A modeling framework to strengthen the discrimination of unnecessary air traffic ground regulations by assessing overloaded sector dynamic behavior

Air Traffic Flow and Capacity Management (ATFCM) aims to balance traffic demand and sector capacity while maintaining safety and network performance. When tactical measures such as rerouting or sector reconfiguration are insufficient to meet demand, ground regulations are imposed as a last resort, generating delays. As an alternative capacity-on-demand mechanism, Clusterized Aircraft Early Handover (CAEHO) enables the early transfer of clustered aircraft near sector boundaries to adjacent sectors with available capacity. This paper introduces a modeling framework to support tactical CAEHO deployment decisions in overload situations where small variations in traffic distribution can lead to markedly different operational outcomes. A macroscopic model describes the spatiotemporal evolution of aircraft density within a sector, while a nonlinear eligibility formulation characterizes aircraft clustering through a power-law representation governed by the exponent α . This exponent reflects the nonlinear relationship between traffic organization and early handover feasibility and is driven by the internal spatial distribution of traffic. Sector-level characteristics such as baseline occupancy (offset), mean dwell time, and peak load are used to approximate its value, while uncertainty in planned traffic induces a range of possible α estimates. The proposed framework is validated using synthetic scenarios and real-traffic-driven simulations over the European airspace. The results show that the exponent α exhibits consistent discriminative behavior with respect to CAEHO feasibility under realistic sector overload conditions, particularly under limited remaining dwell time. The resulting feasibility indicator provides sector-level decision support to assess whether CAEHO can safely avoid an Air Traffic Controller–capacity regulation during the tactical phase.