[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120561-en":3,"doc-seo-120561-105":30,"detail-sidebar-cat-0-en-105":91},{"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},120561,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Predicting Flight Delays Using Machine Learning","Flight delays remain a persistent challenge for the aviation industry, imposing high costs and disrupting operations across interconnected networks. Monitoring and scheduling tools often offer oversight but fall short on predictive accuracy and coordinated disruption management across stakeholders. This study proposes and evaluates an AI-enabled machine learning framework that fuses operational and meteorological data to forecast delays more reliably. Using U.S. domestic flight records and NOAA weather data for 2024, ensemble models—especially Random Forest with SMOTE—identify delayed flights with high accuracy and improve regression error. The framework supports shared situational awareness for airlines, airports, and air traffic control, strengthening resilience and efficiency.","Proceedings of the 59th Hawaii International Conference on System Sciences | 2026  \nPredicting Flight Delays Using Machine Learning  \nKenney Snell United Parcel Service (UPS)  \nLouisville (Retired)  \n[biggreenbox18@icloud.com](biggreenbox18@icloud.com)  \nJozef Zurada University of Louisville College of Business  \njozef,[zurada@louisville.edu](zurada@louisville.edu)  \nZahra Hatami University of Louisville College of Business  \n[zara.hatami@louisville.edu](zara.hatami@louisville.edu)  \nCelina Olszak University of Economics in Katowice  \n[celina.olszak@ue.katowice.pl](celina.olszak@ue.katowice.pl)  \nJan Kozak  \nUniversity of Economics in Katowice  \n[jan.kozak@ue.katowice.pl](jan.kozak@ue.katowice.pl)  \nAbstract  \nFlight delays remain a persistent challenge for the aviation industry, generating high costs and disrupting operations across interconnected networks. Existing monitoring and scheduling tools provide valuable oversight but often lack predictive accuracy and crossstakeholder coordination, limiting their effectiveness in disruption management. This study develops and evaluates an AI-enabled machine learning framework that integrates operational and meteorological data to forecast delays more reliably. Using U.S. domestic flight records and NOAA weather data for 2024, including a case study at Louisville Muhammad Ali International Airport, we apply classification and regression models to predict on-time performance and delay minutes. Ensemble methods, particularly Random Forest with SMOTE balancing, achieve superior results, detecting delayed flights with 94.7% accuracy and reducing mean absolute error in regression tasks to 4.79 minutes. Beyond technical gains, the framework demonstrates how AI-driven prediction can enhance collaborative decision-making by enabling shared situational awareness across airlines, airports, and air traffic control, strengthening resilience and efficiency in aviation operations.  \n1. Introduction  \nWe all use airlines today whether we are going to a meeting in a distant city, going on a trip or vacation or attending a conference or travelling international to board a cruise. Pilots and flight attendants use the airlines to fly from their present location to the airport where they are starting the trip. Airline delays cause problems for travelers and cost the airlines billions of dollars annually (Air Travel Consumer Report – ATCR, 2024) . This paper performs data analysis and data exploration on US domestic flight delays, as tracked by the Bureau of Transportation Statistics (BTS) for 2024. A computer simulation study (SDF Study) contains flights that depart from SDF  \n(Louisville Muhammad Ali International Airport) and return to SDF. The SDF study only looks at airports where the number offlights is greater than 300 (origin, destination pairs) outgoing from SDF and incoming to SDF. Some reports and graphs are included to help one understand the flight data for both flight data sets. The SDF study merges the flight data with NOAA weather data by airport, flight date, and hour. Then the weather data is added for each flight’s outgoing and incoming airport. Last, several machine learning algorithms are processed to predict results for aircraft arrival delaysand aircraft arrivals delay minutes. Cancelled and diverted flights are removed from the SDF study.  \nThe topic explored in this study contributes to the ongoing discourse on the innovative, collaborative, and sustainable development of organizations through the application of AI and BD technologies. As the operational and economic challenges of the aviation industry intensify, the strategic use of machine learning for predictive analytics becomes increasingly relevant. This research addresses the problem offlight delays by developing robust predictive models based on a largescale integration of transportation and weather data. The approach demonstrates how complex, real-world datasets can be transformed into actionable intelligence, supportin","cbCaibFFahJmQBw5","https://ap.wps.com/l/cbCaibFFahJmQBw5","pdf",633828,1,10,"English","en",105,"# Introduction\n## Motivation","[{\"question\":\"What problem does the study address in aviation operations?\",\"answer\":\"The study addresses persistent flight delays and the limitations of current tools in accurately predicting disruptions and enabling coordinated responses.\"},{\"question\":\"How does the proposed framework forecast flight delays?\",\"answer\":\"It integrates operational data with meteorological information from NOAA, then applies classification and regression machine learning models to predict on-time performance and delay minutes.\"},{\"question\":\"Which modeling approach performed best and what were the reported results?\",\"answer\":\"Ensemble methods, particularly Random Forest with SMOTE balancing, achieved superior performance, including 94.7% accuracy for detecting delayed flights and a mean absolute error of 4.79 minutes for regression.\"}]","Predicting Flight Delays Using Machine Learning | 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