1/28 Thibault Lehouillier , Jérémy Omer , François Soumis , Cyril Allignol Interactions between Operations and Context and Motivations Planning in Air Traffic Control Simulation Algorithms Experimental Design Experimental Thibault Lehouillier 1 2 Jérémy Omer 1 2 Results and Analysis François Soumis 1 2 Cyril Allignol 3 Simulations without conflict resolution Simulations with 1 École Polytechnique de Montréal conflict resolution 2 Groupe d’Études et de Recherche en Analyse de Décisions Conclusions and 3 École Nationale de l’Aviation Civile Perspectives May 29, 2014
Sommaire 2/28 Thibault Lehouillier , Jérémy Omer , François Soumis , Cyril Allignol Context and Context and Motivations Motivations Simulation Algorithms Experimental Simulation Algorithms Design Experimental Results and Analysis Experimental Design Simulations without conflict resolution Simulations with conflict resolution Conclusions and Experimental Results and Analysis Perspectives Conclusions and Perspectives
Plan 3/28 Thibault Lehouillier , Jérémy Omer , François Soumis , Cyril Allignol Context and Context and Motivations Motivations Simulation Algorithms Experimental Simulation Algorithms Design Experimental Results and Analysis Experimental Design Simulations without conflict resolution Simulations with conflict resolution Conclusions and Experimental Results and Analysis Perspectives Conclusions and Perspectives
Different Layers of the Air Traffic Management 4/28 Thibault Lehouillier , Jérémy Omer , Different layers corresponding to different time horizons: François Soumis , Cyril Allignol 1. Airspace management filter: Context and ◮ define the structure of the route network Motivations ◮ define navigation rules Simulation Algorithms ◮ divide the airspace between sectors with given capacities Experimental Design Experimental Results and Analysis Simulations without conflict resolution Simulations with conflict resolution Conclusions and Perspectives
Different Layers of the Air Traffic Management 4/28 Thibault Lehouillier , Jérémy Omer , Different layers corresponding to different time horizons: François Soumis , Cyril Allignol 1. Airspace management filter: Context and ◮ define the structure of the route network Motivations ◮ define navigation rules Simulation Algorithms ◮ divide the airspace between sectors with given capacities Experimental Design Experimental 2. Air Traffic Flow Management (ATFM): Results and ◮ file flight plans a few hours before planned take-off Analysis Simulations ◮ regulate traffic to enforce sector capacities with without conflict resolution ground-holding (CASA) Simulations with conflict resolution Conclusions and Perspectives
Different Layers of the Air Traffic Management 4/28 Thibault Lehouillier , Jérémy Omer , Different layers corresponding to different time horizons: François Soumis , Cyril Allignol 1. Airspace management filter: Context and ◮ define the structure of the route network Motivations ◮ define navigation rules Simulation Algorithms ◮ divide the airspace between sectors with given capacities Experimental Design Experimental 2. Air Traffic Flow Management (ATFM): Results and ◮ file flight plans a few hours before planned take-off Analysis Simulations ◮ regulate traffic to enforce sector capacities with without conflict resolution ground-holding (CASA) Simulations with conflict resolution Conclusions and Perspectives 3. Air Traffic Control (ATC) where controllers: ◮ monitor sectors; ◮ ensure safe transitions between sectors; ◮ maintain separation between aircraft at all times. 5NM 1000 ft Figure 1: Vertical and horizontal separation
Present and future: what is at stake? 5/28 Thibault Lehouillier , Jérémy Omer , François Soumis , Cyril Allignol Context and Motivations 1. Present situation: Simulation ◮ airspace congested in Europe Algorithms Experimental Design ◮ costly delays crucial to companies Experimental Results and ◮ few conflicts to solve for controllers Analysis Simulations without conflict resolution Simulations with conflict resolution Conclusions and Perspectives
Present and future: what is at stake? 5/28 Thibault Lehouillier , Jérémy Omer , François Soumis , Cyril Allignol Context and Motivations 1. Present situation: Simulation ◮ airspace congested in Europe Algorithms Experimental Design ◮ costly delays crucial to companies Experimental Results and ◮ few conflicts to solve for controllers Analysis Simulations without conflict resolution Simulations with 2. Questions needing answers for the future: conflict resolution ◮ what will future traffic look like? Conclusions and Perspectives ◮ how will regulations adapt to this future traffic? ◮ what economic outcomes can be expected? ◮ how to be better prepared?
