A causal encounter model of traffic collision avoidance system operations for safety assessment and advisory optimization in high-density airspace

Jun Tang, Feng Zhu, Miquel Angel Piera

Research output: Contribution to journalArticleResearch

9 Citations (Scopus)

Abstract

© 2018 Elsevier Ltd The traffic collision avoidance system (TCAS) acts as a proverbially accepted last-resort means to resolve encounters effectively, while it also has been proven to potentially induce a collision in the hectic air traffic. Thus, new research considering the impact on safety is required to increase the airspace capacity based on a comprehensive analysis and accurate flight evaluation. In this paper, a causal encounter model is proposed to extend the TCAS logic considering the horizontal resolution manoeuvres, which could be used as the auxiliary supports when a potential collision is predicted in the vertical dimension. Based on the generated state space, the model developed in the graphical modelling and analysis software (GMAS), not only provides a better comprehension of the potential collision occurrences for risk assessment by representing the cause-effect relationship of each action, but also aids the pilots in the involved aircraft to make a cooperative and optimal option. Quantitative simulation results are conducted to validate the feasibility and effectiveness of the encounter model with horizontal resolution. The resulting collision scenarios are further investigated to illustrate that the risk rate of TCAS logic failures is expected to reduce by shortening the pilot's response delay, and the computational efficiency is competent in dealing with multi-threat scenarios.
Original languageEnglish
Pages (from-to)347-365
JournalTransportation Research Part C: Emerging Technologies
Volume96
DOIs
Publication statusPublished - 1 Nov 2018

Keywords

  • Encounter model
  • GMAS
  • Potential induced collisions
  • Safety assessment
  • TCAS

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