User-Oriented Traffic Flow Planning for the Next Generation Air Transport System

In view of current projections that the demand for air transport will double if not triple in the next 20 years, and recognizing that the current systems both in Europe and the USA are already pushing the limits of available capacity, we recognize an urgent need to review ATM operations in the near to mid term future. The Federal Aviation Administration in its Operational Evolution Partnership (OEP) stresses the need for collaborative flow planning based on a philosophy to support users own operating preferences with restriction imposed only when a real operational need exists to meet the foreseen demand. One of the key objectives of the Next Generation Air Transportation System (NextGen) is to try to ensure that flight operator objectives are best balanced against the National Airspace System (NAS) performance. To support a user-oriented flow planning capability it is imperative to incorporate technological and innovative planning solutions that increase the available capacity to meet future demand, whilst maintaining and improving safety, enhancing efficiency and offering flexibility to the airline operators. Moreover, all of this should be achieved to ensure the equitable consideration of multiple stakeholder needs in this complex dynamic system. This paper presents our research into the design of a scalable enterprise framework for multiple-stakeholder and multiple-objective model-based planning and optimization in support of future Air Traffic Flow Management. The approach is based on an intelligent evaluation and optimization at the strategic and flight route levels. Our approach not only considers system-level objectives, but also regards the impact of decisions on the principal stakeholders within the US National Airspace System and proposes a novel approach to concerns about equitable solutions.

[1]  Marc Schoenauer,et al.  Dynamic air traffic planning by genetic algorithms , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[2]  George Hunter,et al.  Modeling and Performance of NAS in Inclement Weather , 2006 .

[3]  Jean-Marc Alliot,et al.  Optimization of air traffic control sector configurations using tree search methods and genetic algorithms , 2002, Proceedings. The 21st Digital Avionics Systems Conference.

[4]  Tejal Topiwala,et al.  ARTCC-Initiated Rerouting , 2006 .

[5]  Daniel Delahaye,et al.  Airspace congestion smoothing by multi-objective genetic algorithm , 2005, SAC '05.

[6]  James DeArmon,et al.  The Miles-in-Trail Impact Assessment Capability , 2006 .

[7]  Anthony J. Masalonis,et al.  DYNAMIC DENSITY AND COMPLEXITY METRICS FOR REALTIME TRAFFIC FLOW MANAGEMENT , 2003 .

[8]  Craig Wanke,et al.  A Genetic Algorithm Approach to Probabilistic Airspace Congestion Management , 2006 .

[9]  Dimitris Bertsimas,et al.  The Air Traffic Flow Management Problem with Enroute Capacities , 1998, Oper. Res..

[10]  Piero P. Bonissone,et al.  Evolutionary algorithms + domain knowledge = real-world evolutionary computation , 2006, IEEE Transactions on Evolutionary Computation.

[11]  L. El Ghaoui,et al.  Algorithms for air traffic flow management under stochastic environments , 2004, Proceedings of the 2004 American Control Conference.

[12]  Alexandre d'Aspremont,et al.  A Semidefinite Relaxation for Air Traffic Flow Scheduling , 2007, 2007 IEEE International Conference on Research, Innovation and Vision for the Future.

[13]  Alexandre M. Bayen,et al.  A Control Theoretic Predictive Model for Sector-Based Air Traffic Flow , 2002 .

[14]  J. Krozel,et al.  AGGREGATE STATISTICS OF THE NATIONAL AIRSPACE SYSTEM , 2003 .

[15]  P. K. Menon,et al.  Air traffic control using genetic search techniques , 1999, Proceedings of the 1999 IEEE International Conference on Control Applications (Cat. No.99CH36328).

[16]  Charles-Edmond Bichot,et al.  A tool to design Functional Airspace Blocks , 2007 .

[17]  N. Iyer,et al.  MONACO - Multi-Objective National Airspace Collaborative Optimization , 2007, 2007 IEEE Aerospace Conference.

[18]  Banavar Sridhar,et al.  Airspace Complexity and its Application in Air Traffic Management , 1998 .

[19]  Avijit Mukherjee.  Dynamic Stochastic Optimization Models for Air Traffic Flow Management , 2004 .

[20]  Nicolas Durand,et al.  Genetic algorithms applied to airport ground traffic optimization , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[21]  Alexandre M. Bayen,et al.  Lagrangian Delay Predictive Model for Sector-Based Air Traffic Flow , 2005 .

[22]  Richard J. Ormerod,et al.  Optimisation models for re-routing air traffic flows in Europe , 2001, J. Oper. Res. Soc..

[23]  Jimmy Krozel,et al.  Strategic Traffic Flow Management Concept of Operations , 2004 .

[24]  S. D. Hill,et al.  Simulation optimization of airline delay with constraints , 2001, Proceeding of the 2001 Winter Simulation Conference (Cat. No.01CH37304).

[25]  Ian Crook,et al.  The Common ATM Information State Space - A unified data management system for the entire NAS resources based on an indexing system , 2001 .