Robust identification of air traffic flow patterns in Metroplex terminal areas under demand uncertainty |
| |
| Institution: | 1. Transport and Mobility Laboratory (TRANSP-OR), School of Architecture, Civil and Environmental Engineering (ENAC), École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland;2. Econometric Institute, Erasmus University Rotterdam, P.O. Box 1738, 3000 DR Rotterdam, Netherlands;3. Faculty of Technology, Policy and Management, Delft University of Technology, 2628 BX Delft, Netherlands;1. Massachusetts Institute of Technology, 77 Massachusetts Avenue, Building E40-240, Cambridge, MA 02139, USA;2. Massachusetts Institute of Technology, 77 Massachusetts Avenue, Building 33-218, Cambridge, MA 02139, USA;1. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, PR China;2. National Key Laboratory of Air Traffic Flow Management, Nanjing 210016, PR China;3. Department of Civil and Environmental Engineering, Imperial College London, SW7 2BU, UK;1. Smart City College, Beijing Union University, China;2. School of Electronics and Information Engineering, Beihang University, China;3. School of Engineering and Information Technology, University of New South Wales, Australia |
| |
| Abstract: | Multi-Airport Systems (MAS), or Metroplexes, serve air traffic demand in cities with two or more airports. Due to the spatial proximity and operational interdependency of the airports, Metroplex airspaces are characterized by high complexity, and current system structures fail to provide satisfactory utilization of the available airspace resources. In order to support system-level design and management towards increased operational efficiency in such systems, an accurate depiction of major demand patterns is a prerequisite. This paper proposes a framework for the robust identification of significant air traffic flow patterns in Metroplex systems, which is aligned with the dynamic route service policy for the effective management of Metroplex operations. We first characterize deterministic demand through a spatio-temporal clustering algorithm that takes into account changes in the traffic flows over the planning horizon. Then, in order to handle uncertainties in the demand, a Distributionally Robust Optimization (DRO) approach is proposed, which takes into account demand variations and prediction errors in a robust way to ensure the reliability of the demand identification. The DRO-based approach is applied on pre-tactical (i.e. one-day planning) as well as operational levels (i.e. 2-h rolling horizon). The framework is applied to Time Based Flow Management (TBFM) data from the New York Metroplex. The framework and results are validated by Subject Matter Experts (SMEs). |
| |
| Keywords: | Multi-airport system Terminal area operation Air traffic demand Air traffic management Distributionally robust optimization |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
