A Study of Tradeoffs in Airport Coordinated Surface Operations

A Study of Tradeoffs in Airport Coordinated Surface Operations Ji MA, Daniel DELAHAYE, Mohammed SBIHI ENAC École Nationale de l Aviation Civile, Toulouse, France Paolo SCALA, Miguel MUJICA MOTA Amsterdam University of Applied Sciences, Amsterdam, The Netherlands EIWAC 2017, November 15, 2017 Ma, Delahaye, Sbihi, Scala, Mujica Mota (ENAC, HvA) A Study of Tradeoffs in Airport Surface Operations EIWAC 2017 1 / 31

Outline 1 Background and problem description 2 Mathematical model 3 Solution approach 4 Simulation results 5 Conclusions and perspectives Ma, Delahaye, Sbihi, Scala, Mujica Mota (ENAC, HvA) A Study of Tradeoffs in Airport Surface Operations EIWAC 2017 2 / 31

Air traffic forecast According to Airbus global market forecast 2015-2034, air traffic will double in the next 15 years. 39 out of the 47 aviation mega cities are largely congested today. airport infrastructure is adequate airports with potential for congestion airports where conditions make it impossible to meet demand Ma, Delahaye, Sbihi, Scala, Mujica Mota (ENAC, HvA) A Study of Tradeoffs in Airport Surface Operations EIWAC 2017 4 / 31

Airport capacity The subject of airport capacity and delay has received a great amount of attention. Ma, Delahaye, Sbihi, Scala, Mujica Mota (ENAC, HvA) A Study of Tradeoffs in Airport Surface Operations EIWAC 2017 5 / 31

Towards integrated approach Arrival Management Problem Landing sequencing Ensure proper separation Surface Management Problem Arriving aircraft taxi-in Departing aircraft taxi-out Departure Management Problem Take-off times and sequences for departing flights Ensure proper separation Ma, Delahaye, Sbihi, Scala, Mujica Mota (ENAC, HvA) A Study of Tradeoffs in Airport Surface Operations EIWAC 2017 6 / 31

Given data (1/2) Airport route network (Paris CDG, west configuration) Ma, Delahaye, Sbihi, Scala, Mujica Mota (ENAC, HvA) A Study of Tradeoffs in Airport Surface Operations EIWAC 2017 8 / 31

Given data (2/2) Given a set of flights F = A D, where A stands for arrival, D for departure : C f : wake turbulence category ; M f : meta-gate ; E f : runway entry point for f D or runway exit point for f A ; P 0 f : initial off-block time for f D ; L f : initial landing time for f A ; H f : initial holding point at runway threshold ; Ma, Delahaye, Sbihi, Scala, Mujica Mota (ENAC, HvA) A Study of Tradeoffs in Airport Surface Operations EIWAC 2017 9 / 31

Given data (2/2) Given a set of flights F = A D, where A stands for arrival, D for departure : C f : wake turbulence category ; M f : meta-gate ; E f : runway entry point for f D or runway exit point for f A ; P 0 f : initial off-block time for f D ; L f : initial landing time for f A ; H f : initial holding point at runway threshold ; R f : a set of alternate routes depending on the origin and the destination of f. Ma, Delahaye, Sbihi, Scala, Mujica Mota (ENAC, HvA) A Study of Tradeoffs in Airport Surface Operations EIWAC 2017 9 / 31

Alternate routes generation Previous work Single path : Aircraft follow a predetermined taxi route. Free path : Any route can be assigned to an aircraft. Alternate path : Several routing options are proposed after applying the k-shortest path algorithm. Ma, Delahaye, Sbihi, Scala, Mujica Mota (ENAC, HvA) A Study of Tradeoffs in Airport Surface Operations EIWAC 2017 10 / 31

Alternate routes generation Previous work Single path : Aircraft follow a predetermined taxi route. Limits Free path : Any route can be assigned to an aircraft. Alternate path : Several routing options are proposed after applying the k-shortest path algorithm. Ma, Delahaye, Sbihi, Scala, Mujica Mota (ENAC, HvA) A Study of Tradeoffs in Airport Surface Operations EIWAC 2017 10 / 31

Alternate routes generation Previous work Single path : Aircraft follow a predetermined taxi route. Limits Free path : Any route can be assigned to an aircraft. Alternate path : Several routing options are proposed after applying the k-shortest path algorithm. Our approach Extract alternate routes sets by analyzing airport s flight radar records to find the operationally used potential routes set. Ma, Delahaye, Sbihi, Scala, Mujica Mota (ENAC, HvA) A Study of Tradeoffs in Airport Surface Operations EIWAC 2017 10 / 31

Preprocessed routes set using radar data Analyzing 13 days of real traffic (February 2016) West configuration in CDG In total 510 combinations of different pairs (runway meta-gate) Table Route options count Number of Number of pairs route options i displaying i options 1 342 2 5 159 6 9 9 Figure Route example followed by 309 aircraft Ma, Delahaye, Sbihi, Scala, Mujica Mota (ENAC, HvA) A Study of Tradeoffs in Airport Surface Operations EIWAC 2017 11 / 31

Preprocessed routes set using radar data 4 route options example Ma, Delahaye, Sbihi, Scala, Mujica Mota (ENAC, HvA) A Study of Tradeoffs in Airport Surface Operations EIWAC 2017 12 / 31

Decision variables For arrivals f A : r f R f : taxi-in route t h f : holding time (time spent in runway crossing queues) h f : holding point : t h f {0, t, 2. t,..., N a h. t} Ma, Delahaye, Sbihi, Scala, Mujica Mota (ENAC, HvA) A Study of Tradeoffs in Airport Surface Operations EIWAC 2017 13 / 31

Decision variables For arrivals f A : r f R f : taxi-in route t h f : holding time (time spent in runway crossing queues) h f : holding point : t h f {0, t, 2. t,..., N a h. t} For departures f D : p f : pushback time, where p f {P 0 f, P0 f + t, P0 f + 2. t,..., P0 f + N p. t} r f R f : taxi-out route t h f : holding time (waiting time at takeoff runway threshold) t h f {0, t, 2. t,..., N d h. t} Ma, Delahaye, Sbihi, Scala, Mujica Mota (ENAC, HvA) A Study of Tradeoffs in Airport Surface Operations EIWAC 2017 13 / 31

Constraints Minimum taxi separation of 60 meters between two aircraft Take-off single-runway separation requirements, in seconds. Category Leading Heavy Medium Light Heavy 90 60 60 Trailing Medium 120 60 60 Light 120 60 60 Holding point capacity For arrivals : 1 or 2 For departures : depends on runway pressure Ma, Delahaye, Sbihi, Scala, Mujica Mota (ENAC, HvA) A Study of Tradeoffs in Airport Surface Operations EIWAC 2017 14 / 31

Ground conflict detection Link conflict Flight f Flight g Node conflict Flight f Detection zone Flight f Flight g Rn Node u Link l = (u,v) Node v Node n Flight g Ma, Delahaye, Sbihi, Scala, Mujica Mota (ENAC, HvA) A Study of Tradeoffs in Airport Surface Operations EIWAC 2017 15 / 31

Runway conflict We note the accumulated time of separation violation for all pairs of aircraft as an indicator for our runway evaluation. Pred.\Succ. Heavy Medium Light Cross Heavy 96 120 120 60 Medium 60 60 60 60 Light 60 60 60 60 Cross 40 40 40 10 Particular case (Triangle inequality) Sequence : Heavy Departure Crossing Medium Departure Departure Heavy Crossing 60 s 40 s Departure Medium 60 s 40 s 120 s Ma, Delahaye, Sbihi, Scala, Mujica Mota (ENAC, HvA) A Study of Tradeoffs in Airport Surface Operations EIWAC 2017 16 / 31

Objective function We minimize C + α (p f P 0 f ) + β h f + γ (t f p f ) + (t f L f ) where f D C : Total number of conflicts ; (p f P 0 f ) : Total pushback f D delay ; h f : Total holding time ; f F f F f D f A (t f p f ) : Total taxi time for f D departures ; (t f L f ) : Total taxi time for arrivals ; α, β and γ : weighting coefficients corresponding to pushback delays, holding time and taxi time respectively. f A Ma, Delahaye, Sbihi, Scala, Mujica Mota (ENAC, HvA) A Study of Tradeoffs in Airport Surface Operations EIWAC 2017 17 / 31

Solution approach Factors to be considered Benefits of integrated airport optimization are promising The complexity of the integrated problem would grow Computational time is critical in practice Heuristics and hybrid methods have more potential than exact approaches Ma, Delahaye, Sbihi, Scala, Mujica Mota (ENAC, HvA) A Study of Tradeoffs in Airport Surface Operations EIWAC 2017 19 / 31

