# Air Traffic Simulation Framework for Testing Automated Air Traffic Control Solutions +
## 2 Proposed Architecture Framework
### Operational Modes
#### 2.3.2 Traffic Estimation Mode  
In certain use cases, only a simplified prediction of future traffic is required. For example, sectorization decisions are often based on short-term forecasts--typically over a 20 min horizon. In such scenarios, the precise position estimates of individual aircraft are less critical than broader traffic metrics, such as the predicted sector load. Consequently, traffic prediction outputs should be available for a user-defined airspace and timeframe, serving as input for a range of downstream applications. Users configure this model through the following parameters:  
* Spatial boundaries: defined by latitude and longitude limits;
* Prediction horizon (\(t_{E}\)): the duration for which traffic is to be estimated;
* Time resolution (\(t_{R}\)): the granularity prediction output.  
Figure 2 illustrates a single iteration of the traffic estimation process. The variables on the figure are listed below:  
* \(t_{0}\): start time of the simulation;
* \(t_{R}\): API response time or loading time;
* \(t_{E}\): estimation timeframe;
* \(R_{i}\): dataset containing artificially generated (randomized) traffic;
* \(D_{i}\): dataset containing the non-final traffic situation;
* \(S_{i}\): dataset containing the predicted traffic situation;
* \(i\): index of the current cycle;
* _est_: traffic estimation function (Section 3.1).  
Traffic input data may originate from either live API sources or stored historical datasets. Additionally, artificially generated (randomized) traffic may be included to simulate higher traffic volumes, although this feature is not implemented in the current version of the simulation environment. As indicated by the dashed arrow, the data collection functionality described in Section 2.3.1 can be activated. At the beginning of each iteration(cycle \(i\)), raw traffic data are loaded and processed into a structured form, denoted as \(D\), which serves as the foundation for all subsequent data manipulations. To simulate the increased traffic densities, the structure \(D\) can be augmented with artificially generated data defined in a separate structure \(R_{i}\), parameterized by the upper simulation layer. The structure \(D\) is then modified according to the estimation algorithm outlined in Section 3.1. In practice, this involves appending predicted aircraft positions to \(D\). The result is a traffic snapshot for cycle \(i\), denoted as \(S_{i}\), which reflects the estimated positions of all aircraft at a specific point in time. The timing of each cycle is determined by the user-defined estimation parameters, and the timestamp of snapshot \(S_{i}\) is calculated as \(t(S_{i})=t_{0}+t_{E}+i\cdot t_{R}\), where \(t_{0}\) is the simulation start time. The process then proceeds to the next iteration, following the methodology described in Section 3.2.  
The upper layer receives the predicted traffic information through an interface described in Section 4. In addition to the spatial traffic representation, the simulation can also provide supplementary traffic metrics, such as the number of aircraft within a sector or the number of potential conflicts. These outputs enable a range of practical applications at the upper layer, including support for tactical flow management, workload estimation for ATCOs, and the evaluation and comparison of different sectorization strategies.