joint work with James Jones and David Lovell From Air Traffic Management to Finding a Parking Space: Dynamically Assigning Destination Resources while Enroute Michael Ball Robert H Smith School of Business & Institute for Systems Research University of Maryland joint work with James Jones and David Lovell

Problem Scenario: airplane or auto seeks to obtain or adjust necessary arrival resources while enroute Sample Resources: Arrival time slot Bay at loading dock Arrival gate Parking space Enroute adjustments: Change in speed Change in route (incl destination)

Recent research on speed control in air traffic management (Jones, Ball & Lovell) Stochastic Optimization Models for Transferring Delay along Flight Trajectories in Order to Reduce Fuel Usage Combining Control by CTA and Dynamic Enroute Speed Adjustment to Improve Ground Delay Program Performance

The Players: Pilot / Flight Deck Airline Operational Control Center FAA -- CAAC/ATMB: Air Traffic Control

Air Traffic Control (ATC) The Players: Pilot / Flight Deck Airline Operational Control Center FAA: Air Traffic Control (ATC) Collaborative decision making initiatives, starting in the mid-90s, have sought to improve FAA-airline joint decision-making.

Aircraft Fuel Usage Characteristics Specific Range NM/ton Fuel efficiency is a concave function of speed General Characteristics: Cost curves are relatively flat Cruise speeds can often exceed the maximum range Slowing down during cruise can increase specific range Maximum Range Range of Operation vmin vmax Mach Number

How can aircraft adjust their travel time today? Reduce or increase speed within certain window. Take “short-cuts” allowed by air traffic control (ATC) Path extension techniques to increase time, e.g. vectoring, circular holding patterns, “tromboning” … 1.) may be restricted in congested areas (more likely to be available on long-haul flights). … in future (NextGen), new navigational technologies may make individual flights more independent and allow greater freedom in speed determination; more precise and flexible flight planning will decrease 2.) .

Insights on arrival delays: Tromboning and vectoring:

Circular holding patterns: Jan 15, 2009 12:30-12:45pm Circular holding patterns: 40 nm 100 nm

Jan 15, 2009 12:30-12:45pm Two ways to get from SFO to PHL in 4 hrs, 45 min: 40 nm 30 min on green path 4 hrs, 30 min departure and en-route time: 4 hrs, 15 min departure and en-route time: 15 min on red path 100 nm Fuel usage comparison: fuel(4 hrs 30 min departure + enroute) ~ fuel (4 hrs 15 min departure + enroute) fuel(15 min on red path) << fuel (30 min green path)  2nd option uses much more fuel

Europe main 34: ave: 2.50 min US OEP 34: ave: 2.45 min From (Knorr et al 2011)

Approach Develop a process for transferring delay away from the terminal by assigning a Controlled Time of Arrival (CTA) at 500 nm

Multiple performance criteria / objectives: Throughput Multiple Integer programming and stochastic integer programming models developed: Multiple performance criteria / objectives: Throughput Delay transfer / fuel usage Equitable treatment of airlines Stochastic aspects: Unmanaged flights Airport capacity

Sample results: delay transfer graph On average ~ 19% of delay is transferred; Why can’t more delay be transferred? Limit on speed ranges / time that can be absorbed within 500 nmi Congestion due to unmanaged flights Test case: ATL, 5/1/2012

Research described today: Stochastic Optimization Models for Transferring Delay along Flight Trajectories in Order to Reduce Fuel Usage Combining Control by CTA and Dynamic Enroute Speed Adjustment to Improve Ground Delay Program Performance

Ground Delay Programs control = flight departure time delayed departures delayed departures control = flight departure time decision variable = flight arrival time (slot) delayed arrivals/ no airborne holding delayed departures 16

GDP Planning Process These processes adjust the arrival slots assigned to flights, which in turn are used to adjust the controlled time of departure (CTD) of each flight.

Motivation: flight exemptions Ground delay SFO EWR flight vs ORD  EWR flight?? Choice of flights to reassign arrival slot from 4:00  4:45 (EST) SFO flight  departure time change: 10:50  11:35 (EST) ORD flight  departure time change: 1:50  2:35 (EST) 5 hr, 10 min flight Figure 10: United States National Airspace System Suppose weather at EWR clears at 12:45 (EST)  no delays are necessary!! EWR ORD SFO

Motivation: flight exemptions Ground delay SFO EWR flight vs ORD  EWR flight?? Choice of flights to reassign arrival slot from 4:00  4:45 (EST) SFO flight  departure time change: 10:50  11:35 (EST) ORD flight  departure time change: 1:50  2:35 (EST) 5 hr, 10 min flight Figure 10: United States National Airspace System Suppose weather at EWR clears at 12:45 (EST)  no delays are necessary.!! EWR ORD SFO At 12:45 SFO flight would already be en-route  delay already taken (unecessarily).

Motivation: flight exemptions Ground delay SFO EWR flight vs ORD  EWR flight?? Choice of flights to reassign arrival slot from 4:00  4:45 (EST) SFO flight  departure time change: 10:50  11:35 (EST) ORD flight  departure time change: 1:50  2:35 (EST) 5 hr, 10 min flight Figure 10: United States National Airspace System Suppose weather at EWR clears at 12:45 (EST)  no delays are necessary.!! EWR ORD SFO At 12:45 ORD flight would still be on the ground  delay could be rescinded.

Lesson: it is better to “focus” delays on short-haul flights; this is typically done by exempting flights outside an exemption radius from the ground delay program Figure 10: United States National Airspace System EWR ORD SFO

Speed Control in Ground Delay Programs and a New Control Architecture Current practice: while planning is based on controlled times of arrival (CTAs), actual control is based on a controlled time of departure (CTD) to flights; the CTD is obtained from the CTA by subtracting the estimated flight time. Controlling based on CTAs (rather than CTDs) may offer a more attractive means for controlling flights: Provides carriers more flexibility and control Allows for system-wide trade-offs Delay allocation under conventional GDP Planning Travel Time GD STD CTD STA CTA

Delay allocation under CTA based GDP Planning Speed Control in Ground Delay Programs and a New Control Architecture Current practice: while planning is based on controlled times of arrival (CTAs), actual control is based on a controlled time of departure (CTD) to flights; the CTD is obtained from the CTA by subtracting the estimated flight time. Controlling based on CTAs (rather than CTDs) may offer a more attractive means for controlling flights: Provides carriers more flexibility and control Allows for system-wide trade-offs Flexibility in departure time Delay allocation under CTA based GDP Planning Variable Delay Travel Time GDmin STD CTDmin CTDmax STA CTA

Delay allocation under CTA based GDP Planning Speed Control in Ground Delay Programs and a New Control Architecture Current practice: while planning is based on controlled times of arrival (CTAs), actual control is based on a controlled time of departure (CTD) to flights; the CTD is obtained from the CTA by subtracting the estimated flight time. Controlling based on CTAs (rather than CTDs) may offer a more attractive means for controlling flights: Provides carriers more flexibility and control Allows for system-wide trade-offs Dynamically adjust arrival time Delay allocation under CTA based GDP Planning Variable Delay Travel Time GDmin STD CTDmin CTDmax STA CTA

FAA procedural modifications New GDP Architecture FAA procedural modifications Replace the use of CTDs with CTAs in GDP planning Remove the exemption radius Allow en route speed changes by carriers Airline decision making modifications Incorporate speed changes into substitution and cancellation process Introduce decision-support models to support airline decision-making with substitutions and cancellations (including airborne flights and new exemption policy)  airlines take over weather “hedging” formally done by FAA

Airline cancellation and substitution problem (w CTD control): assignment fixed Delta flights: 4:00 4:04 4:08 alternate assignments Slots assigned to Delta 4:12 4:16 4:20 4:24 4:28 4:32 airborne flights Cancel flights on ground 26

Airline cancellation and substitution problem (w CTA control): Delta flights: 4:00 4:04 4:08 alternate assignments Slots assigned to Delta 4:12 4:16 4:20 4:24 4:28 4:32 airborne flights Cancel flights on ground 27

Airline cancellation and substitution problem (w CTA control): Delta flights: GDP canceled: newly avail slot 4:00 4:04 4:08 alternate assignments Slots assigned to Delta 4:12 4:16 4:20 4:24 option to adjust speed and arrive in “new” slot 4:28 4:32 airborne flights Cancel flights on ground 28

System modifications: FAA role / problem becomes easier Changes required in the manner in which slot allocations are dynamically adjusted over time Airlines’ problem becomes harder: must dynamically adjust flight speeds and coordinate airborne flight arrival times with departure times of flights on the ground stochastic integer programming models support airline flight planning and dynamic flight adjustments

Back to Original Problem Scenario: Parking problem: New technology – info distributed dynamically to drivers on which spaces are available, when will spaces become available; drivers can reserve enroute, etc

Key Issues for both cases How are resources (slots / parking spaces) allocated: administrative rules market mechanism, e.g. auction 2 level approaches: long-term allocation of reservation or priority via admin or market mechanism / short term allocation based on assigned priority Stochastic aspects: travel times airport capacity (number slots available) when parking space will be vacated Dynamics: trading and dynamic reallocation User vs system optimal; system vs profit maximizing Periodic optimization / re-optimization vs real-time / transaction-oriented control

Air traffic mgmt. scenarios Strong winds encountered; will arrive 10 min late. AOC ATC

Air traffic mgmt. scenarios Some winds encountered; prefer to arrive 10 min late. AOC ATC

Air traffic mgmt. scenarios New slot available you can arrive 10 min earlier. AOC ATC

Air traffic mgmt. scenarios Several new slots available; how can you make use of them AOC All of these scenarios will require optimization / coordination / trading among multiple airlines ATC

Auto / parking scenarios My estimated arrival time is 8:30 AM, I want a nearby parking space. Parking coordination center

Auto / parking scenarios I am willing to park further away than previously indicated. Parking Coordination Center

Auto / parking scenarios I am willing to pay more than previously indicated. Parking Coordination Center

Auto / parking scenarios Don’t need space – I have decided to skip work and go play golf. Parking Coordination Center

Auto / parking scenarios A space likely will be available near location X. Parking Coordination Center

Auto / parking scenarios Your assigned space has not been vacated it most likely will not be available. Parking Coordination Center

Auto / parking scenarios A better space is available for a higher price. Parking Coordination Center

Issues of user vs system optimal Final Thoughts Problems discussed are stochastic, dynamic and have multiple decision makers. Solution architecture can employ iterative optimization / re-optimization or real-time control. Issues of user vs system optimal Good solution approaches should take mechanism design perspective.

Some References Zou, B, N Kafle, O Wolfson & J Lin (2015) A mechanism design based approach to solving parking slot assignment in the information era, Transportation Research Part B, 81, 631 – 653. Gang Y & C Cassandras (2012) A new “Smart Parking” System Infrastructure and Implementation, Procedia: Social and Behavioral Sciences, 54, 1278 – 1287. Jones, J, D Lovell & M Ball (2017) Stochastic Optimization Models for Transferring Delay along Flight Trajectories in Order to Reduce Fuel Usage, Transportation Science, Articles in Advance, Published Online: February 24, 2017 https://doi.org/10.1287/trsc.2016.0689 Jones, J., D. Lovell and M. Ball (2015) Combining Control by CTA and Dynamic Enroute Speed Adjustment to Improve Ground Delay Program Performance, in Proceedings of the 11th USA/Europe Air Traffic Management R&D Seminar.