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The work analyzes 2014 surface surveillance flight records (246,083 departures after filtering), categorizing taxi-out times by terminal concourse, spot, runway, departure fix, aircraft weight class, and month. Key operational factors are identified to support more accurate prediction. Performance is evaluated by comparing prediction methods using actual surface surveillance data, then integrating the module with a tactical scheduler for the ATD-2 project via live-data 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is the primary goal of this document?","Question",{"text":112,"@type":113},"To predict taxi-out time for departing aircraft at Charlotte Airport (CLT) using machine learning techniques and evaluate the prediction performance for operational use in ATD-2.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What data and how much of it is used for the analysis?",{"text":117,"@type":113},"Actual flight data at CLT in 2014 is used, with 246,083 departures analyzed after data filtering.",{"name":119,"@type":110,"acceptedAnswer":120},"Which factors are considered when analyzing taxi-out times?",{"text":121,"@type":113},"Taxi-out times are categorized by terminal concourse, spot, runway, departure fix, aircraft weight class, and month.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},268809,1790225709,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":29,"category_name":30,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":26},7971474921005,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","[https://ntrs.nasa.gov/search.jsp?R=20170000660 2019-07-1](https://ntrs.nasa.gov/search.jsp?R=20170000660 2019-07-1) 1T02:47:05+00:00Z  \nTaxi-out Time Prediction for Departures at Charlotte Airport Using Machine Learning Techniques  \nHanbong Lee  \n3 rd Joint Workshop  \nfor KAIA/KARI – NASA ATM Research Collaboration NASA Ames Research Center October 24-26, 2016  \nOutline   \n• Introduction: Aircraft taxi time prediction  \n• Charlotte Douglas International Airport (CLT)  \n• Taxi-out time data analysis  \n• Taxi time prediction using machine learning techniques  \n• Prediction performance evaluation  \n• Ongoing work for ATD-2  \n– Linear regression model with live data at CLT  \nMotivation   \n• Taxi-out time for departing aircraft  \n– Ground movement time from pushback to takeoff  \n– Depend on taxi route and surface congestion  \n• Aircraft taxi time prediction  \n– Increase takeoff time predictability  \n– Improve efficiency in airport surface operations  \n– Help controllers find better takeoff sequences to maximize runway throughput  \n• However, accurate prediction is difficult.  \n– Uncertainties in airport operations  \n– Operational complexity  \nPrevious Research   \n• Queuing models for taxi-out time estimation  \n• Machine learning based approaches  \n– Linear regression models, Neural network model, Reinforcement learning algorithms, etc.  \n– Independently applied to limited data at several airports  \n• Taxi time prediction using machine learning methods and fast-time simulation (Lee, 2015)  \n– Used human-in-the-loop simulation data for CLT  \n– Possibly over-trained with limited datasets  \nObjectives   \n• Analyze actual taxi time data at Charlotte airport (CLT)  \n– Identify unique operational characteristics of CLT  \n– Determine key factors affecting taxi times  \n• Develop precise taxi time prediction modules  \n– Based on taxi-out time data analysis  \n– Using machine learning techniques  \n• Evaluate taxi time prediction performance  \n– Using actual surface surveillance data at CLT  \n– Comparison of prediction methods  \n• Apply the taxi time prediction module to live data and incorporate it with a tactical scheduler for ATD-2 project  \nCharlotte International Airport (CLT )  \nRamp Area  \n1  \n5  \n10  \n8  \nTaxi-Out Time Data Analysis  \n• Taxi-out time data  \n– Used actual flight data at CLT in 2014  \n– Analyzed 246,083 departures after data filtering  \n• Taxi-out times categorized by  \n– Terminal concourse  \n– Spot  \n– Runway  \n– Departure fix  \n– Aircraft weight class  \n– Month  \nTaxi Time by Terminal  \nDeparture distribution by terminal concourse  \n35  \n30  \n25  \n20  \n15  \n10  \n5  \n0  \n\n| Standard Deviation |  |  |  |  | −−− 2014 Average |  |  |  |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- |\n|  |  |  |  |  |  |  |  |  |\n| |  |  |  |  |  |  |  |  |\n|  |  |  |  |  |  |  |  |  |\n|  |  |  |  |  |  |  |  |  |\n|  |  |  |  |  |  |  |  |  |\n|  |  |  |  |  |  |  |  |  |\n|  |  |  |  |  |  |  |  |  |\n|  |  |  |  |  |  |  |  |  |\n\nA B C D E Unknown  \nAverage taxi-out time (in minutes) by terminal concourse  \nAverage taxi time seems insensitive to terminal concourse, except for concourse D used by international flights.  \nTaxi Time by Spot  \nS1  \nUnknown  \n19.3%  \nS12  \n11.2%  \nS11  \n0.5%  \nS8  \nS10 12.8% 4.5%  \nDeparture distribution by spot  \n45  \n40  \n35  \n30  \n25  \n20  \n15  \n10  \n5 0  \n−−− 2014 Average  \nclose to hardstand  \nclose to runway 18L  \nAverage taxi-out time (in minutes) by spot  \nSpots S10, S11 and S12 are assigned to flights from concourse D/E to runway 18L, leading to short taxi time.  \nTaxi Time by Runway  \n0.6% 0.0%  \nDeparture distribution by runway  \n45  \n40  \n35  \n30  \n25  \n20  \n15  \n10  \n5 0  \n\n|  |  |  |  |  |  |  | −−− 2014 Average |  |  |  |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n|  |  |  |  |  |  |  |  |  |  |  |\n|  |  |  |  |  |  |  |  |  |  |  |\n|  |  |  |  |  |  |  |  |  |  |  |\n|  |  |  | |  |  |  |  |  |  |  |\n|  |  |  |  |  |  |  |  |  |  |  |\n|  |  |  |  |  |  |  |  |  |  |","cbCaioLDQWsksq2x","https://ap.wps.com/l/cbCaioLDQWsksq2x","pdf",989581,24,"English","# Introduction\n# Charlotte Douglas International Airport (CLT)\n## Taxi-out time data analysis\n## Taxi time prediction using machine learning techniques\n## Prediction performance evaluation\n# Ongoing work for ATD-2","[{\"question\":\"What is the primary goal of this document?\",\"answer\":\"To predict taxi-out time for departing aircraft at Charlotte Airport (CLT) using machine learning techniques and evaluate the prediction performance for operational use in ATD-2.\"},{\"question\":\"What data and how much of it is used for the analysis?\",\"answer\":\"Actual flight data at CLT in 2014 is used, with 246,083 departures analyzed after data filtering.\"},{\"question\":\"Which factors are considered when analyzing taxi-out times?\",\"answer\":\"Taxi-out times are categorized by terminal concourse, spot, runway, departure fix, aircraft weight class, and month.\"}]","Taxi-out Time Prediction for Departures at Charlotte Airport Using Machine Learning Techniques - read online free | PDF",1789437434]