Predicting and planning airport acceptance rates in metroplex systems for improved traffic flow management decision support |
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| Institution: | 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;1. Ecole Nationale de l’Aviation Civile, University of Toulouse, 7 Avenue Edouard Belin, Toulouse 31055, France;2. Mathematical Institute of Toulouse, University of Toulouse, 118 Route de Narbonne, Toulouse 31062, France;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, Jiangjun Road # 29, Nanjing 211106, China;2. College of General Aviation and Flight, Nanjing University of Aeronautics and Astronautics, Jiangjun Road # 29, Nanjing 211106, China |
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| Abstract: | Efficient planning of Airport Acceptance Rates (AARs) is key for the overall efficiency of Traffic Management Initiatives such as Ground Delay Programs (GDPs). Yet, precisely estimating future flow rates is a challenge for traffic managers during daily operations as capacity depends on a number of factors/decisions with very dynamic and uncertain profiles. This paper presents a data-driven framework for AAR prediction and planning towards improved traffic flow management decision support. A unique feature of this framework is to account for operational interdependency aspects that exist in metroplex systems and affect throughput performance. Gaussian Process regression is used to create an airport capacity prediction model capable of translating weather and metroplex configuration forecasts into probabilistic arrival capacity forecasts for strategic time horizons. To process the capacity forecasts and assist the design of traffic flow management strategies, an optimization model for capacity allocation is developed. The proposed models are found to outperform currently used methods in predicting throughput performance at the New York airports. Moreover, when used to prescribe optimal AARs in GDPs, an overall delay reduction of up to 9.7% is achieved. The results also reveal that incorporating robustness in the design of the traffic flow management plan can contribute to decrease delay costs while increasing predictability. |
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| Keywords: | Traffic flow management Capacity management Multi-airport systems Machine learning |
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