more fuel

However, speeding up the first aircraft in a sequence may decrease the delay incurred by subsequent aircraft Concept of Time advance

[Neuman and Erzberger 1991, Lee and Balakrishnan ACC 2008]

0

200

400

600

800

1000

1200

0 1 2 3 4 5Time Advance (min)

Extra fuel cost rel. to

ETAs ($)

FCFS

1-CPS

2-CPS

3-CPS

Evaluating the benefits of Time Advance

Total estimated fuel cost vs. allowable time advance

[Lee and Balakrishnan, Proceedings of the IEEE, 2008]

Modeling the taxi-out process

Queuing network model of the taxi-out process

Model inputs (ASPM database) Pushback schedules Gate assignments Runway configurations Weather conditions

Desired outputs Taxi-out time for each flight Level of congestion on airport surface Loading on departure queues

Estimating taxi-out time Taxi-out time expressed as

t = tunimpeded + ttaxiway + tdep.queue

Unimpeded taxi-out time (tunimpeded) dominates when the number of aircraft on the surface is low

Departure queue wait time (tdep.queue) dominates when the number of departures on the surface is high

Taxiway congestion term (ttaxiway) is important under medium traffic conditions

Parameter estimation: Unimpeded taxi-out time Departure throughput saturation Runway service process

[Simaiakis and Balakrishnan, AIAA GNC, 2009]

Improvement through including taxiway congestion term Assume ttaxiway = aR(t) R(t) is the number of aircraft on ramps and taxiways Choose a for best parameter fit

Without taxiway congestion term

With taxiway congestion term

[BOS; 4L,4R|4L,4R,9; VMC]

[Simaiakis and Balakrishnan, AIAA GNC, 2009]

Model validation: departure throughput

Model validated on 2008 data

Without taxiway congestion term

With taxiway congestion term

[BOS; 4L,4R|4L,4R,9; VMC]

[Simaiakis and Balakrishnan, AIAA GNC, 2009]

Taxi times as a function of congestion

High (N ≥ 17)

Medium (9 ≤N ≤16)

Low (N ≤ 8)

[Simaiakis and Balakrishnan, AIAA GNC, 2009]

Prediction of taxi-out timesm

inm

in

[Simaiakis and Balakrishnan, AIAA GNC, 2009]

One potential strategy: “N-Control” Conceptually simple: Limit the buildup of queues on the airport

surface by controlling the pushback times of aircraft If number of aircraft in movement area > Nctrl

Add any departing aircraft that requests clearance to a virtual departure queue, unless there is an aircraft waiting to use the gate, in which case, clear departure for pushback

If number of aircraft in movement area ≤ Nctrl Clear aircraft in virtual departure queue for pushback in FCFS order,

unless there is a flight waiting to use the gate of an aircraft in the virtual departure queue, in which case, clear departure for pushback

Model can be used to evaluate surface traffic management strategies

Pushback requests

Pushback clearances

Departure queue

Departure throughput

(Virtual)pushback queue for N-control

Ramp and taxiway interactions

Runway

Evaluating delay-emissions tradeoffs

Effect of stopping and starting while taxiing

Potential fuel burn impact from stopping on the surface

No significant impact

Effect of stopping and starting while taxiing

Impact depends on pilot actions

No significant change in thrust setting

thrust setting (%)

Using CFDR data to estimate impact of different taxi profiles

ICAO emissions databank assumes that aircraft taxi at a constant thrust setting of 7%

Using CFDR data (from Swiss Air) corresponding to taxi profiles of various aircraft, we Developed a regression model for fuel burn, that considers the

baseline fuel burn and the impact of stop-start events Stop-start impact: Estimate of the form

“The extra fuel burn from a start-stop event is equivalent to x additional minutes of taxi time”

Developed a (linear) regression model between fuel burn and thrust settings

Conducted above analysis for 9 aircraft types

Fuel burn / √Temp = Baseline fuel burn rate*(taxi time) + (Stop-start impact)*(# of stop-start events)

CFDR estimates vs. ICAO fuel burn rates

9.9%

8.8%

1.6%

7.4%

4.0%

6.3%

27.4%

4.1%

5.9%

[Khadilkar, Balakrishnan & Reynolds, working paper, 2010]

Labels indicate thrust settings; ICAO assumes 7% constant thrust during taxi.

