On the Possibilities of Efficient Air Traffic Monitoring through Complex Network Clustering Based Airspace Sub-Sectorization: A Multi-Objective Discrete Particle Swarm Optimization Approach

This study models the airspace sub-sectorization problem as a multi-objective complex network clustering problem. A decomposition-based discrete particle swarm optimization (DPSO) algorithm is then used to solve the problem, followed by applying the minimum bounding geometry method to design convex and compact boundaries. An Indian airspace sector was considered to validate the proposed framework. The waypoints and routes within the sector were represented as a network graph, and discretized traffic loads were randomly allotted to the vertices to guide the DPSO. The maximum number of generations or iterations was set as the termination criteria. The proposed approach generates clusters that result in all sub-sectors having a medium traffic load, ensuring equity that is difficult to achieve. This framework offers enough flexibility to avoid several strict constraints, thereby reducing the problem's complexity. Moreover, the proposed framework improves the adaptability of sub-sectors to network evolution and traffic conditions, recognizing the hierarchical characteristics of air transport networks. The present research also motivates several research opportunities and possibilities for future air traffic management systems.