Metaheuristics for Air Traffic Management

Inbunden, Engelska, 2015

AvNicolas Durand,David Gianazza,Jean-Baptiste Gotteland,Jean-Marc Alliot

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Air Traffic Management involves many different services such as Airspace Management, Air Traffic Flow Management and Air Traffic Control. Many optimization problems arise from these topics and they generally involve different kinds of variables, constraints, uncertainties. Metaheuristics are often good candidates to solve these problems. The book models various complex Air Traffic Management problems such as airport taxiing, departure slot allocation, en route conflict resolution, airspace and route design. The authors detail the operational context and state of art for each problem. They introduce different approaches using metaheuristics to solve these problems and when possible, compare their performances to existing approaches

Produktinformation

Utgivningsdatum2015-12-15
Mått163 x 241 x 18 mm
Vikt476 g
FormatInbunden
SpråkEngelska
Antal sidor216
FörlagISTE Ltd and John Wiley & Sons Inc
ISBN9781848218109

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Introduction ixChapter 1. The Context of Air Traffic Management 11.1. Introduction 11.2. Vocabulary and units 21.3. Missions and actors of the air traffic management system 31.4. Visual flight rules and instrumental flight rules 41.5. Airspace classes 41.6. Airspace organization and management 51.6.1. Flight information regions and functional airspace blocks 51.6.2. Lower and upper airspace 61.6.3. Controlled airspace: en route, approach or airport control 71.6.4. Air route network and airspace sectoring 71.7. Traffic separation 91.7.1. Separation standard, loss of separation 91.7.2. Conflict detection and resolution 111.7.3. The distribution of tasks among controllers 121.7.4. The controller tools 121.8. Traffic regulation 131.8.1. Capacity and demand 131.8.2. Workload and air traffic control complexity 151.9. Airspace management in en route air traffic control centers 161.9.1. Operating air traffic control sectors in real time 161.9.2. Anticipating sector openings (France and Europe) 171.10. Air traffic flow management 191.11. Research in air traffic management 201.11.1. The international context 201.11.2. Research topics 21Chapter 2. Air Route Optimization 232.1. Introduction 232.2. 2D-route network 242.2.1. Optimal positioning of nodes and edges using geometric algorithms 242.2.2. Node positioning, with fixed topology, using a simulated annealing or a particle swarm optimization algorithm 282.2.3. Defining 2D-corridors with a clustering method and a genetic algorithm 292.3. A network of separate 3D-tubes for the main traffic flows 312.3.1. A simplified 3D-trajectory model 312.3.2. Problem formulations and possible strategies 342.3.3. An A∗ algorithm for the “1 versus n” problem 352.3.4. A hybrid evolutionary algorithm for the global problem 412.3.5. Results on a toy problem, with the simplified 3D-trajectory model 502.3.6. Application to real data, using a more realistic 3D-tube model 572.4. Conclusion on air route optimization 66Chapter 3. Airspace Management 693.1. Airspace sector design 703.2. Functional airspace block definition 713.2.1. Simulated annealing algorithm 733.2.2. Ant colony algorithm 733.2.3. A fusion–fission method 733.2.4. Comparison of fusion–fission and classical graph partitioning methods 743.3. Prediction of air traffic control sector openings 743.3.1. Problem difficulty and possible approaches 783.3.2. Using a genetic algorithm 783.3.3. Tree-search methods, constraint programming 793.3.4. A neural network for workload prediction 803.3.5. Conclusion on the prediction of sector openings 83Chapter 4. Departure Slot Allocation 854.1. Introduction 854.2. Context and related works 864.2.1. Ground holding 864.3. Conflict-free slot allocation 874.3.1. Conflict detection 884.3.2. Sliding forecast time window 904.3.3. Evolutionary algorithm 914.4. Results 954.4.1. Evolution of the problem size 954.4.2. Numerical results 964.5. Concluding remarks 98Chapter 5. Airport Traffic Management 1015.1. Introduction 1015.1.1. Airports’ main challenges 1015.1.2. Known difficulties 1025.1.3. Optimization problems in airport traffic management 1035.2. Gate assignment 1035.2.1. Problem description 1035.2.2. Resolution methods 1045.3. Runway scheduling 1065.3.1. Problem description 1065.3.2. An example of problem formulation 1085.3.3. Resolution methods 1095.4. Surface routing 1115.4.1. Problem description 1115.4.2. Related work 1125.5. Global airport traffic optimization 1155.5.1. Problem description 1155.5.2. Coordination scheme between the different predictive systems 1165.5.3. Simulation results 1175.6. Conclusion 121Chapter 6. Conflict Detection and Resolution 1236.1. Introduction 1236.2. Conflict resolution complexity 1256.3. Free-flight approaches 1286.3.1. Reactive techniques 1296.3.2. Iterative approach 1296.3.3. An example of reactive approach: neural network trained by evolutionary algorithms 1306.3.4. A limit to autonomous approaches: the speed constraint 1376.4. Iterative approaches 1386.5. Global approaches 1386.6. A global approach using evolutionary computation 1406.6.1. Maneuver modeling 1406.6.2. Uncertainty modeling 1416.6.3. Real-time management 1426.6.4. Evolutionary algorithm implementation 1446.6.5. Alternative modeling 1516.6.6. One-day traffic statistics 1526.6.7. Introducing automation in the existing system 1536.7. A global approach using ant colony optimization 1556.7.1. Problem modeling 1556.7.2. Algorithm description 1566.7.3. Algorithm improvement: constraint relaxation 1596.7.4. Results 1606.7.5. Conclusion and further work 1606.8. A new framework for comparing approaches 1636.8.1. Introduction 1636.8.2. Trajectory prediction model 1636.8.3. Conflict detection 1686.8.4. Benchmark generation 1696.8.5. Conflict resolution 1706.9. Conclusion 177Conclusion 179Bibliography 181Index 193