Terminal Control Area Capacity Estimation Model Incorporating Structural Space

TL;DR

This paper introduces a novel TMA capacity estimation model based on structural space, utilizing flight trajectory data to improve capacity assessment and operational planning in air traffic management.

Contribution

It develops a new capacity estimation model grounded in structural space, incorporating flight path length and traffic characteristics, specifically addressing TMA's unique features.

Findings

Estimated capacities: 9.3 and 6.9 aircraft for Jeju TMA.

Capacity influenced by aircraft speed and separation standards.

Operational measures can effectively enhance TMA capacity.

Abstract

The continuous growth in global air traffic demand highlights the need to accurately estimate airspace capacity for efficiently using limited resources in air traffic management (ATM) systems. Although previous studies focused on either sector capacity based on air traffic controllers (ATCo) workload or runway throughput, studies on the unique structural and functional characteristics of terminal control area (TMA) remain lacking. In this study, capacity is defined as the maximum occupancy count. Further, a TMA capacity estimation model grounded in structural space conceptually defined as the space formed by instrument flight procedures and traffic characteristics is developed. Capacity is estimated from the temporal flight distance, which represents the physical length of arrival paths converted to flight time, and the average time separation at the runway threshold considering traffic proportions and aircraft mix. The proposed model is applied to the Jeju International Airport TMA (RWY 07/25) using one year of ADS-B trajectory data. The estimated capacities are 9.3 (RWY 07) and 6.9 (RWY 25) aircraft, and the differences are attributed to the temporal flight distance. Sensitivity analysis shows that capacity is shaped by aircraft speed and air traffic control (ATC) separations, which implies that operational measures such as speed restrictions or adjusted separations effectively enhance capacity even within physically constrained TMA. The model offers a practical, transparent, and quantitative framework for TMA capacity assessment and operational design.