Metroplex identification, evaluation, and optimization

Scheduling;Metroplex;Clustering;Simulation

McClain, Evan James ; Aerospace Engineering

University:Georgia Institute of Technology

Department:Aerospace Engineering

关键词: Scheduling;    Metroplex;    Clustering;    Simulation;   

Others  :  https://smartech.gatech.edu/bitstream/1853/47692/1/mcclain_evan_j_201305_phd.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

As airspace congestion becomes increasingly more common, one of the primary places airspace congestion is felt today, and will only continue to increase, is in areas where more than one major airport interact. We will call these groups of interdependent airports a metroplex; a term originally coined to describe large metropolitan areas where more than one city of equal (or near equal) size or importance. These metroplex areas are of particular importance in understanding future capacity demands because many of these areas are currently experiencing problems with meeting the current demand, and demand is only projected to increase as air travel becomes more popular. Many of these capacity issues have been identified in the FAA's Future Airport Capacity Task (FACT).From the second FACT report, it is stated that "the FACT 1 analysis revealed that many of our hub airports and their associated metropolitan areas could be expected to experience capacity constraints (i.e. unacceptable levels of delay) by 2013 and 2020, even if the planned improvements envisioned at that time were completed." This analysis shows that the current methods of expanding airports will not scale with the growing demand. To address this growing demand, a three part solution is proposed.The first step is to properly identify the metroplex areas to be evaluated.While the FACT reports serve to identify areas where capacity growth does not meet demand, these areas are not grouped into metroplexes. To do this grouping, an interaction metric was developed based on airport distance and traffic volume. This interaction metric serves as a proxy for how the existence of a second airport impacts the operation of the first. This pairwise metric was then computed for all commercial airports in the US and were grouped into metroplexes using a clustering algorithm.The second obstacle was to develop a tool to evaluate each metroplex as new algorithms were tested. A discrete event based simulation was developed to model each link in the airspace structure for each aircraft that enters the TRACON.This program tracks the delay each aircraft is required to accumulate in holding patterns or traffic trombones.A third and final method discussed here was an optimization program that can be used to schedule aircraft that are entering the TRACON to perform small modifications in their speed while en route to reduce the overall delay (both en route and in the TRACON). While formal optimization methods for scheduling aircraft arrivals have been presented before, the computational complexity has greatly prevented such algorithms from being used to schedule many aircraft in a dense schedule. This is because mixed integer programming (MIP) is a NP-hard problem. Practically, this means that the solution time can grow exponentially as the problem size (number of aircraft) increases. To address this issue, a Benders' decomposition scheme was introduced that allows solutions to be computed in near real-time on commodity hardware. These solutions can be evaluated and compared against the currently used TMA algorithm to show surprising gains in high density traffic.