[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118563-en":3,"doc-seo-118563-105":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},118563,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Improving Parking Availability Data by Utilizing Machine Learning","The thesis addresses incomplete parking availability data in the city of Turku caused by multiple parking permits, such as disabled parking and resident permits. It develops an improved data approach by combining two datasets and evaluating two machine learning models to enhance accuracy and produce more realistic real-time availability status. Dataset integration is implemented in Python using Pandas and GeoPandas to create a unified dataset. Model comparison in different categories supports selecting an iterative self-learning method using AutoML, intended as a foundation for future model building.","Bachelor’s thesis  \nBusiness Information Technology 2025  \nIitu Kuusrainen  \nImproving Parking Availability Data by Utilizing Machine Learning  \nBachelor’s Thesis | Abstract  \nTurku University of Applied Sciences Business Information Technology 2025 | 23 pages, 1 appendix  \nIitu Kuusrainen  \nImproving Parking Availability Data by Utilizing Machine Learning  \nThe current parking availability data in the city of Turku is incomplete due to various parking permits, such as disabled parking permits and resident permits. The aim of this thesis was to improve parking availability data. This was achieved by combining two different datasets and comparing two different machine learning models. The work was commissioned by the City of Turku.  \nThe datasets were combined using the Python programming language, utilizing the Pandas and GeoPandas libraries. The result of the combination was a new dataset that considers data from both sources and therefore provides more accurate parking data. After the combination, machine learning models could be compared. In this work, an iterative self-learning model and a prediction coefficient model were compared in three different categories.  \nThe result of the comparison was the selection of the iterative self-learning model, specifically the AutoML technique. This work is intended to serve as a basis for building a machine learning model.  \nKeywords:  \nmachine learning, artificial intelligence, Python, parking  \nOpinnäytetyö (AMK) | Tiivistelmä Turun ammattikorkeakoulu Tietojenkäsittely  \n2025 | 23 sivua, 1 liitesivu  \nIitu Kuusrainen  \nPysäköinnin saatavuusdatan parantaminen koneoppimista hyödyntämällä  \nNykyinen Turun kaupungin pysäköinnin saatavuusdata on epätäydellistä erilaisten pysäköintilupien takia, kuten liikuntaesteisten pysäköintilupien ja asukaslupien. Tämän opinnäytetyön tavoitteena oli parantaa pysäköinninsaatavuusdataa. Tämä saavutettiin yhdistämällä kaksi datasettiä ja vertailemalla kahta eri koneoppimismallia. Työ toteutettiin toimeksiantona Turun kaupungille.  \nDatasettien yhdistäminen tapahtui Python–ohjelmointikielellä hyödyntäen Pandas– ja GeoPandas–kirjastoja. Yhdistämisen tuloksena syntyi uusi datasetti, joka ottaa huomioon molemmista lähteistä saatavan datan. Yhdistämisen jälkeen pystyttiin vertailemaan koneoppimismalleja. Tässä työssä verrattiin iteratiivista itseoppivaa mallia sekä ennustekerroinmallia. Vertailutehtiin kolmessa eri kategoriassa.  \nDatasettien yhdistämisen jälkeen tuloksena oli uusi datasetti, joka ottaa huomioon molemmista lähteistä saatavan datan. Vertailun tuloksena päädyttiiniteratiiviseen itseoppivaan malliin ja tarkemmin koneoppimistekniikkaan AutoML. Tätä työtä on tarkoitus käyttää pohjana koneoppimismallin rakentamiseen.  \nAsiasanat:  \nkoneoppiminen, tekoäly, Python, pysäköinti  \nContents  \nList of abbreviations 6  \n1 Introduction 7  \n2 Parkkihub 9  \n3 Django 10  \n4 Machine learning 11  \n4.1 Iterative Self-Learning Model 12  \n4.2 Predictive Model with Annual Training 14  \n5 Datasets in Parkkihub 16  \n6 Comparison of AutoML and LSTM 18  \n6.1 Performance 18  \n6.2 Complexity 19  \n6.3 Practical Considerations 19  \n6.4 Findings 20  \n7 Conclusion 21  \nReferences 22  \nAppendices  \nAppendix 1. Code for combining the datasets  \nFigures  \nFigure 1. Machine Learning Iterative Process (Kasture, 2020) . 13 Figure 2. Long Short-Term Memory Networks (Kienzler, 2024) . 15  \nFigure 3. How the datasets are combined. 17  \nList of abbreviations  \nAutoML  \nRNN  \nLSTM  \nPython  \nPandas  \nGeoPandas  \nWGS84  \nAutomated Machine Learning  \nRecurrent Neural Networks  \nLong Short-Term Memory  \na programming language  \na Python library  \na Python library for handling geographic data World Geodetic System  \n1 Introduction  \nAccurate parking availability data is crucial for efficient urban mobility and effective parking management. However, the City of Turku , Finland faces challenges in providing reliable real-time information about available parking spots. These challenges stem f","cbCaiauvlTd3Txlo","https://ap.wps.com/l/cbCaiauvlTd3Txlo","pdf",793054,1,25,"English","en",105,"# Introduction\n# Parkkihub\n# Django\n# Machine learning\n## Iterative Self-Learning Model\n## Predictive Model with Annual Training\n# Datasets in Parkkihub\n# Comparison of AutoML and LSTM\n## Performance\n## Complexity\n## Practical Considerations\n## Findings\n# Conclusion\n# References\n# Appendices","[{\"question\":\"Why is Turku’s parking availability data currently incomplete?\",\"answer\":\"The data is incomplete because it depends on a single source and does not properly account for different parking permits, such as disabled and resident permits, which are not recorded electronically.\"},{\"question\":\"How does the thesis improve the parking availability dataset?\",\"answer\":\"It combines two datasets using Python with Pandas and GeoPandas, producing a new dataset that incorporates information from both sources for more accurate availability data.\"},{\"question\":\"Which machine learning model is selected and what is the reason?\",\"answer\":\"The iterative self-learning model using AutoML is selected based on the comparison results against another predictive model across multiple categories, and it is intended as a basis for building further machine learning solutions.\"}]","Improving Parking Availability Data by Utilizing Machine Learning | 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is Turku’s parking availability data currently incomplete?","Question",{"text":75,"@type":76},"The data is incomplete because it depends on a single source and does not properly account for different parking permits, such as disabled and resident permits, which are not recorded electronically.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the thesis improve the parking availability dataset?",{"text":80,"@type":76},"It combines two datasets using Python with Pandas and GeoPandas, producing a new dataset that incorporates information from both sources for more accurate availability data.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model is selected and what is the reason?",{"text":84,"@type":76},"The iterative self-learning model using AutoML is selected based on the comparison results against another predictive model across multiple categories, and it is intended as a basis for building further machine learning 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