STATISTICAL META-MODEL FOR AIR TRAFFIC FLOW AND CAPACITY MANAGEMENT BASED ON AIRSPACE OPTIMIZATION-SIMULATION: THE CONTINUOUS CHALLENGE OF THE HUB OF THE AMERICA CONGESTION

Abstract

Panama is only Country on the Americas with a Canal and the hub of Logistics that include the interaction of the Atlantic and the Pacific in a less than a day. This Logistics growth after the Panama Canal Expansion resulted in an overwhelming growth in Aviation. Furthermore, the economic and Logistic growth at Panama is increasing the demand of air transportation and it is creating potential for Air Logistics. Thus, the air traffic congestion is one of the greatest challenges that the Aviation Industry is seeking to address. The objective of this research is to understand if the Air Traffic Congestion in Panama can be reduced through minimizing the impact of historical congestion variables. In order to meet this objective, three specific criteria are investigated as follows: • Specific Objective #1: Determine which variables are most relevant to minimize Air Traffic Congestion. • Specific Objective #2: Determine the significance of the variables and their impact on the Air Traffic Congestion. • Specific Objective #3: Identify the cost effectiveness of the variables on Air Traffic Congestion. A statistical Meta-Model that includes Cause and Effect Analysis, Design and Analysis of Computer Experiments, Linear Regression, Mixed Integer Linear Programming, and Engineering Economics is used to address Air Traffic Flow and Capacity Management Uncertainty over the Congestion at the Airspace in Panama.