Increasing Air Traffic: What is the Problem?
Areski Hadjaz, Ga\'etan Marceau (INRIA Saclay - Ile de France), Pierre, Sav\'eant (TRT), Marc Schoenauer (INRIA Saclay - Ile de France, MSR - INRIA)

TL;DR
This paper proposes an integrated framework for air traffic management that models trajectory uncertainties with Bayesian Networks, optimizes overflight timings to reduce congestion and delays, and continuously updates plans based on real-time data.
Contribution
It introduces a novel system combining Bayesian modeling, optimization, and real-time monitoring to improve air traffic control and management efficiency.
Findings
Bayesian Network effectively models trajectory uncertainties.
Optimization reduces sector congestion and delays.
Real-time updates improve traffic management accuracy.
Abstract
Nowadays, huge efforts are made to modernize the air traffic management systems to cope with uncertainty, complexity and sub-optimality. An answer is to enhance the information sharing between the stakeholders. This paper introduces a framework that bridges the gap between air traffic management and air traffic control on the one hand, and bridges the gap between the ground, the approach and the en-route centers on the other hand. An original system is presented, that has three essential components: the trajectory models, the optimization process, and the monitoring process. The uncertainty of the trajectory is modeled with a Bayesian Network, where the nodes are associated to two types of random variables: the time of overflight on metering points of the airspace, and the traveling time of the routes linking these points. The resulting Bayesian Network covers the complete airspace, and…
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Taxonomy
TopicsAir Traffic Management and Optimization · Aviation Industry Analysis and Trends · International Law and Aviation
