Airports represent the major bottleneck in the air traffic management system with increasing traffic density. Enhanced levels of automation and coordination of surface operations are imperative to reduce congestion and to improve efficiency. This paper addresses the problem of comparing different control strategies on the airport surface to investigate their impacts and benefits. We propose an optimization approach to solve in a unified manner the coordinated surface operations problem on network models of an actual hub airport. Controlled pushback time, taxi reroutes and controlled holding time (waiting time at runway threshold for departures and time spent in runway crossing queues for arrivals) are considered as decisions to optimize the ground movement problem. Three major aspects are discussed:1) benefits of incorporating taxi reroutes on the airport performance metrics; 2) priority of arrivals and departures in runway crossings; 3) tradeoffs between controlled pushback and controlled holding time for departures. A preliminary study case is conducted in a model based on operations of Paris Charles De-Gaulle airport under the most frequently used configuration. Airport is modeled using a node-link network structure. Alternate taxi routes are constructed based on surface surveillance records with respect to current procedural factors. A representative peak-hour traffic scenario is generated using historical data. The effectiveness of the proposed optimization methods is investigated.
MULTIFILE
Airports and surrounding airspaces are limited in terms of capacity and represent the major bottleneck in the air traffic management system. This paper proposes a two level model to tackle the integrated optimization problem of arrival, departure, and surface operations. The macroscopic level considers the terminal airspace management for arrivals and departures and airport capacity management, while the microscopic level optimizes surface operations and departure runway scheduling. An adapted simulated annealing heuristic combined with a time decomposition approach is proposed to solve the corresponding problem. Computational experiments performed on real-world case studies of Paris Charles De-Gaulle airport, show the benefits of this integrated approach.
Predictive models and decision support toolsallow information sharing, common situational awarenessand real-time collaborative decision-making betweenairports and ground transport stakeholders. To supportthis general goal, IMHOTEP has developed a set of modelsable to anticipate the evolution of an airport’s passengerflows within the day of operations. This is to assess theoperational impact of different management measures onthe airport processes and the ground transport system. Twomodels covering the passenger flows inside the terminal andof passengers accessing and egressing the airport have beenintegrated to provide a holistic view of the passengerjourney from door-to-gate and vice versa.This paper describes IMHOTEP’s application at two casestudy airports, Palma de Mallorca (PMI) and London City(LCY), at Proof of Concept (PoC-level) assessing impactand service improvements for passengers, airport operatorsand other key stakeholders.For the first time onemeasurable process is created to open up opportunities forbetter communication across all associated stakeholders.Ultimately the successful implementation will lead to areduction of the carbon footprint of the passenger journeyby better use of existing facilities and surface transportservices, and the delay or omission of additional airportfacility capacities.
