A discrete-event modeling approach for the analysis of TCAS-induced collisions with different pilot response times

Abstract

The traffic alert and collision avoidance system (TCAS) currently mandated worldwide on all commercial transport aircraft is intended to provide last-minute collision avoidance (CA) guidance directly to the flight crew and has been shown to significantly reduce the risk of near-midair collisions. The TCAS logic uses a deterministic model to predict the future trajectories of the aircraft and does not explicitly represent variability in pilot response time which can have a great impact on the execution of the CA logic. Prior work has designed an encounter model to identify all the induced potential collision scenarios that are representative of possible hazardous situations that may occur with a fixed configuration of aircraft in the surrounding airspace. This paper extends the encounter model using an agent-based modeling approach developed via the colored Petri net (CPN) formalism to include the agent pilot response time that captures the variability delay in pilot behavior in order to analyze its influence on TCAS-induced collisions. Quantitative simulation results are conducted to validate the proposed causal model, dealing with challenging results about the extra airside capacity that could be obtained by offering a specific training on TCAS to the pilots or by use of automatisms, which is the case of remotely piloted aircraft systems.