[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121187-en":3,"doc-seo-121187-105":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},121187,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Predictive Analysis of Traffic Alerts and Jams in Oeste CIM - A Machine Learning Approach","Traffic congestion significantly affects urban mobility, transportation efficiency, and public safety. This thesis presents a predictive analysis of traffic alerts and traffic jams in the Oeste Intermunicipal Community (CIM) using machine learning methods. Crowdsourced Waze data is leveraged to train and compare Random Forest, MultiLayer Perceptron, and XGBoost models using spatiotemporal and meteorological variables. The workflow follows CRISP-DM, including preprocessing, exploratory analysis, feature selection, model development, and evaluation. Results show XGBoost achieves the best performance.","MGI  \nMaster Degree Program in  \nInformation Management  \nPredictive Analysis of Traffic Alerts and Jams in Oeste CIM  \nA Machine Learning Approach  \nFilipe José Ferreira Alves  \nMaster Thesis  \npresented as partial requirement for obtaining the Master Degree in Information Management  \nNOVA Information Management School Instituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nNOVA Information Management School Instituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nPredictive Analysis of Traffic Alerts and Jams in Oeste CIM  \nA Machine Learning Approach  \nby  \nFilipe José Ferreira Alves  \nMaster Thesis presented as partial requirement for obtaining the Master’s degree in Information Management, with a specialization in Knowledge Management and Business Intelligence  \nSupervisor: Bruno Jardim, PhD, NOVA Information Management School  \nSTATEMENT OF INTEGRITY  \nI hereby declare having conducted this academic work with integrity. I confirm that I have not used plagiarism or any form of undue use of information or falsification of results along the process leading to its elaboration. I further declare that I have fully acknowledged the Rules of Conduct and Code of Honor from the NOVA Information Management School.  \nFilipe José Ferreira Alves Lisbon, 15th July, 2024  \nACKNOWLEDGEMENTS  \nTo my supervisor, Professor Bruno Jardim, for his support, guidance, and encouragement throughout this project. His expertise was essential for the completion of this work.  \nI would also like to acknowledge the support of my parents and sister throughout my entire master's degree. Their encouragement made this program achievable.  \nLast, but not least, to my girlfriend Susana, for all the patience during the challenging moments, for believing in me and the constant love and support provided.  \nABSTRACT  \nTraffic congestion is a pervasive issue in urban areas, impacting transportation efficiency and public safety. This thesis explores the predictive analysis of traffic alerts and jams in the Oeste Intermunicipal Community using machine learning techniques. Leveraging crowdsourced data from the Waze application, distinct machine learning models, including Random Forest, MultiLayer Perceptron, and XGBoost, are employed and compared to determine the most effective approach for predicting traffic congestion and alerts based on spatiotemporal and meteorological data. The methodology follows the CRISP-DM framework, encompassing data preprocessing, exploratory data analysis, feature selection, model building, and evaluation. The results demonstrate that XGBoost outperforms the other models when comparing the metrics for weighted F1-score and balanced accuracy, achieving, respectively, 85.55% and 74.12% on alerts prediction, and scoring 59.02% and 63.56% on jams prediction.  \nKEYWORDS  \nSmart Region; Machine Learning; Traffic Prediction; Intelligent Transport Systems;  \nClassification Problem  \nSustainable Development Goals (SDG):  \nTABLE OF CONTENTS  \n1. Introduction .................................................................................................................. 1  \n1.1. Motivation ............................................................................................................. 1  \n1.2. Objectives and Methodology ................................................................................2  \n2. Literature Review .........................................................................................................3  \n2.1. Crowdsource Data .................................................................................................3  \n2.2. Intelligent Transportation Systems .......................................................................4  \n2.3. Traffic Characterization .........................................................................................5  \n2.4. Traffic Flow Prediction..........................................................","cbCaitK170Vi7nHa","https://ap.wps.com/l/cbCaitK170Vi7nHa","pdf",1349726,1,50,"English","en",105,"# 1. Introduction\n## 1.1. Motivation\n## 1.2. Objectives and Methodology\n# 2. Literature Review\n## 2.1. Crowdsource Data\n## 2.2. Intelligent Transportation Systems\n## 2.3. Traffic Characterization\n## 2.4. Traffic Flow Prediction\n## 2.5. Alerts Prediction\n# 3. Methodology\n## 3.1. Business Understanding\n## 3.2. Data Understanding\n## 3.3. Initial Exploratory Data Analysis\n## 3.4. Data Preprocessing\n## 3.5. Data Preparation\n## 3.6. 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