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ORIGINAL RESEARCH article

Front. Energy Res.
Sec. Carbon Capture, