Dynamic Capacity Management for Air Traffic Operations in High Density Constrained Urban Airspace

Journal Article (2023)
Author(s)

Niki Patrinopoulou (University of Patras)

I. Daramouskas (University of Patras)

Calin Andrei Badea (Student TU Delft)

A. Morfin Veytia (TU Delft - Control & Simulation)

Vaios Lappas (National and Capodistrian University of Athens)

Joost Ellerbroek (TU Delft - Control & Simulation)

Jacco M. Hoekstra (TU Delft - Control & Simulation)

Vassilios Kostopoulos (University of Patras)

Research Group
Control & Simulation
Copyright
© 2023 Niki Patrinopoulou, Ioannis Daramouskas, Calin Andrei Badea, A. Morfin Veytia, Vaios Lappas, Joost Ellerbroek, J.M. Hoekstra, Vassilios Kostopoulos
DOI related publication
https://doi.org/10.3390/drones7060395
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 Niki Patrinopoulou, Ioannis Daramouskas, Calin Andrei Badea, A. Morfin Veytia, Vaios Lappas, Joost Ellerbroek, J.M. Hoekstra, Vassilios Kostopoulos
Research Group
Control & Simulation
Issue number
6
Volume number
7
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Abstract

Unmanned Aircraft Systems (UAS) Traffic Management (UTM) is an active research subject as its proposed applications are increasing. UTM aims to enable a variety of UAS operations, including package delivery, infrastructure inspection, and emergency missions. That creates the need for extensive research on how to incorporate such traffic, as conventional methods and operations used in Air Traffic Management (ATM) are not suitable for constrained urban airspace. This paper proposes and compares several traffic capacity balancing methods developed for a UTM system designed to be used in highly dense, very low-level urban airspace. Three types of location-based dynamic traffic capacity management techniques are tested: street-based, grid-based, and cluster-based. The proposed systems are tested by simulating traffic within mixed (constrained and open) urban airspace based on the city of Vienna at five different traffic densities. Results show that using local, area-based clustering for capacity balancing within a UTM system improves safety, efficiency, and capacity metrics, especially when simulated or historical traffic data are used.