Simulated annealing AT INIT TEMP. Unconditional Acceptance OBJECTIVE FUNCTION HILL CLIMBING HILL CLIMBING Moved accepted with probability e E T HILL CLIMBING AT FINAL TEMP NUMBER OF ITERATIONS Ma, Delahaye, Sbihi, Scala, Mujica Mota (ENAC, HvA) A Study of Tradeoffs in Airport Surface Operations EIWAC 2017 20 / 31

Neighborhood selection (1/2) Aircraft list x 1 x i x N Decision Changes Arrivals { T 1 T i T N G 1 G i G N R 1 R i R N Take off performance } Departures Ground performance Runway crossing performance Ma, Delahaye, Sbihi, Scala, Mujica Mota (ENAC, HvA) A Study of Tradeoffs in Airport Surface Operations EIWAC 2017 21 / 31

Neighborhood selection (2/2) Example (Ground performance) : Ma, Delahaye, Sbihi, Scala, Mujica Mota (ENAC, HvA) A Study of Tradeoffs in Airport Surface Operations EIWAC 2017 22 / 31

Case study 9 : 00 10 : 00, February 18, 2016 100 flights (69 departures, 31 arrivals) Medium (65%), Heavy (35%) Three major aspects concerning airport ground performances are discussed : Ma, Delahaye, Sbihi, Scala, Mujica Mota (ENAC, HvA) A Study of Tradeoffs in Airport Surface Operations EIWAC 2017 24 / 31

Taxi reroute Benefits of incorporating taxi reroutes on the airport performance metrics Objectives : Conflicts 30 random tests Table CPU time comparison for T p = 10 min, T d h = 10 min, T a h = 3 min Decision Choice With Without Taxi Reroute Taxi Reroute Av. CPU time 11 s 26 s Min CPU time 4 s 4 s Max CPU time 25 s 112 s Failed number 0/30 2/30 Objectives : Conflicts + Delay + Taxi time Table Pushback delay and holding time comparison, 27R, 26L Landing ; 27L, 26R Takeoff Total With Without Taxi Reroute Taxi Reroute thold 27R 1.8 min 2.1 min thold 26L 8.8 min 9.5 min thold 27L 14 min 15.5 min thold 26R 40.5 min 45.6 min PB Delay 27L 22.2 min 32 min PB Delay 26R 79 min 81 min Ma, Delahaye, Sbihi, Scala, Mujica Mota (ENAC, HvA) A Study of Tradeoffs in Airport Surface Operations EIWAC 2017 25 / 31

Runway holding Take-off time comparison between FCFS strategy and optimized case for runway 26R Runway FCFS average holding time Optimized average holding time Take-off 26R 7.1 min 2.6 min Landing 26L 0 0.5 min Ma, Delahaye, Sbihi, Scala, Mujica Mota (ENAC, HvA) A Study of Tradeoffs in Airport Surface Operations EIWAC 2017 26 / 31

Trade-offs between pushback time and holding time T p : Maximum pushback delay : Maximum holding time for departures T d h Figure Trade-offs between pushback time and holding time for runway 26R Ma, Delahaye, Sbihi, Scala, Mujica Mota (ENAC, HvA) A Study of Tradeoffs in Airport Surface Operations EIWAC 2017 27 / 31

Conclusions An optimization approach to solve in a unified manner the ground movement problem and runway scheduling problem ; Alternate taxi routes are constructed based on surface surveillance records with respect to current procedural factors ; Different control strategies (controlled pushback time, taxi reroutes, controlled holding time) on the airport surface to investigate their impacts and benefits. Ma, Delahaye, Sbihi, Scala, Mujica Mota (ENAC, HvA) A Study of Tradeoffs in Airport Surface Operations EIWAC 2017 29 / 31

Perspectives Integrated optimization of TMA and airport Ma, Delahaye, Sbihi, Scala, Mujica Mota (ENAC, HvA) A Study of Tradeoffs in Airport Surface Operations EIWAC 2017 30 / 31

Thank you for your attention! Ma, Delahaye, Sbihi, Scala, Mujica Mota (ENAC, HvA) A Study of Tradeoffs in Airport Surface Operations EIWAC 2017 31 / 31