[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122063-en":3,"doc-seo-122063-105":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},122063,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Deciphering Air Travel Disruptions - A Machine Learning Approach","This research investigates flight delay trends by examining factors such as departure time, airline, and airport. It applies regression-based machine learning to estimate how different sources contribute to delays, comparing time-series approaches like LSTM, Hybrid LSTM, and Bi-LSTM with baseline regression models including Multiple Regression, Decision Tree Regression, Random Forest Regression, and Neural Networks. Despite notable baseline errors, the goal is to identify influential features for delay prediction and to support aviation planning. Unlike prior work, the study analyzes delay components (e.g., security, weather) independently using regression and time-series methods, yielding operational insights for aviation stakeholders.","Deciphering Air Travel Disruptions: A Machine Learning  \nApproach∗  \nAravinda Jatavallabha  \nNorth Carolina State University Raleigh, NC, USA [arjatava@ncsu.edu](arjatava@ncsu.edu)  \nJacob Gerlach  \nNorth Carolina State University Raleigh, NC, USA [jwgerlac@ncsu.edu](jwgerlac@ncsu.edu)  \nAadithya Naresh  \nNorth Carolina State University Raleigh, NC, USA [anaresh@ncsu.edu](anaresh@ncsu.edu)  \narXiv :2408 .02802v1 [ cs .LG] 5 Aug 2024  \nABSTRACT  \nThis research investigates flight delay trends by examining factors such as departure time, airline, and airport. It employs regression machine learning methods to predict the contributions of various sources to delays. Time-series models, including LSTM, Hybrid LSTM, and Bi-LSTM, are compared with baseline regression models such as Multiple Regression, Decision Tree Regression, Random Forest Regression, and Neural Network. Despite considerable error in the baseline models, the study aims to identify influential features in delay prediction, potentially informing flight planning strategies. Unlike previous work, this research focuses on regression tasks and explores the use of time-series models for predicting flight delays. It offers insights into aviation operations by analyzing each delay component (e.g., security, weather) independently.  \nKEYWORDS  \nFlight Delay Prediction, Time Series Forecasting, Predictive Modeling, LSTM Models, Supervised Learning, Aviation Industry, Optimization Algorithms, Operational Efficiency  \n1 INTRODUCTION  \nDelay stands out as one of the most salient performance metrics for any transportation network. Flight punctuality is a critical aspect of airport and airline service quality, but flight delays in arrival and departure pose substantial challenges impacting operational efficiency and customer satisfaction [1] . The Federal Aviation Administration (FAA) estimated in 2019 that these delays cost the aviation industry $33 billion annually [2] . In addition to financial implications, delays also contribute to environmental concerns through increased fuel emissions [3, 4] . Predicting flight delays is essential for proactive planning and resource allocation, benefiting airlines, passengers, and airports.  \nFlight delay prediction involves categorizing issues such as delay causes, institutional impacts, and mitigation strategies as seen in Fig 1 . These encompass delay propagation, departure and route delays, and flight cancellations. While challenges persist, predictive tools aid operators and administrators in proactive management. Delays affect airlines, airports, and airspace, necessitating synchronized operations [5] . Predictive system development entails utilizing methods like machine learning, probabilistic models, statistical analysis, or network representations.  \nWith this study, we aim to individually predict the different factors contributing to flight delay rather than overall delay. We believe that doing so will allow for passengers and airlines to better predict flight delay bottlenecks.  \n∗GitHub Link: [https://github.com/jwgerlach00/flight_delay_prediction](https://github.com/jwgerlach00/flight_delay_prediction)  \nFigure 1: Taxonomy ofFlight Delay Prediction Problem  \n1.1 Statistical Analysis  \nUtilizing statistical models involves employing correlation analysis, parametric and non-parametric tests, multivariate analysis, and econometric models. Government agencies have adopted these econometric models to comprehend the relationship between delaysand factors such as passenger demand, fares, and aircraft size.  \n1.2 Probabilistic Models  \nProbabilistic modeling necessitates analysis tools that estimate the likelihood of an event based on historical data. The predicted outcome is expressed as a distribution function of probability. The inherent randomness factor invariably influences decisions or outcomes generated by probabilistic models.  \n1.3 Machine Learning  \nIn supervised machine learning, datasets comprising input and output are prov","cbCaipoRFJPVVHlH","https://ap.wps.com/l/cbCaipoRFJPVVHlH","pdf",931968,1,10,"English","en",105,"# Introduction\n## Statistical Analysis\n## Probabilistic Models\n## Machine Learning\n# Literature Survey","[{\"question\":\"What factors does the study use to analyze flight delays?\",\"answer\":\"The study examines factors such as departure time, airline, and airport to understand delay behavior and trends.\"},{\"question\":\"Which models are compared for predicting delay contributions?\",\"answer\":\"Time-series LSTM variants (LSTM, Hybrid LSTM, Bi-LSTM) are compared with baseline regression methods such as Multiple Regression, Decision Tree Regression, Random Forest Regression, and Neural Network.\"},{\"question\":\"How does the research differ from previous flight-delay work?\",\"answer\":\"It focuses on regression tasks and uses time-series models to predict contributions from each delay component independently, such as security and weather.\"}]","Deciphering Air Travel Disruptions - 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