Metaheuristics for Air Traffic Management E-Book

Nicolas Durand

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 313

Veröffentlichungsjahr: 2015

Beschreibung

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

Leseprobe

Table of Contents

Cover

Title

Copyright

Introduction

1 The Context of Air Traffic Management

1.1. Introduction

1.2. Vocabulary and units

1.3. Missions and actors of the air traffic management system

1.4. Visual flight rules and instrumental flight rules

1.5. Airspace classes

1.6. Airspace organization and management

1.7. Traffic separation

1.8. Traffic regulation

1.9. Airspace management in en route air traffic control centers

1.10. Air traffic flow management

1.11. Research in air traffic management

2 Air Route Optimization

2.1. Introduction

2.2. 2D-route network

2.3. A network of separate 3D-tubes for the main traffic flows

2.4. Conclusion on air route optimization

3 Airspace Management

3.1. Airspace sector design

3.2. Functional airspace block definition

3.3. Prediction of air traffic control sector openings

4 Departure Slot Allocation

4.1. Introduction

4.2. Context and related works

4.3. Conflict-free slot allocation

4.4. Results

4.5. Concluding remarks

5 Airport Traffic Management

5.1. Introduction

5.2. Gate assignment

5.3. Runway scheduling

5.4. Surface routing

5.5. Global airport traffic optimization

5.6. Conclusion

6 Conflict Detection and Resolution

6.1. Introduction

6.2. Conflict resolution complexity

6.3. Free-flight approaches

6.4. Iterative approaches

6.5. Global approaches

6.6. A global approach using evolutionary computation

6.7. A global approach using ant colony optimization

6.8. A new framework for comparing approaches

6.9. Conclusion

Conclusion

Bibliography

Index

End User License Agreement

Guide

Cover

Table of Contents

Begin Reading

List of Figures

1 The Context of Air Traffic Management

Figure 1.1.

Air traffic forecast in

Europe.

Figure 1.2.

FIRs in Europe

Figure 1.3.

Functional airspace blocks in Europe. For a color version of the figure, see

www.iste.co.uk/durand/atm.zip

Figure 1.4.

Top and sectional view of Paris TMA (2011). For a color version of the figure, see

www.iste.co.uk/durand/atm.zip

Figure 1.5.

En route, approach and airport control. For a color version of the figure, see

www.iste.co.uk/durand/atm.zip

Figure 1.6.

Routes and airspace sectors in Europe (2009), in the upper airspace. For a color version of the figure, see

www.iste.co.uk/durand/atm.zip

Figure 1.7.

Aircraft conflict

Figure 1.8.

An example of 4-aircraft conflict (cluster)

Figure 1.9.

Intuitive approach of ATC complexity

Figure 1.10.

Factors impacting the air traffic controller workload

.

Figure 1.11.

Example of an HMI displaying sector opening schemes. For a color version of the figure, see

www.iste.co.uk/durand/atm.zip

Figure 1.12.

“Monitoring values” for the “occupancy count” metric

Figure 1.13.

Eurocontrol CHMI/CIFLO interface for FMPs

2 Air Route Optimization

Figure 2.1.

Crossing points of the direct routes of traffic flows above 10 flights per day. For a color version of the figure, see

www.iste.co.uk/durand/atm.zip

Figure 2.2.

Clustering process applied to crossing points. For a color version of the figure, see

www.iste.co.uk/durand/atm.zip

Figure 2.3.

Voronoï diagram applied to the clusters barycenters. For a color version of the figure, see

www.iste.co.uk/durand/atm.zip

Figure 2.4.

Delaunay triangulation of the clusters barycenters. For a color version of the figure, see

www.iste.co.uk/durand/atm.zip

Figure 2.5.

Air route network found by a simulated annealing (right), starting from an initial regular grid (left)

Figure 2.6.

A simplified model of 3D-trajectory

Figure 2.7.

Possible lateral (left) or vertical deviations (right)

Figure 2.8.

Example of area used to compute the transition cost

Figure 2.9.

Vertical profile with smallest deviation cost between d and arrival, and area used to compute the heuristic