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A major improvement accelerates policy testing from about 3 hours to roughly 10 seconds, achieving evaluation results in Bilbao’s Moyua area with over 5% emission reductions and 91% machine-learning accuracy.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/a-machine-learning-approach-to-a-mobility-policy-proposal-journal-article/290782/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/a-machine-learning-approach-to-a-mobility-policy-proposal-journal-article/290782.png","ImageObject",300,407,{"name":42,"@type":43},"River Wang","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-18","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",11,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What is the main goal of the URBANITE project described in the document?","Question",{"text":63,"@type":64},"It aims to build an open-data, open-source smart-city framework that improves decision-making in European cities using simulation enhanced by service modules.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How does the machine-learning module contribute to mobility policy selection?",{"text":68,"@type":64},"It runs on simulation results to analyze large data volumes, detect patterns and trends, and identify the policy best aligned with constraints, preferences, scenarios, and key performance indicators.",{"name":70,"@type":61,"acceptedAnswer":71},"What improvement in policy testing speed is reported, and what was the evaluation area?",{"text":72,"@type":64},"Policy verification time drops from about 3 hours to around 10 seconds. 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(2023) . A machine-learning approach to a mobility policy proposal. Heliyon, 9(10), 1-15. [https://doi.org/10.1016/j.heliyon.2023.e20393](https://doi.org/10.1016/j.heliyon.2023.e20393)  \nNutzungsbedingungen:  \nDieser Text wird unter einer CC BY Lizenz (Namensnennung) zur Verfügung gestellt. Nähere Auskünfte zu den CC-Lizenzen finden Sie hier:  \n[https://creativecommons.org/licenses/by/4.0/deed.de](https://creativecommons.org/licenses/by/4.0/deed.de)  \nTerms of use:  \nThis document is made available under a CC BY Licence (Attribution). For more Information see:  \n[https://creativecommons.org/licenses/by/4.0](https://creativecommons.org/licenses/by/4.0)  \nDiese Version ist zitierbar unter / This version is citable under:  \n[https://nbn-resolving.org/urn:nbn:de:0168-ssoar-99593-2](https://nbn-resolving.org/urn:nbn:de:0168-ssoar-99593-2)  \nHeliyon 9 (2023) e20393  \nContents lists available at ScienceDirect  \nHeliyon  \n[journal homepage:](journal homepage: www.cell.com/heliyon)[ www.cell.com/heliyon](journal homepage: www.cell.com/heliyon)  \n| Research article\u003Cbr>A machine-learning approach to a mobility policy proposal Miljana Shulajkovska ∗ , Maj Smerkol, Erik Dovgan, Matjaž Gams\u003Cbr>Jožef Stefan Institute, Jamova cesta 39, SI-1000 Ljubljana, Slovenia |  | ✩ | |\n| --- | --- | --- | --- |\n| A R T I C L E I N F O\u003Cbr>Keywords: Machine learning Smart cities Mobility policy | A B S T R A C T\u003Cbr>The objective of the URBANITE project is to design an open-data, open-source, smart-city framework to enhance the decision-making processes in European cities. The framework’s basis is a robust and user-friendly simulation tool that is supplemented with several innovative service modules. One of the modules, a multi-output, machine-learning unit, is deployed on the simulation results, enabling city officials to more effectively analyse vast quantities of data, discern patterns and trends, and so facilitate advanced policy decisions. The city’s decision makers define potential city scenarios, key performance indicators, and a utility function, while the module assists in identifying the policy that is best aligned with the stipulated constraints and preferences. One of the main improvements is a speeding up of the policy testing for the decision makers, reducing the time needed for one policy verification from 3 hours to around 10 seconds. The system was evaluated for Bilbao’s Moyua area, where it suggested strategies that could result in a decrease in emissions of more than 5% 􀀂􀀃2 , 􀀄􀀃􀀅, 􀀆􀀇 in the selected area and a broader part of the city with a machine-learning accuracy of 91%. The system was therefore able to provide valuable insights into effective policies for restricting private traffic in specific districts and identifying the most advantageous times for these restrictions. |  |  |\n\n1. Introduction  \nThe rapid growth in the world’s population combined with a trend for increasing urbanisation presents modern cities with challenges. According to the United Nations, the global population is projected to increase by nearly 2 billion people in the next 30 years, with 7 out of 10 individuals residing in urban areas [1]. To address the issues associated with rapid urbanisation and achieve sustainable development, the concept of smart cities (SCs) has emerged. Numerous studies have explored the definitions of SCs [2–5], and recent advances in technology have empowered the development of SCs to enhance urban performance and improve people’s quality of life. In particular, the utilisation of Information and Communication Technologies (ICTs) in the latest research has enabled the implementation of smart strategie","cbCaiolwg4JjAzoA","https://ap.wps.com/l/cbCaiolwg4JjAzoA","pdf",3024610,16,"English","# Introduction\n## Smart-city context and decision-making\n## Mobility policy design and evaluation","[{\"question\":\"What is the main goal of the URBANITE project described in the document?\",\"answer\":\"It aims to build an open-data, open-source smart-city framework that improves decision-making in European cities using simulation enhanced by service modules.\"},{\"question\":\"How does the machine-learning module contribute to mobility policy selection?\",\"answer\":\"It runs on simulation results to analyze large data volumes, detect patterns and trends, and identify the policy best aligned with constraints, preferences, scenarios, and key performance indicators.\"},{\"question\":\"What improvement in policy testing speed is reported, and what was the evaluation area?\",\"answer\":\"Policy verification time drops from about 3 hours to around 10 seconds. The system was evaluated for Bilbao’s Moyua area.\"}]","A machine-learning approach to a mobility policy proposal - Journal article | PDF"]