[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122029-en":3,"doc-seo-122029-105":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},122029,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Accident Prediction Using Machine Learning - Analyzing Weather Conditions and Model Performance","The transportation industry faces higher risk when accidents occur in remote areas and late rescue efforts increase mortality. This master’s thesis explores machine learning methods to forecast accident likelihood while focusing on how weather and road conditions shape both the occurrence and severity of collisions. The study identifies the most influential factors using an extensive literature review and evaluates multiple predictive models. Trained on real traffic-collision data, the models are assessed with accuracy, precision, recall, and F1-score, producing actionable insights for data-driven prevention and safety planning.","Accident Prediction Using Machine Learning: Analyzing Weather Conditions, and Model Performance  \nUniversity Of Oulu Faculty of InformationTechnology and Electrical Engineering Master’s Thesis Muhamad Shahroz Abbas 30th May 2023  \nAcknowledgement  \nIt is with immense gratitude that I acknowledge the support and help of those whose efforts have made it possible for me to complete this thesis. The journey, though challenging, has been rewarding and enlightening, and I owe a deep debt of gratitude to everyone who has played a part in this process. First and foremost, I wish to express my sincere thanks to the University of Oulu, for providing a conducive environment that fostered learning, growth, and the pursuit of knowledge. The enriching experiences I've had here in the Information Processing Science and Software Engineering program have been invaluable and will guide me throughout my future career.  \nMy thesis on \"Predicting Accidents\" stands as a testament to the mentorship and guidance of my supervisors, Dr. Ella Peltonen and Tero Päivärinta. Their profound expertise in Artificial Intelligence and Machine Learning has been crucial in navigating the complexities of this research. Their patient guidance, constructive criticisms, and unwavering faith in my potential have continually driven me towards excellence. The intellectual challenges they posed along this journey have fostered my critical thinking abilities and spurred intellectual growth. They were always there to assist and support me. I kept advancing and getting better because to her support. They not only assisted me with the research for this thesis, but also taught me how to do research and approach problems in a scientific manner. For this, I am deeply grateful.  \nI am also thankful for the support of my fellow students and friends, especially Numan Akbar who have been a constant source of encouragement, companionship, and inspiration. Their supportive words and actions, especially during challenging periods, have been instrumental in keeping me focused and motivated. A heartfelt thanks goes tomy family, whose unwavering support and belief in me have been the bedrock upon which I stand. Their sacrifices, patience, and love have been my strength throughout this journey.  \nFinally, I am deeply thankful to all the researchers and authors whose works have been referred to in this thesis. Their significant contributions to the field of artificial intelligence have not only formed the foundation of this work but also spurred my interest and learning in the subject. To all of you, I offer my sincere thanks and appreciation. Your collective effort has made this journey not only possible but also a truly enriching experience.  \nAbstract  \nThe transportation industry has undergone a technological revolution. The necessity of traveling safely, staying informed, and being updated has multiplied. People may rely on modern vehicles with the newest technology for reliability and safety. However, a tense scenario is made worse by late rescue efforts at accident place, which also significantly raises the accident mortality rate in remote places. This thesis investigates the use of machine learning approaches to forecast the likelihood of accidents, with a particular emphasis on understanding the impact that weather and road conditions have in determining the severity of accidents. Specifically, this thesis investigates how weather and road conditions affect the likelihood of accidents occurring. The purpose of the study is to contribute to the development of accident prevention techniques that are more effective and data-driven by determining the most influential elements that lead to accidents and evaluating the effectiveness of various machine learning models in properly predicting accident likelihood. This will be accomplished by identifying the factors that are most likely to lead to accidents. An exhaustive examination of the relevant literature was carried to determine the ","cbCaimNHac1LUi4l","https://ap.wps.com/l/cbCaimNHac1LUi4l","pdf",1315561,1,66,"English","en",105,"# Introduction\n# Literature review\n## Comparative Analysis of ML Models For Accident Predictions\n## Weather and its effects on road accidents\n## Statistical techniques","[{\"question\":\"What is the main goal of the thesis?\",\"answer\":\"To forecast accident likelihood using machine learning and to understand how weather and road conditions influence accident severity and occurrence.\"},{\"question\":\"What data and evaluation metrics are used?\",\"answer\":\"Models are trained and evaluated on a dataset of actual traffic collisions, using accuracy, precision, recall, and F1-score to measure performance.\"},{\"question\":\"What do the results suggest about machine learning for accident prediction?\",\"answer\":\"With sufficient data and carefully selected features, machine learning models can accurately predict accident likelihood and provide insights into the relationships between environmental conditions and severity.\"}]","Accident Prediction Using Machine Learning - 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