Results of CFDR analysis

Taxi time is by far the dominating factor in determining taxi fuel burn

Impact of stops and turns appears to be due to the increase in taxi time, rather than due to a significant change in the fuel burn rate The majority of fuel consumed during stops appeared to be due to

the additional time, rather than the application of breakaway power; the exact estimates varied (depending on aircraft type) from 3%-15% of the average fuel burn during a stop corresponding to the breakaway

In the data set considered (mostly from European airports), each stop appeared to add on average 2 min to the taxi time, while each turn added on average 20 seconds

[Khadilkar, Balakrishnan & Reynolds, working paper, 2010]

Comparison of CFDR and surface surveillance data

Hybrid estimation to filter surface tracks Compare time spent in different taxi modes

ASDE-X data is only from BOS; CFDR is from mostly European and a few US airports

Predictive model for routing

Can we build a model of the airport surface that allows us to predict taxi times and recommend taxi routes?

Mean error: -32 sec, stdev: 141 secAt first node in path

Mean error: -4sec, stdev: 121 secAt second node in path

Take-offs from Node 7

Predictive model for routing

Predictive model for routing

Mitigating the impact of weather on air traffic operations

Objectives: More flexible and dynamic trajectories Increase efficiency of operations Increase operational robustness in the presence of weather

Routing flexibility increases capacity E.g., can we find a route within B km of original route that does not

pass through weather

Forecast Actual

B

[Michalek and Balakrishnan, ATM R&D Seminar 2009]

Dynamic fix relocation: 60-min ahead

Pilots typically assumed to avoid Level 3+ weather

Dynamic fix relocation: 60-min ahead

Pilots typically assumed to avoid Level 3+ weather

Dynamic fix relocation: 60-min ahead

Pilots typically assumed to avoid Level 3+ weather

Dynamic fix relocation: 60-min ahead

Pilots typically assumed to avoid Level 3+ weather

Dynamic fix relocation: 60-min ahead

Pilots typically assumed to avoid Level 3+ weather

Dynamic fix relocation: 60-min ahead

Pilots typically assumed to avoid Level 3+ weather

Dynamic fix relocation: 60-min ahead

Pilots typically assumed to avoid Level 3+ weather

Dynamic fix relocation: 60-min ahead

Pilots typically assumed to avoid Level 3+ weather

Resectorization and rerouting/relocating fix

Forecast Observed

WHI

NZCADI

T

LAGR

ANGE BRAVS

SINCA

DOOLY

GEETK

ROME

WHI

NZCADI

T

LAGR

ANGE BRAVS

SINCA

DOOLY

GEETK

ROME

New Sector boundaryOld sector boundaryNew FixOld Fix

Legend

Multi-objective optimization: Maximize probability of fixes being open; minimize movement of sector boundary; minimize deviation of route from procedural route; try to keep all sectors open

Pilots typically assumed to avoid Level 3+ weather

Summary statistics of resectorization (fix/route relocation)

Note that these metrics do not reflect the probability with which fix was predicted to be open.

77%

71%

85%

72%

95%

88%

86%

85%

0%

0%

7%

7%

4%

5%

5%

3%

[Michalek and Balakrishnan, CDC 2010]

Summary statistics of resectorization (fix/route relocation)

[Michalek and Balakrishnan, CDC 2010]

Pilot behavior

Of course, pilots do occasionally penetrate weather today Can provide additional flexibility Can we identify the factors that determine their behavior?

0 1 2 3 4 5 60

0.1

0.2

0.3

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0.6

0.7

0.8

0.9

1

VIL Level

Pro

ba

bili

ty o

f p

ilot

pe

ne

tra

tio

n

0 1 2 3 4 5 60

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

VIL Level

Pro

ba

bili

ty o

f p

ilot

pe

ne

tra

tio

n

“3 Weather days” “Really bad weather day”

Conclusions

Air transportation system presents many important problems that require the development of CPS methodologies

Solutions have the potential to increase system efficiency (reduce delays), robustness and energy efficiency (reduce fuel burn), and decrease environmental impact

Key objectives should include More dynamic, flexible trajectories (and system structure) Multi-objective control and balancing of tradeoffs More decentralized decision-making