[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122855-en":3,"doc-seo-122855-105":30,"detail-sidebar-cat-0-en-105":83},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122855,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Machine Learning-Enhanced Aircraft Landing Scheduling under Uncertainties - Abstract","Aircraft delays create safety risks and financial losses that can persist for hours during extreme arrival scenarios. Heuristic First-Come-First-Served practices rely on controller judgments and struggle to manage uncertainty. This paper presents a machine learning-enhanced framework where data-driven models predict ETA distributions and separation-time quantities, then embed these predictions as safety constraints in a time-constrained traveling salesman formulation. Mixed-integer linear programming and reliability handling using historical recordings are evaluated on real flight tracks, reducing total landing time by 17.2% on average.","arXiv :2311 . 16030v1 [ cs .AI] 27 Nov 2023  \nHighlights  \nMachine Learning-Enhanced Aircraft Landing Scheduling under Uncertainties  \nYutian Pang, Peng Zhao, Jueming Hu, Yongming Liu  \n• We identify the problem of interest by investigating the various flight scenarios and observe that holding patterns exist in most of the arrival delay cases.  \n• We propose using machine learning to predict the estimated arrival time (ETA) distributions for landing aircraft from real-world flight recordings, which are further used to obtain the probabilistic Minimal Separation Time (MST) between successive arrival flights.  \n• We propose to incorporate the predicted MSTs into the constraints of the Time-Constrained Traveling Salesman Problem (TSP) for Aircraft Landing Scheduling (ALS) optimization.  \n• We build a multi-stage conditional prediction algorithm based on the looping events, and find that explicitly including flight event counts and airspace complexity measures can benefit the model prediction capability.  \n• We demonstrate that the proposed method reduces the total landing time with a controlled reliability level compared with the First-Come-FirstServed (FCFS) rule by running experiments with real-world data.  \nMachine Learning-Enhanced Aircraft Landing Scheduling under Uncertainties  \nYutian Panga , Peng Zhaoa , Jueming Hua , Yongming Liua,∗  \na School for Engineering of Matter, Transport and Energy, Arizona State  \nUniversity, Tempe, 85287, AZ, USA  \nAbstract  \nAircraft delays lead to safety concerns and financial losses, which can propagate for several hours during extreme scenarios. Developing an efficient landing scheduling method is one of the effective approaches to reducing flight delaysand safety concerns. Existing scheduling practices are mostly done by air traffic controllers (ATC) with heuristic rules. This paper proposes a novel machine learning-enhanced methodology for aircraft landing scheduling. Data-driven machine learning (ML) models are proposed to enhance automation and safety. ML enhancement is adopted for both prediction and optimization. First, the flight arrival delay scenarios are analyzed to identify the delay-related factors, where strong multimodal distributions and arrival flight time duration clusters are observed. A multi-stage conditional ML predictor is proposed for improved prediction performance of separation time conditioned on flight events. Next, we propose incorporating the ML predictions as safety constraints of the timeconstrained traveling salesman problem formulation. The scheduling problem is then solved with mixed-integer linear programming (MILP) . Additionally, uncertainties between successive flights from historical flight recordings and model predictions are included to ensure reliability. We demonstrate the real-world applicability of our method using the flight track and event data from the Sherlock database of the Atlanta Air Route Traffic Control Center (ARTCC ZTL) . The case studies provide evidence that the proposed method is capable of reducing the total landing time by an average of 17.2% across three case studies, when compared to the First-Come-First-Served (FCFS) rule. Unlike the deterministic heuristic FCFS rule, the proposed methodology also considers the uncertainties between aircraft and ensures confidence in the scheduling. Finally, several concluding remarks and future research directions are given. The code used can be retrieved from [Link] .  \nKeywords: Air Traffic Management, Landing Scheduling, Data-Driven Prediction, Optimization, Machine Learning  \n∗ Corresponding author.  \nEmail address: [Yongming.liu@asu.edu](Yongming.liu@asu.edu) (Yongming Liu)  \nPreprint Accepted by Transportation Research Part C November 28, 2023  \n1. Introduction  \nThe civil aviation industry is losing air traffic control talents, while the need for maintaining daily operations keeps surging (FAA, 2020) . This situation leads to increased operational costs, higher safety concerns, an ele","cbCaihfT4olrk65i","https://ap.wps.com/l/cbCaihfT4olrk65i","pdf",9952132,1,43,"English","en",105,"# Introduction\n## Aircraft delay challenges and automation needs\n## Decision support tools and terminal operations background","[{\"question\":\"How are uncertainties incorporated into the optimization model?\",\"answer\":\"Uncertainties between successive flights are included through reliability-focused modeling, and the predicted separation constraints are enforced within a time-constrained traveling salesman problem solved via MILP.\"}]","Machine Learning-Enhanced Aircraft Landing Scheduling under Uncertainties - Abstract | PDF",1785813323,108,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":78,"head_meta":80,"extra_data":82,"updated_unix":28},"machine-learning-enhanced-aircraft-landing-scheduling-under-uncertainties-abstract","",{"@graph":36,"@context":77},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-enhanced-aircraft-landing-scheduling-under-uncertainties-abstract/122855/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"How are uncertainties incorporated into the optimization model?","Question",{"text":75,"@type":76},"Uncertainties between successive flights are included through reliability-focused modeling, and the predicted separation constraints are enforced within a time-constrained traveling salesman problem solved via MILP.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,115,120,123,127],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":98,"slug":130},19,"General","general"]