Subset Simulation Based Operational Risk Assessment of Procedures for Go-Around Handling Enabled by a Predictive Decision Support

This paper presents a method to assess a novel procedure for Air Traffic Controllers, enabled by predictions of a machine learning-based go-around prediction model, regarding the operational risk of separation infringements and traffic alarms. In a previous work, potentially novel procedures were elaborated in human-in-the-loop simulations with Air Traffic Controllers. However, only a very limited number of simulations were possible due to the limited availability of Air Traffic Controllers, especially at the early stage of development of the decision support concept. Therefore, the evaluation of the decision support tool covered only a limited part of the operational domain. To tackle these shortcomings, this paper presents a subset simulation-based approach, a Monte Carlo variant to efficiently estimate small probabilities, which allows for assessing the concept over a wider operational spectrum and quantifying the risk of separation infringement and traffic alarms. The subset simulation-based method confirms that if the go-around prediction model predicts a go-around, the novel procedure could increase separation distances and thereby avoid separation infringements compared to the state-of-the-art procedure.