[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123509-en":3,"doc-seo-123509-105":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":20,"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},123509,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Flight Delay Prediction using Hybrid Machine Learning Approach - A Case Study of Major Airlines in the United States","The aviation industry has grown steadily since U.S. airline deregulation in 1978, making flight delays a persistent challenge for both airlines and passengers. Delays increase consumption of constrained resources such as fuel, labor, and capital, and are expected to intensify in coming decades. This study proposes a hybrid modeling approach that combines deep learning features with classic machine learning methods, evaluates multiple algorithms, and reports accuracy, precision, recall, F1-score, as well as ROC and AUC, supported by extensive data and model analysis for U.S. airlines.","Flight Delay Prediction using Hybrid Machine Learning Approach: A Case Study of  \nMajor Airlines in the United States  \nRajesh Kumar Jha 1, Shashi Bhushan Jha2,4, *, Vijay Pandey3, Radu F. Babiceanu4  \n1Department of Electronics and Communication Engineering, BNMIT, India 2Department of Computer Science, University of West Florida, FL, USA 3Department of Computer Science Engineering, IIT Kharagpur, India 4Department of Electrical Engineering and Computer Science, Embry-Riddle Aeronautical  \nUniversity, Daytona Beach, USA  \nE-mail: [sjha@uwf.edu](sjha@uwf.edu), [babicear@erau.edu](babicear@erau.edu), [vijayiitkgp13@gmail.com](vijayiitkgp13@gmail.com), [rajeshjnv23@gmail.com](rajeshjnv23@gmail.com)  \n*Corresponding author (email: [sjha@uwf.edu](sjha@uwf.edu))  \nAbstract  \nThe aviation industry has experienced constant growth in air traffic since the deregulation of the U.S. airline industry in 1978. As a result, flight delays have become a major concern for airlines and passengers, leading to significant research on factors affecting flight delays such as departure, arrival, and total delays. Flight delays result in increased consumption of limited resources such as fuel, labor, and capital, and are expected to increase in the coming decades. To address the flight delay problem, this research proposes a hybrid approach that combines the feature of deep learning and classic machine learning techniques. In addition, several machine learning algorithms are applied on flight data to validate the results of proposed model. To measure the performance of the model, accuracy, precision, recall, and F1-score are calculated, and ROC and AUC curves are generated. The study also includes an extensive analysis of the flight data and each model to obtain insightful results for U.S. airlines.  \nI. Introduction  \nOver the last few decades, studying the air transportation system has become a crucial area of research, especially in relation to flight delays caused by high demand and limited capacity. Since the deregulation of the U.S. airline industry in 1978, the aviation industry has experienced constant growth in air traffic, resulting in increased competition and a significant amount of research on factors affecting flight delays such as departure, arrival, and total delays. This growth has also led to a global increase in air traffic, resulting in numerous delays and costs for both airlines and passengers (World Bank, 2013) (Ball et al., 2010; JEC, 2008; Cook et al., 2004) . The Federal Aviation Administration (FAA) predicts a 40% increase in total enplanements by 2038, with increased operating costs for airlines being a major concern due to flight delays (Zou and Chen, 2017) . Such delays result in increased consumption of fuel, labor, capital, and other limited resources. According to the FAA, delay and congestion are expected to increase in the coming decades, which will further exacerbate these issues (Boeing, 2011) .  \nInitially, the airline industry required information on actual flight departure and arrival times, as well as details on flight cancellations and diversions. Between 1995 and 2008, the aviation industry was also required to disclose several factors related to flight delays, includingthe cause of delays, flight cancellations, technical issues, airborne times, aircraft tail numbers, and taxi times. Moreover, the tarmac rule stipulates that carriers are not permitted to keep passengers on board for more than three hours without deplaning them (Yimga and Gorjidooz, 2019; Forbes et al., 2019) .  \nThis research aims to address the flight delay problem by categorizing it into three distinct subproblems-flight departure delay, flight arrival delay, and total flight delay-and using important features of flight operations. To solve these problems, the study develops a hybrid approach that combines deep learning and classic machine learning techniques. Additionally, various machine learning algorithms are used to validate the result","cbCaieE95VvR4MhC","https://ap.wps.com/l/cbCaieE95VvR4MhC","pdf",2155904,1,20,"English","en",105,"# Introduction\n# Literature Review\n# Problem Definition and Data Analysis\n# Proposed Hybrid Approach\n# Experimental Results\n# Discussion\n# Conclusion and Future Scope","[{\"question\":\"What problem does the study address?\",\"answer\":\"It addresses the flight delay problem by modeling and categorizing delay into departure delay, arrival delay, and total flight delay using operational features.\"},{\"question\":\"What modeling approach is proposed?\",\"answer\":\"The research proposes a hybrid approach that combines deep learning with classic machine learning techniques, then applies multiple algorithms to validate the results.\"},{\"question\":\"How is the model performance evaluated?\",\"answer\":\"Performance is measured using accuracy, precision, recall, and F1-score, and the study generates ROC and AUC curves to assess classification quality.\"}]","Flight Delay Prediction using Hybrid Machine Learning Approach - 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