Our contributions 6/28 Thibault Lehouillier , Jérémy Omer , François Soumis , Cyril Allignol Context and Motivations Simulation Algorithms Based on a air traffic simulator we: Experimental Design Experimental Results and ◮ simulate future French traffic up to 2035 Analysis Simulations ◮ design different regulation scenarios without conflict resolution Simulations with ◮ compute ground-holding costs and ATC costs conflict resolution Conclusions and ◮ perform a traffic and cost analysis Perspectives
Plan 7/28 Thibault Lehouillier , Jérémy Omer , François Soumis , Cyril Allignol Context and Context and Motivations Motivations Simulation Algorithms Experimental Simulation Algorithms Design Experimental Results and Analysis Experimental Design Simulations without conflict resolution Simulations with conflict resolution Conclusions and Experimental Results and Analysis Perspectives Conclusions and Perspectives
The whole picture 8/28 Thibault Lehouillier , Jérémy Omer , François Soumis , Cyril Allignol Traffic Real Increase increase traffic Context and factors procedure data Motivations Simulation Algorithms Experimental Regulation Design algorithms Experimental Results and Analysis Simulations without conflict resolution Trajectories Simulations with simulator conflict resolution Conclusions and Perspectives Conflict Resolution Algorithm Simulator Figure 2: Experimental design
Traffic increase 9/28 Thibault Lehouillier , Jérémy Omer , François Soumis , Cyril Allignol Context and Motivations Simulation Procedure parametrized by a multiplying factor f (i.e 40 % ): Algorithms Experimental ◮ go from n flights to n + = n ( 1 + f ) flights: Design 1. choose random flights to be duplicated Experimental Results and 2. apply a small perturbation on departure time Analysis Simulations without conflict resolution ◮ same random seed used: consistent increase Simulations with conflict resolution Conclusions and Perspectives ◮ maintain a similar temporal distribution of flights
Ground-holding regulation: CASA Algorithm 10/28 Thibault Lehouillier , Jérémy Omer , François Soumis , Cyril Allignol Context and Motivations Simulation Computer Assisted Slot Allocation (CASA) Algorithms Experimental Design ◮ Allocates slots for take-off Experimental Results and ◮ Greedy heuristic (FIFO fashion) Analysis Simulations ◮ one delay value for each overflown regulated zone without conflict resolution Simulations with ◮ assigned delay: maximum delay over all overflown regulated conflict resolution Conclusions and zones Perspectives
Traffic simulation and conflict resolution 11/28 Thibault Lehouillier , Jérémy Omer , François Soumis , Cyril Allignol Context and Motivations Traffic simulator: Complete Air Traffic Simulator (CATS) Simulation ◮ time-discretized execution model Algorithms Experimental ◮ aircraft specifications and performances extracted from Design BADA tables Experimental Results and ◮ detailed outputs: traffic statistics, sector occupancy, conflicts Analysis Simulations data without conflict resolution Simulations with conflict resolution Conclusions and Perspectives
Traffic simulation and conflict resolution 11/28 Thibault Lehouillier , Jérémy Omer , François Soumis , Cyril Allignol Context and Motivations Traffic simulator: Complete Air Traffic Simulator (CATS) Simulation ◮ time-discretized execution model Algorithms Experimental ◮ aircraft specifications and performances extracted from Design BADA tables Experimental Results and ◮ detailed outputs: traffic statistics, sector occupancy, conflicts Analysis Simulations data without conflict resolution Simulations with conflict resolution Conclusions and Perspectives Air conflict resolution used: ◮ genetic algorithm from Durand(1996)[4] ◮ embedded in CATS
Plan 12/28 Thibault Lehouillier , Jérémy Omer , François Soumis , Cyril Allignol Context and Context and Motivations Motivations Simulation Algorithms Experimental Simulation Algorithms Design Experimental Results and Analysis Experimental Design Simulations without conflict resolution Simulations with conflict resolution Conclusions and Experimental Results and Analysis Perspectives Conclusions and Perspectives
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