Spatially-Temporal Interdependencies for the Aerial Ecosystem Identification

Marko Radanovic*, Miquel Angel Piera Eroles

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

2 Citations (Scopus)


Present research in Air Traffic Management (ATM) is going towards improvement of airspace capacity, accessibility and efficiency while reducing the management costs and increasing the safety performance indicators. A 4D contract between an Airspace User (AU) and Air Traffic Control, in which aircraft should be located at a given time on a particular waypoint, opens a wide scope of applications for decision support tools (DSTs). This paper introduces a new modeling approach for a smooth safety nets transition within the high en-route airspace operations. The approach is based on a causal state space search as a response to some shortages in the collision avoidance events, resulted from a limited logic of Traffic alert and Collision Avoidance System (TCAS). It considers Enhanced TCAS (E-TCAS) that relies on an extended time horizon at the separation management level to define functionalities that will provide the most optimal resolution trajectories and remove the deadlock scenarios.

Original languageEnglish
Title of host publicationSpatially-Temporal Interdependencies for the Aerial Ecosystem Identification
Number of pages8
Publication statusPublished - 1 Dec 2016

Publication series

NameProcedia Computer Science
ISSN (Print)1877-0509

Bibliographical note

Publisher Copyright:
© 2017 The Authors.

Copyright 2017 Elsevier B.V., All rights reserved.


  • Deadlock
  • Decision support tools
  • Ecosystem
  • Intersection point
  • Look-ahead time
  • State space analysis


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