The recent surge in space launch activities, driven by the emergence of commercial space launches, has compelled the aviation and space launch sectors to collaborate for the safe and efficient integration of space launch activities. This paper introduces an agent-based modeling (ABM) and simulation framework designed to assess the impact of spacecraft launches on air traffic within an integrated air and space traffic management system. The proposed framework incorporates various agents involved in the execution phase of space launches and considers the interactions and coordination between air traffic management and space traffic management. The paper firstly provides a comprehensive overview of the current state of space launch operations and their effects. Then, a general agent-based model is developed for space launch execution phase in order to gain an understanding of various entities involved in a space launch activity as well as the interactions among these entities. Using Monte-Carlo simulations based on the ABM, the paper assesses the impact on air traffic operations in the event of a space launch failure. In each simulation, various factors are taken into account, including launch site position, launch slot, failure probability during the execution phase, debris dispersion, and time delay in Air Traffic Management (ATM)/Space Traffic Management (STM) coordination. To demonstrate the practical application of the proposed framework in an operational context, the paper presents a case study of a sea-based space launch in the Singapore FIR. The paper makes a valuable contribution to the field of air and space traffic management by addressing the need for innovative strategies to ensure the safe sharing ofairspace among different stakeholders.

Safety is a top priority for civil aviation. Data mining in digital Flight Data Recorder (FDR) or Quick Access Recorder (QAR) data, commonly referred to as black box data on aircraft, has gained interest for proactive safety management. New anomaly detection methods, primarily clustering methods, have been developed to monitor pilot operations and detect any risks from such flight data. However, all existing anomaly detection methods are offline learning — the models are trained once using historical data and used for all future predictions. In practice, new flight data are accumulated continuously and analyzed every month at airlines. Clustering such dynamically growing data is challenging for an offline method because it is memory and time intensive to re-train the model every time new data come in. If the model is not re-trained, false alarms or missed detections may increase since the model cannot reflect changes in data patterns. To address this problem, we propose a novel incremental anomaly detection method based on Gaussian Mixture Model (GMM) to identify common patterns and detect outliers in flight operations from digital flight data. It is a probabilistic clustering model of flight operations that can incrementally update its clusters based on new data rather than to re-cluster all data from scratch. It trains an initial GMM model based on historical offline data. Then, it continuously adapts to new incoming data points via an expectation–maximization (EM) algorithm. To track changes in flight operation patterns, only model parameters need to be saved, not the raw flight data. The proposed method was tested on three sets of simulation data and two sets of real-world flight data. Compared with the traditional offline GMM method, the proposed method can generate similar clustering results with significantly reduced processing time (57 %–99 % time reduction in testing sets) and memory usage (91 %–95 % memory usage reduction in testing sets). Preliminary results indicate that the incremental learning scheme is effective in dealing with dynamically growing data in flight data analytics.