[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121078-en":3,"doc-seo-121078-105":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},121078,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Data-driven decarbonization: Optimizing P+R in Istanbul with machine learning energy modeling and ITS","Migration-driven congestion and rapidly rising emissions in metropolitan areas motivate data-driven solutions that integrate mobility and energy planning. This study investigates Intelligent Transportation Systems and the park-and-ride application, focusing on Istanbul and comparing its investment rationale with global practices. Predictions for P+R application and energy consumption across 1–24 months are produced with machine learning, then linked to carbon emissions and greenhouse-gas impacts. The results support evidence-based planning, greener environments, and climate-change research.","TYPE Original Research PUBLISHED 20 August 2024  \nDOI 10.3389/fenrg.2024.1395814  \nOPEN ACCESS  \nEDITED BY  \nHailong Li,  \nCentral South University, China  \nREVIEWED BY  \nElżbieta Macioszek,  \nSilesian University of Technology, Poland Magdalena Klimczuk-Kochańska, University of Warsaw, Poland  \n*CORRESPONDENCE  \nMehmet Akif Kartal,  \n [mkartal@bandirma.edu.tr](mkartal@bandirma.edu.tr)  \nRECEIVED 04 March 2024  \nACCEPTED 30 July 2024  \nPUBLISHED 20 August 2024  \nCITATION  \nKartal MA (2024) Data-driven decarbonization: Optimizing P+R in Istanbul with machine learning energy modeling and ITS.  \nFront. Energy Res. 12:1395814 .  \ndoi: 10.3389/fenrg.2024.1395814  \nCOPYRIGHT  \n© 2024 Kartal. 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.  \nData-driven decarbonization: Optimizing P+R in Istanbul with machine learning energy modeling and ITS  \nMehmet Akif Kartal*  \nBandırma Onyedi Eylül University, Distance Education Application and Research Center, Balıkesir, Türkiye  \nDue to the rapidly developing technologies, fast and practical solutions are offered to the problems encountered in daily life. Metropolitan cities are greatly affected by the ever-increasing population and migrations to big cities, the increase in production with the economy and job opportunities. At this point, with the introduction of smart transportation systems, fast and effortless solutions can be produced by saving time and space. City life can be facilitated by applying more efficient and rational solutions with smart transportation systems. In this study, it is aimed to investigate information about the Intelligent Transportation Systems and one of its applications, park and ride, which has created a significant agenda within the scope of transportation engineering in the recent past, and to provide information about the investments made by examining the application for Istanbul along with its various applications in the world. Some suggestions will be made by emphasizing the importance of the park and Ride smart city application for Istanbul. In conclusion, predictions of P + R application and energy consumption in periods of 1–24 months were made through machine learning. By obtaining energy consumption data thanks to machine learning, carbon gas emissions and its effects on greenhouse gases were also examined. It can be thought that by obtaining energy consumption data for the long term thanks to machine learning, it can make significant contributions to future investments, green environment-green world, and climate change studies.  \nKEYWORDS  \npark and ride, smart city, machine learning, carbon emission, climate change, energy research  \n1 Introduction  \nMigration to metropolises and rapid population growth, lack of infrastructure and the increasing number of vehicles on the road and rapid consumption in proportion to the increase in people’s income levels; It brought along problems such as transportation problems, traffic congestion and increased carbon emissions. In Istanbul, which is a cosmopolitan city, it emerges as a priority solution area to encourage the public transportation sector in cities where the demand for the road exceeds the capacity, travel times are prolonged, delays to the desired destination are experienced, and it is difficult to find a parking space.  \nThere is a need for smart transportation applications that work in an integrated way that combines more than one type of transportation in order to prevent traffic density, parking space, accumulation and loss of time. At this point, with the use of the “Park and Ride”  \nFrontiers in Energy Researc","cbCaisNHcAb80fpm","https://ap.wps.com/l/cbCaisNHcAb80fpm","pdf",4229293,1,19,"English","en",105,"# Introduction\n## Park and Ride as a Smart Transportation Solution\n## Study Scope and Istanbul Case Focus\n# Conclusion","[{\"question\":\"Why is park and ride considered a priority solution for Istanbul?\",\"answer\":\"It helps commuters park outside the city center and shift to public transport, reducing traffic, confusion, travel delays, and associated carbon emissions.\"},{\"question\":\"What role does machine learning play in this study?\",\"answer\":\"Machine learning is used to predict P+R-related energy consumption over 1–24 months and to derive implications for carbon emissions and greenhouse-gas effects.\"},{\"question\":\"What factors are analyzed to estimate potential energy savings?\",\"answer\":\"The study analyzes elements such as waiting times, travel times, and P+R usage patterns to estimate energy savings from implementing P+R systems.\"}]","Data-driven decarbonization: Optimizing P+R in Istanbul with machine learning energy modeling and ITS | 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