[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123603-en":3,"doc-seo-123603-105":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},123603,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Legal actions in Brazilian air transport - A machine learning and multinomial logistic regression analysis - Predictive modeling for airline indemnities","Air transport in Brazil faces significant operational risk from the volume of lawsuits filed against airlines. These legal actions can depress entry of new competitors and increase legal uncertainty, while shaping the level of judicial indemnities. The research analyzes lawsuits from 2016 to 2021 using company data and evaluates Naive Bayes, Random Forest, Support Vector Machines, and Multinomial Logistic Regression. Random Forest and Logistic Regression show superior predictive power; delays, cancellations, and airline faults negatively affect indemnities. Above-average compensation concentrates in some states, where awarded moral damages drive higher values.","TYPE Original Research PUBLISHED 17 April 2023  \nDOI 10.3389/ffutr.2023.1070533  \nOPEN ACCESS  \nEDITED BY  \nGustavo Alonso,  \nPolytechnic University of Madrid, Spain  \nREVIEWED BY  \nFernando Gomez Comendador, Polytechnic University of Madrid, Spain Rosa Arnaldo,  \nPolytechnic University of Madrid, Spain Antonio Comi,  \nUniversity of Rome Tor Vergata, Italy  \n*CORRESPONDENCE  \nGabriel de Oliveira Torres,  [gabrielot4@gmail.com](gabrielot4@gmail.com)  \nSPECIALTY SECTION  \nThis article was submitted to Transportation Systems Modeling, a section of the journal Frontiers in Future Transportation  \nRECEIVED 15 October 2022  \nACCEPTED 17 January 2023  \nPUBLISHED 17 April 2023  \nCITATION  \nTorres GdO, Guterres MX and  \nCelestino VRR (2023), Legal actions in Brazilian air transport: A machine learning  \nand multinomial logistic regression analysis.  \nFront. Future Transp. 4:1070533 .  \ndoi: 10.3389/ffutr.2023.1070533  \nCOPYRIGHT  \n© 2023 Torres, Guterres and Celestino. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nLegal actions in Brazilian air transport: A machine learning and multinomial logistic regression analysis  \nGabriel de Oliveira Torres 1*, Marcelo Xavier Guterres 1 and Victor Rafael Rezende Celestino 2  \n1Aeronautics Institute of Technology, São José dos Campos, Brazil, 2Department of Administration of the Faculty of Economics, Administration, Accounting and Public Management, University of Brasilia, Brasilia, Brazil  \nIn Brazil, one of the most harmful costs for airlines is the number of lawsuits filed against them. It is a problem that can affect its operations, reduce the entry of new competitors and create legal uncertainty in the country. This work seeks to highlight the factors which most contribute to the rise of judicial indemnities, discuss the most relevant issues and identify the best techniques to predict the indemnified values. The objective is to provide subsidies for airlines to mitigate the number of legal actions by using machine learning models. This research contributes by discussing one of the most relevant subjects in Brazilian air transport and comparing the machine learning models ’ performance. The study is based on lawsuits between 2016 and 2021 using the companies’ data. The performance of Naive Bayes, Random Forest, Support Vector Machines, and Multinomial Logistic Regression models are evaluated through the accuracy, area under the ROC curve, and confusion matrix. The results showed better predictive power for Random Forest and Logistic Regression. The latter showed that flight delays, cancellations, and airline faults have a negative effect on indemnities. The above-average compensation is a tendency in some states, being the moral damage awarded to customers the main cause of higher compensation.  \nKEYWORDS  \nair transport, airline, lawsuit, machine learning, multinomial logistic regression  \n1 Introduction  \nThe air transport industry, which has narrow profit margins, depends on external factors that are not only outside the companies’ control, but also difficult to control, such as the price of fuel (Doganis, 2019) . Other factors, such as weather conditions, aeronautical infrastructure, air traffic problems, and unexpected aircraft maintenance, can also hamper airline operations. In addition to increasing the complexity of operations, some aspects may generate extra expenses for the companies, such as costs with lawsuits, food supply, transport assistance, and accommodation for passengers, since the chances of failure may frustrate customers’expectations (Gasparotto et al., 2018) .  \nSome conditions ","cbCaiiFzgvREwsR6","https://ap.wps.com/l/cbCaiiFzgvREwsR6","pdf",1408671,1,16,"English","en",105,"# Introduction\n## Background on airline operations and lawsuit costs\n## Drivers of legal actions and external factors\n# Methods (implied)\n## Dataset of lawsuits (2016–2021)\n## Predictive models and evaluation metrics\n# Results (implied)\n## Model performance and comparative findings\n## Factors affecting indemnities and regional patterns","[{\"question\":\"What problem do the authors address in Brazilian air transport?\",\"answer\":\"The work addresses the harmful impact of the number of lawsuits filed against airlines, which can affect operations, competition, and legal uncertainty while driving judicial indemnities.\"},{\"question\":\"Which machine learning models are evaluated in the study?\",\"answer\":\"Naive Bayes, Random Forest, Support Vector Machines, and Multinomial Logistic Regression are evaluated.\"},{\"question\":\"What factors negatively affect indemnities according to the results?\",\"answer\":\"Flight delays, cancellations, and airline faults have a negative effect on indemnities.\"}]","Legal actions in Brazilian air transport - A machine learning and multinomial logistic regression analysis - Predictive modeling for airline indemnities | PDF",1785817593,40,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"legal-actions-in-brazilian-air-transport-a-machine-learning-and-multinomial-logistic-regression-analysis-predictive-modeling-for-airline-indemnities","",{"@graph":36,"@context":85},[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/legal-actions-in-brazilian-air-transport-a-machine-learning-and-multinomial-logistic-regression-analysis-predictive-modeling-for-airline-indemnities/123603/",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",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem do the authors address in Brazilian air transport?","Question",{"text":75,"@type":76},"The work addresses the harmful impact of the number of lawsuits filed against airlines, which can affect operations, competition, and legal uncertainty while driving judicial indemnities.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are evaluated in the study?",{"text":80,"@type":76},"Naive Bayes, Random Forest, Support Vector Machines, and Multinomial Logistic Regression are evaluated.",{"name":82,"@type":73,"acceptedAnswer":83},"What factors negatively affect indemnities according to the results?",{"text":84,"@type":76},"Flight delays, cancellations, and airline faults have a negative effect on indemnities.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":29,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]