Data-Based Modeling and Optimization of En Route Traffic

Abstract

Air traffic management aims at ensuring safe and efficient movement of aircraft in the airspace. With the predicted growth of air transportation, providing traffic flow managers with the tools to support decision-making is essential. These tools should aid in accommodating the air traffic throughput increase while limiting controller workload and ensuring high safety levels. The objective of this paper is to present a methodology to model and simulate traffic in a given portion of the airspace from data under nominal and perturbed conditions. A new framework for en route traffic flow management and airspace health monitoring is developed. It is based on a data-driven approach for air traffic flow modeling using historical data. This large-scale three-dimensional flow network provides valuable insight on airspace complexity. A linear programming formulation for optimizing en route air traffic is proposed. It takes into account a controller task load model based on flow geometry, in order to estimate airspace capacity. To analyze airspace degradation, weather blockage maps based on vertically integrated liquid are incorporated in the model, representing weather perturbations on the same dataset used to compute the flows. Comparing the weather blockages and the network model of the airspace provides means of quantifying airspace degradation. The impact of the perturbations is then examined. The results of the simulations are compared with the data from these specific days, to identify the advantages and drawbacks of the present model. The methodology presented is scalable and adaptable to various types of airspaces, traffic loads, and uncertainty levels. This work is contributing to a better data-based understanding of traffic congestion, rerouting options depending on origin and destination pairs, how traffic patterns influence the controller task load, and how the size and location of weather cells impact air traffic operations.