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This report presents a machine learning approach for predicting open parking vacancies to improve space utilization and reduce congestion. The model applies support vector regression (SVR) and a custom YOLOv4-based object detector to collect training data by identifying empty and occupied spaces. Weather, lighting, and obstacle noise are considered in detection, followed by additional weather features and hyperparameter tuning (grid search, random search, random search plus, parameter optimization loop).",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/predicting-open-space-parking-vacancies-using-machine-learning/290397/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/predicting-open-space-parking-vacancies-using-machine-learning/290397.png","ImageObject",300,407,{"name":92,"@type":93},"Eliana","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",13,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why is predicting open parking vacancies needed in urban areas?","Question",{"text":113,"@type":114},"Urban parking faces an imbalance between supply and demand, and increasing parking supply alone no longer solves the problem effectively.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does the system gather data for training the prediction model?",{"text":118,"@type":114},"A custom object detector based on YOLOv4 is used to collect training data by detecting empty and occupied parking spaces.",{"name":120,"@type":111,"acceptedAnswer":121},"Which algorithms and tuning methods are used to build and improve the SVR prediction model?",{"text":122,"@type":114},"SVR is compared with linear regression and decision tree regressor, and the SVR model is improved using hyperparameter tuning methods including grid search, random search, random search plus, and a parameter optimization loop.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290397,1790509295,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":146,"preview_page_count":4},4398048949847,"https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267","PREDICTING OPEN SPACE PARKING VACANCIES USING MACHINE  \nLEARNING  \nBy  \nLee Wei Jun  \nA REPORT  \nSUBMITTED TO  \nUniversiti Tunku Abdul Rahman  \nin partial fulfillment of the requirements  \nfor the degree of  \nBACHELOR OF INFORMATION SYSTEMS (HONOURS) INFORMATION SYSTEMS ENGINEERING Faculty of Information and Communication Technology  \n(Kampar Campus)  \nJAN 2023  \nREPORT STATUS DECLARATION FORM  \nUNIVERSITI TUNKU ABDUL RAHMAN  \nREPORT STATUS DECLARATION FORM  \nTitle: _Predicting Open Space Parking Vacancies using Machine Learning    \n__________________________________________________________  \n__________________________________________________________  \nAcademic Session:   January 2023   \nI   LEE WEI JUN    \n(CAPITAL LETTER)  \ndeclare that I allow this Final Year Project Report to be kept in  \nUniversiti Tunku Abdul Rahman Library subject to the regulations as follows:  \n1. The dissertation is a property of the Library.  \n2. The Library is allowed to make copies of this dissertation for academic purposes.  \nVerified by,  \n(Supervisor’s signature)  \n  Ms Tseu Kwan Lee    \nSupervisor’s name  \nDate:    26 APRIL 2023 _  \nFYP THESIS SUBMISSION FORM  \n\n| Universiti Tunku Abdul Rahman |  |  |  |\n| --- | --- | --- | --- |\n| Form Title : Sample of Submission Sheet for FYP/Dissertation/Thesis |  |  |  |\n| Form Number: FM-IAD-004 | Rev No.: 0 | Effective Date: 21 JUNE 2011 | Page No.: 1 of 1 |\n\nFACULTY OF INFORMATION AND COMMUNICATION TECHNOLOGY  \nUNIVERSITI TUNKU ABDUL RAHMAN  \nDate:   26 APRIL 2023   \nSUBMISSION OF FINAL YEAR PROJECT /DISSERTATION/THESIS  \nIt is hereby certified that  Lee Wei Jun    (ID No:  19ACB03389  ) has completed this final year project/ dissertation/ thesis* entitled “_Predicting Open Space Parking Vacancies using Machine Learning _” under the supervision of   Ms Tseu Kwan Lee   (Supervisor) from the Department of Computer Science, Faculty of Information and Communication Technology.  \nI understand that University will upload softcopy of my final year project / dissertation/ thesis* in pdf format into UTAR Institutional Repository, which may be made accessible to UTAR community and public.  \nYours truly,  \n(LEE WEI JUN)  \nDECLARATION OF ORIGINALITY  \nI declare that this report entitled “PREDICTING OPEN SPACE PARKING VACANCIES USING MACHINE LEARNING” is my own work except as cited in the references. The report has not been accepted for any degree and is not being submitted concurrently in candidature for any degree or other award.  \nSignature :      \nName :   LEE WEI JUN    \nDate :   26-4-2023   \nACKNOWLEDGEMENTS  \nI would like to express my sincere thanks and appreciation to my supervisors, Ms Tseu Kwan Lee who has given me this bright opportunity to engage in Green Software, Sustainability and Machine Learning field studies. When I was facing problems in this project, the advice from them always assists me in overcoming the problems. Again, a million thanks to my supervisor.  \nTo a very special person in my life, my mother, Lim Saw Hoon, for her patience, unconditional support, and love, and for standing by my side during hard times. Finally, I must say thanks to my parents and my family for their love, support, and continuous encouragement throughout the course.  \nA finalised report paper from FYP1 named “Predicting Open Parking Space using Deep Learning and Support Vector Regression” had submitted to ISMSI 2023 (Paper ID: MS029) . A screenshot of the acceptance notification of the full paper (ISMSI 2023 Acceptance Notification of Full Paper) is attached under the appendix.  \nABSTRACT  \nVehicle parking has become a significant issue in urban areas due to the imbalance between supply and demand for parking spaces, and increasing the number of parking spaces is no longer an effective solution. Predicting open parking vacancies using machine learning is a practical and effective solution to overcome parking issues. The ability to predict parking availability maximizes parking space utilization, ultimately alleviat","cbCaisoNiOgbok8i","https://ap.wps.com/l/cbCaisoNiOgbok8i","pdf",4483728,143,"English","# Chapter 1 INTRODUCTION\n## 1.1 Problem Statement and Motivation\n## 1.2 Research Objectives\n## 1.3 Project Scope and Direction\n## 1.4 Contributions\n## 1.5 Report Organization\n# CHAPTER 2 LITERATURE REVIEW\n## 2.1 Previous works on Machine Learning\n## 2.2 Previous works on Deep Learning\n## 2.3 Limitations of Previous Studies\n## 2.4 Proposed","[{\"question\":\"Why is predicting open parking vacancies needed in urban areas?\",\"answer\":\"Urban parking faces an imbalance between supply and demand, and increasing parking supply alone no longer solves the problem effectively.\"},{\"question\":\"How does the system gather data for training the prediction model?\",\"answer\":\"A custom object detector based on YOLOv4 is used to collect training data by detecting empty and occupied parking spaces.\"},{\"question\":\"Which algorithms and tuning methods are used to build and improve the SVR prediction model?\",\"answer\":\"SVR is compared with linear regression and decision tree regressor, and the SVR model is improved using hyperparameter tuning methods including grid search, random search, random search plus, and a parameter optimization loop.\"}]","PREDICTING OPEN SPACE PARKING VACANCIES USING MACHINE LEARNING | PDF",1789641601,360]