ActionWebs : Energy -efficient Air Transportation Systems

Hamsa Balakrishnan Massachusetts Institute of Technology

NSF−UC Berkeley−MIT ActionWebs Kickoff Meeting

December 17, 2009

ActionWebs: Energy-efficient Air Transportation Systems

The Air Transportation System

Key player in global travel and commerce• 2 billion passenger enplanements in 2006 (US: ~700 mil.)• 29 million flights worldwide in 2007 (US: ~30,000 flights

per day)• Number of flights expected to grow ~2-3x by 2025• Jet fuel prices are volatile

Air traffic delays

In 2007, domestic air traffic delays cost the US economy $41 billion• 24% of arrivals at least 15 min late (avg. delay: 46 min)• ~20% of total domestic flight time was delay

• 1,565 flights delayed on the ground (not at gate) for over 3 hours

• 60% rise in rate of passenger complaints (2006-07)• $19 billion in direct operating costs to the airlines• $12 billion cost to passengers

Data sources: IATA, FAA ASPM and OPSNET databases, Bureau of Transportation Statistics, Joint Economic Committee, Airline Quality Rating 2008

Air Traffic Management: current functional architecture

Approved handoffs

Clearances/advisories

[Adapted from A. Haraldsdottir (Boeing), 1998]

Flight Planning

Flight schedules

Weather

National Flow

Planning

Facility Flow

Planning

Filed flight plans

Capacity schedule

Aircraft Guidance

& Navigatio

n

Aircraft state

< 5 min

Pilot

Recorded ETMS data

Hrs-day

Facility Traffic Control

5 min

Facility ATC

Airline Operations Center

Central Flow Mgmt Unit

Traffic Mgmt Unit

Facility Traffic

Planning

Approved flight plans/ planned

flow rates

Traffic situation

5-20 min

Desired traffic loads

Negotiate handoffs

Airlines Facility

Traffic Manager

Efficiency SafetyPlanning Execution

Throughput

Consequence: Delay propagation!

Key research objectives

More decentralized decision-making More flexible and dynamic trajectories Increase efficiency of operations Increase operational robustness in the presence of

weather Decrease fuel burn and environmental impact Multi-objective control techniques for balancing tradeoffs

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

More decentralized decision-making

An Eulerian model for distributed feedback control

s1|s2 s2|s3

s2|s4

s3|s2s2|s1

s4|s2

s3|s5

s5|s3

s3|s6

s6

Sector 1

Sector 2

Sector 3

Sector 4

Sector 5 Network model has two nodes for each sector boundary (one on each sector)

Links correspond to the regions of airspace

Set of queues associated with each node, each containing aircraft currently in that region of the airspace

Aircraft move from one node to another, at rates determined by the flow rates along the links

Le Ny and Balakrishnan, ACC 2009]

An Eulerian model for distributed feedback control

At every node, there is a queue corresponding to each destination

Then, we model the dynamics of queue m at node i by:

is the rate allocation on link (i,j) to flow m (control variable) Putting this together,

airport arrivals/departures inflow outflow

Advantages of this framework

Can apply distributed feedback control policies • For example, MaxWeight

Attractive idea, because sector controllers only need to talk to their neighbors (much like today!)

Build model from archived ETMS (surveillance) data

Le Ny and Balakrishnan, ACC 2009; Kannan, Harvard BA thesis, 2009]

For example, the airspace surrounding SFO

Numbers denote maximum inter-sector handoff rates in aircraft/min

Scheduling with routing

No routing (predefined routes):

With routing:(better balanced loads in two routes)

Time (in hrs) Time (in hrs)

Sector load (# of aircraft)

Sector load (# of aircraft)

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

weather

Routing using convective weather forecasts

Convective weather has a significant impact on NAS operations, especially in summer

As the time horizon increases, forecast errors increase (especially when we are trying to forecast storm intensity in a 1 km x 1 km pixel)

5 min 30 min

Route flexibility improves accuracy

Forecast Actual

B

Decrease fuel burn and environmental impact Multi-objective control techniques for balancing tradeoffs

Environmental impacts of air transportation

Aviation is responsible for 3% of total global carbon emissions• Aircraft contribute about 12% of CO2 emissions from the

transportation sector• According to the European Union, international aviation is

one the largest growing contributors to CO2 emissions, having increased 87% between 1990 and 2004

The aviation sector was responsible for 187.5 million metric tons of CO2 emissions in the US in 2007 (about 3% of total emissions)

[Commission of the European Communities, 2006; EPA 2007]

Environmental impact of air traffic delays

Air traffic delays have an environmental cost, in addition to the inconvenience to passengers, airline costs and impact on the economy

In 2007, domestic air traffic delays• 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

Enhancing system capacity and improving efficiency will result in environmental benefits as well

[Joint Economic Committee of the US Senate, 2008]

Surface emissions from taxiing aircraft

In 2007, aircraft in the US spent over 63 million minutes taxiing in to their gates, and over 150 million minutes taxiing out to their runways• 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 for departure

These flights burn fuel and contribute to emissions at low altitudes, and adversely impact local air quality

Taxi-out emissions correspond to about 5% of the fuel burn and emissions from aircraft operations

How do we optimize surface traffic movement to reduce aircraft emissions from taxi processes?

[FAA ASPM database; Balakrishnan et al. 2008]

Aircraft taxi trajectories from surface surveillance data

Aircraft taxi trajectories from surface surveillance data

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 actionsNo significant change in throttle setting

Using CFDR data to estimate impact of different taxi profiles

ICAO emissions databank assumes that aircraft taxi at a constant throttle 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 throttle settings

• Conducted above analysis for 9 aircraft types

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

CFDR vs. ICAO fuel burn estimates

Impact of a stop-start event

Labels indicate throttle settings

Minimizing fuel burn impacts of aircraft trajectories

Multi-objective control of taxi trajectories

Identification of hybrid system model of taxi

trajectory

Surface surveillance

Flight data recorder

Identification of fuel burn

model

Surface surveillance

Flight data recorder

(archival data)

(real-time data)

Multiple real-world objectives

Maximize throughput

Minimize average delay

Minimize maximum delay

Maximize robustness

Minimize fuel consumption

Minimize fuel costs

Minimize operating costs

Minimize passenger delays

Multiple stakeholders with differing objectives, tradeoffs involved

[Balakrishnan and Chandran, Operations Research, to appear; Lee and Balakrishnan, Proceedings of the IEEE, 2008]

Summary

ActionWebs concepts can help achieve• More decentralized decision-making• More flexible and dynamic trajectories• Increased efficiency of operations• Increased operational robustness in the presence of

weather• Decreased fuel burn and environmental impact

The Air Transportation System test bed will be an exciting application of these concepts!