# Air Traffic Simulation Framework for Testing Automated Air Traffic Control Solutions +
### Identified Gaps in Current Simulation Frameworks  
Our review of the literature has highlighted several important gaps in existing air traffic simulation frameworks:  
* Integrating Traffic Simulation with Real-World Traffic Data: Although many simulation environments can generate detailed outputs such as four-dimensional trajectories, integrating these simulations with real-world traffic data remains a challenge. A key issue lies in the reliance on flight plan data (such as ELSA, using flight plan inputs), which is subject to licensing restrictions. These limitations reduce accessibility and hinder real-time applicability. Moreover, using historical trajectory data as input is problematic because such trajectories typically reflect air traffic control interventions already made during those flights. As a result, they do not represent the unaltered,  
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nominal flight paths, making them unsuitable for simulations that aim to model or test the impact of control actions.
* Lack of Well-Defined Interfaces for Algorithm Integration: Many simulation environments offer limited flexibility for development and algorithm modification. Even when the source code is publicly available, these platforms may lack clearly defined interfaces for incorporating custom algorithms or adding new functional modules--such as the case for ELSA and BlueSky, where algorithm modification would require source code modification.
* Limited Support for Programmable Controller Actions: In most existing systems, controller actions can be input manually via a human-machine interface or programmed through external languages (e.g., TrafScript in BlueSky or Auto-lead Data Format in CASSIOPEIA). However, native support for modifying flight data within the simulation environment itself is lacking. This limitation reduces the flexibility and the ability to seamlessly implement custom ATC algorithms.  
To address these limitations the simulation environment proposed in this study is designed as an open-source framework comprising modular functions that support customizable applications. By leveraging open-access data interfaces, the framework significantly simplifies data acquisition and integration for end users. It uses ADS-B data as its primary traffic input, ensuring access to both historical and real-time air traffic information. This data is used to initialize the positions of all aircraft. However, within the designated control area real-world ADS-B trajectories are not used directly, as they typically reflect aircraft already under ATC guidance. Instead, aircraft positions in the controlled airspace are estimated and updated through the simulation framework, based on controller interactions. This approach ensures that the impact of automated ATC algorithms can be evaluated without being constrained by previously executed control decisions in the recorded data. This makes the environment particularly suitable for validating ATC automation solutions. Within the framework, traffic data can be manipulated using the simulation's native language through well-defined interfaces. This allows for the flexible implementation and validation of automated controller algorithms and supports robust testing of automation strategies under realistic traffic scenarios.  
The remainder of this article is structured as follows: Section 2 presents the overall architecture of the simulation environment and the operational modes. Section 3 describes the traffic prediction components, including both the microscopic position estimation and the macroscopic area-based update mechanism. Section 4 details the interface structures. Section 5 discusses the simulation's time management model in relation to real-time operations. Section 6 offers a case study demonstrating the performance of a sample controller algorithm. Finally, Section 7 summarizes the findings and outlines directions for future research.