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The simulation supports long-horizon forecasting, such as 365 days, and produces performance metrics for feedback to optimization methods. Results from the developed surface model show high prediction accuracy versus historical service rates, queue lengths, and taxi times.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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is the core idea of the Neural-Queue framework?","Question",{"text":63,"@type":64},"It combines an artificial neural network trained on historical airport data with a queueing network that controls aircraft flow through congestion points on the tarmac.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How far into the future can the model forecast traffic?",{"text":68,"@type":64},"The simulation is capable of forecasting arrival and departure traffic over long time horizons, for example 365 days.",{"name":70,"@type":61,"acceptedAnswer":71},"How is the framework validated?",{"text":72,"@type":64},"The accuracy and stability are validated by comparing simulated traffic and congestion metrics from extended simulations with real historical traffic 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Approach to Modeling Airport Surface Traffic at Charlotte-Douglas International Airport  \nLiam McKenna∗ , Kenny Chour†, Milad Memarzadeh‡, and Krishna Kalyanam§  \nNASA Ames Research Center, Moffett Field, CA 94035, USA  \nRajnikant Sharma¶  \nAir Force Institute of Technology, Wright-Patterson AFB, OH 45433, USA  \nIn the pursuit of more powerful analysis tools for Air Traffic Management, training and evaluation of tool effectiveness is typically performed within the context of a simulated system that can adequately replicate the complexity of the National Airspace System. The ground operations of airports is of particular concern for approaches to improving congestion and traffic flow, but due to complex aircraft taxi procedures and variation of air traffic playbooks, forecasting is difficult. This work outlines a framework to aggregate and predict complex airport surface traffic in the context of optimizing air traffic flow and ground operations. In our approach, an Artificial Neural Network is trained on historical data from Charlotte-Douglas International Airport. The Neural Network then informs a Queueing Network for controlling aircraft traffic flow through various congestion points on the tarmac. The resulting system is capable of forecasting arrival and departure traffic over a long time horizon (e.g. 365 days). The simulation environment then provides a basis for deriving useful performance metrics which could be used as feedback to optimization techniques. The output of the surface model we develop shows high prediction accuracy compared to the historical service rates, queue lengths, and taxi times.  \nI. Introduction  \nAirports are among busiest locations in the world, serving millions of flights annually. The airport’s surface is often congested due to long queues of aircraft at runways or terminal locations, leading to increased fuel burn, delays, and emissions [1, 2] . Therefore, it is imperative to accurately predict traffic flow behavior to analyze congestion mitigation strategies. In runway configuration management (RCM), the goal is to optimally select the flow configuration of an airport to maximize traffic flow. Various methods for performing RCM include optimization [3], heuristics [4], and Reinforcement Learning (RL) approaches [5], but validating their efficacy is challenging. Thus, there is a need to fill this validation gap with an efficient traffic flow modeling and simulation framework.  \nTraditional methods of capturing airport traffic flow include stochastic queue modeling [6] or microsimulations (such as commercially available tools like SIMMOD[7]) . Stochastic queue modeling is very stable and tractable but struggles to account for all of the conditions affecting a given airport (weather, layout, traffic patterns, and human factors) . Microsimulations are highly accurate but are very cumbersome to set up and computationally demanding, making their use impractical for fast-time evaluations.  \nMore recently, data-driven approaches have been adopted to overcome these challenges [8–11] . Notably, traffic prediction approaches using Machine Learning (ML) have proven their versatility and effectiveness predicting traffic dynamics in the short term, but ML models typically perform poorly for extended prediction horizons (e.g. longer thana week) .  \nThis work proposes the use of a hybrid framework to capture the complex, long-term traffic flow behavior of an airport’s surface. We devise a method to simulate airport surface traffic flow using a combination of numerical discrete time models and encoding of historical state information. Our approach addresses some shortcomings faced by other simplified airport performance models and model-free approaches by encoding airport states with an Artificial  \n∗ Graduate Student, University of Cincinnati and NASA OSTEM intern, AIAA Student Member †Senior Software Engineer, Metis Technology Solutions Inc., AIAA Member  \n‡Aviation Systems Division, NASA ","cbCailbvHJR5NeRA","https://ap.wps.com/l/cbCailbvHJR5NeRA","pdf",2031209,14,"English","# Introduction\n## Related Works\n### Airport Surface Queueing Model","[{\"question\":\"What is the core idea of the Neural-Queue framework?\",\"answer\":\"It combines an artificial neural network trained on historical airport data with a queueing network that controls aircraft flow through congestion points on the tarmac.\"},{\"question\":\"How far into the future can the model forecast traffic?\",\"answer\":\"The simulation is capable of forecasting arrival and departure traffic over long time horizons, for example 365 days.\"},{\"question\":\"How is the framework validated?\",\"answer\":\"The accuracy and stability are validated by comparing simulated traffic and congestion metrics from extended simulations with real historical traffic data.\"}]","A Neural-Queuing Approach to Modeling Airport Surface Traffic at Charlotte-Douglas International Airport | PDF",35]