# Air Traffic Simulation Framework for Testing Automated Air Traffic Control Solutions +
###### Abstract  
As air traffic control (ATC) automation advances, simulation environments become essential for testing and validating novel solutions before deployment. This study presents a modular framework that integrates real air traffic data to simulate controlled and uncontrolled airspace environments for automation assessment. The framework consists of a two-layer structure: a traffic simulation layer for generating and updating aircraft positions, and an upper layer for managing control agents and traffic commands. It uses ADS-B data to simulate realistic conditions, incorporates randomized traffic generation, and enables pilot-controller interactions. The system supports various operational modes, from simple data recording to fully interactive control scenarios. Interfaces allow external algorithm integration for traffic prediction, conflict resolution, and controller workload evaluation. A case study demonstrates the framework's ability to assess a basic control algorithm's performance under increasing traffic density. This open-source, MATLAB-based simulation environment supports robust, repeatable ATC automation testing using real-time or recorded traffic data. Its flexible architecture and clearly defined interfaces enable customization for diverse research applications, including sectorization studies, flow management, and workload estimation.  
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Footnote †: _Appl. Sci._**2025**, 15, 6414  
aim to reduce costs and improve capacity--through, for example, optimized information sharing [4] or implementing of Deep Learning technologies [5]--as the use of AI is one of the most actively researched areas in ATM [6]. Despite these advancements, enhancing safety remains one of the key goals.  
Although automation solutions increasingly address various ATC functions, human operations continue to play a key role. For instance, the AUTOPACE project assessed hazard and risk in two future ATC scenarios with varying levels of automation [7]. As ATC automation evolves, researchers, industry stakeholders, and policymakers require robust simulation environments to evaluate the implications of new technologies, regulations, and procedures. Section 1.2 provides an overview of simulation tools designed specifically for ATM applications.  
Realistic and flexible simulation frameworks rely heavily on the use of real-world trajectory data, which is a key point not only in the air traffic domain, but many other domains of the transportation sector, so here we give a few examples of valuable research. [PERSON] et al. [8] successfully applied high-resolution data to study lane-changing behavior in ground traffic systems, identifying key trajectory points based on lateral displacement thresholds. This method offers valuable insights that can be adapted to the modeling of air traffic flows, where capturing dynamic movement is equally important.  
A different approach comes from the maritime domain, where [PERSON] et al. [9] introduced an A*-guided Double Deep Q-Network. By combining heuristic path planning with reinforcement learning, their model was able to identify optimal routes under changing environmental conditions. Building on the idea of intelligent learning, [PERSON] et al. [10] developed a sample prioritization strategy for supervised learning, helping the model focus on the most informative data to improve classification accuracy.  
In a complementary direction, [PERSON] and [PERSON] [11] applied a Bayesian Structural Time Series model to better understand how system dynamics evolve over time. Their work highlights the value of probabilistic modeling in uncovering structural changes and temporal patterns. Taken together, these studies demonstrate the potential of combining data-driven learning with domain-specific insights to improve the performance and adaptability of transportation models.