[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123292-en":3,"doc-seo-123292-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},123292,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Assessing smart cities’e􀀀ffectiveness: machine learning approaches","Smart city initiatives are expanding globally, yet scholars and city leaders lack consensus on how smart cities should be perceived and developed, and evaluation quality indicators remain insufficient. This research identifies drivers shaping residents’ assessments of life quality and comfort by combining survey-based resident evaluations in priority problematic areas with measures of technological development. Machine-learning models built in RapidMiner Studio predict the city Human Development Index (HDI) and determine the most influential drivers. Model comparison selects the optimal Fast Large Margin approach, using an international dataset covering 141 smart cities across 73 countries.","TYPE Original Research PUBLISHED 22 May 2025  \nDOI 10. 3389/frsc.2025.1400917  \nOPEN ACCESS  \nEDITED BY  \nNitin Goyal,  \nCentral University of Haryana, India  \nREVIEWED BY  \nIvan Izonin,  \nLviv Polytechnic National University, Ukraine Thomas W. Sanchez,  \nTexas A and M University, United States  \n*CORRESPONDENCE  \nOleh Berezsky  \n [ob@wunu.edu.ua](ob@wunu.edu.ua)  \nRECEIVED 14 March 2024  \nACCEPTED 30 April 2025  \nPUBLISHED 22 May 2025  \nCITATION  \nBerezsky O, Kovalchuk O, Berezka K and Ivanytskyy R (2025) Assessing smart cities’e􀀀ectiveness: machine learning approaches. Front. Sustain. Cities 7:1400917 .  \ndoi: 10.3389/frsc.2025.1400917  \nCOPYRIGHT  \n© 2025 Berezsky, Kovalchuk, Berezka and Ivanytskyy. 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.  \nAssessing smart cities’e􀀀ectiveness: machine learning approaches  \nOleh Berezsky1*, Olha Kovalchuk2 , Kateryna Berezka3 and Roman Ivanytskyy4  \n1 Department of Computer Engineering, West Ukrainian National University, Ternopil, Ukraine,  \n2 Department of Law Theory and Constitutionalism, West Ukrainian National University, Ternopil, Ukraine, 3 Department of Applied Mathematics, West Ukrainian National University, Ternopil, Ukraine, 4 Department of Informatics and Methods of its Teaching, Ternopil Volodymyr Hnatiuk National Pedagogical University, Ternopil, Ukraine  \nAmid the global emergence of smart cities, there exists a lack of consensus among scholars and city leaders regarding their perception and development. Notably, there is a dearth of quality indicators for evaluating the progress of smart city development. This study addresses this gap by focusing on identifying the drivers that influence residents’ assessments of life quality and comfort. By gathering assessments from residents in priority areas identified as problematic for city prosperity, and incorporating basic measures of technological development, machine-learning models were constructed using RapidMiner Studio. These models aim to predict the Human Development Index (HDI) of the city and discern the most impactful drivers related to citizens’life satisfaction. The research compares various models, ultimately selecting the optimal Fast Large Margin model. The findings highlight crucial concerns for residents, including air pollution, recycling, basic amenities, and health services. The study relies on a unique dataset comprising o􀀈cial statistical information from 141 smart cities across 73 countries. The developed modelso􀀀er valuable insights for decision-makers, enabling the formulation of e􀀀ective strategies for sustainable smart city development and the enhancement of digitalization policies.  \nKEYWORDS  \nsmart city, sustainable development, performance evaluation, decision-making, machine learning, Fast Large Margin model  \n1 Introduction  \nModern key concepts and initiatives of the smart city have undergone many transformations, and are developing and improving (Kirimtat et al., 2020; José and Rodrigues, 2024) . However, today there is no agreed understanding of this phenomenon and the de􀀂nition of the term “smart city” (Camero and Alba, 2019) . It is de􀀂ned as“the convergence of technology and the city” (Yigitcanlar et al., 2018), and as “centers of economic wealth and hope for a standardized life” (Kutty et al., 2022) . No agreement has been reached on the uni􀀂ed de􀀂nition of concepts such as “smart people,”“smart living,”“smart mobility,”“smart environment,”“smart governance,”“smart economy”(Tutak and Brodny, 2023), and “citizens’ quality”(Chang and Smith, 2023) . There is no universally accepted understanding o","cbCaifFlVbiyhvw2","https://ap.wps.com/l/cbCaifFlVbiyhvw2","pdf",1926003,1,18,"English","en",105,"# Introduction\n## Definitions and lack of consensus on smart city concepts\n## Smart versus sustainable development interpretation gap\n## Ongoing global spread and policy goals of smart cities","[{\"question\":\"What problem does the study address about smart cities?\",\"answer\":\"It addresses the lack of quality indicators for evaluating smart city progress and the absence of consensus on how smart cities are perceived and developed.\"},{\"question\":\"How does the study model residents’ assessments?\",\"answer\":\"It collects residents’ evaluations in priority problematic areas, adds basic measures of technological development, and trains machine-learning models in RapidMiner Studio.\"},{\"question\":\"Which machine-learning approach is selected as optimal, and what drivers are emphasized?\",\"answer\":\"The Fast Large Margin model is selected as optimal. Key resident concerns include air pollution, recycling, basic amenities, and health services.\"}]","Assessing smart cities’e􀀀ffectiveness: machine learning approaches | PDF",1785815780,45,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"assessing-smart-citieseffectiveness-machine-learning-approaches","",{"@graph":36,"@context":86},[37,54,69],{"@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/assessing-smart-citieseffectiveness-machine-learning-approaches/123292/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-06","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the study address about smart cities?","Question",{"text":76,"@type":77},"It addresses the lack of quality indicators for evaluating smart city progress and the absence of consensus on how smart cities are perceived and developed.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the study model residents’ assessments?",{"text":81,"@type":77},"It collects residents’ evaluations in priority problematic areas, adds basic measures of technological development, and trains machine-learning models in RapidMiner Studio.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine-learning approach is selected as optimal, and what drivers are emphasized?",{"text":85,"@type":77},"The Fast Large Margin model is selected as optimal. 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