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.
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Diagnosing teachers are teachers who perceive diagnostic information about students’ learning process, interpret these aspects, decide how to respond, and act based on this diagnostic decision. During supervision meetings about the undergraduate thesis supervisors make in-the-moment decisions while interacting with their students. We regarded research supervision as a teaching process for the supervisor and a learning process for the student. We tried to grasp supervisors’ in-the-moment decisions and students’ perceptions of supervisors’ actions. Supervisor decisions and student perceptions were measured with video-stimulated recall interviews and coded using a content analysis approach. The results showed that the in-the-moment decisions our supervisors made had a strong focus on student learning. Supervisors often asked questions to empower students or to increase student understanding. These supervising strategies seemed to be adapted to students’ needs, as the latter had positive perceptions when their control increased or when they received stimuli to think for themselves.
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