In this paper, we present a framework for composing discrete-event simulation models from a large amount of airspace traffic data without using any specific waypoints. The framework consists of two parts. In the first part, abstracted route graphs that indicate representative routes in the airspace are composed. We propose two methods for extracting important routes in the form of graphs based on combination of various technologies such as space partition, trajectory clustering, and skeleton extraction. In the second part, discrete-event simulation models are composed based on statistical information on flight time along each edge of the abstracted route graph. The composed simulation models have intermediate granularity between micro models, such as multi-agent simulation, and macro models, such as queuing models, and therefore they should be classified as mesoscopic models. Finally, we show numerical results to evaluate the accuracy of the simulation model.

In 2015, the Ministry of Land, Infrastructure and Transportation started to provide information on aircraft flying over Japan, called CARATS Open Data, and to promote research on aviation systems actively. The airspace is divided into sectors, which are used for limiting air traffic to control safely and efficiently. Since the demand for air transportation is increasing, new optimization techniques and efficient control have been required to predict and resolve demand-capacity imbalances in the airspace. In this paper, we aim to construct mathematical models of the inter-sector air traffic flow from CARATS Open Data. In addition, we develop methods to predict future sector demand. Accuracy of the prediction is evaluated by comparison between predicted sector demand and the actual data.

Sectors in the airspace are units of the air traffic control. For airspace traffic data consists of the location of each aircraft with timestamp, we propose an efficient method to identify the sector where each aircraft lies.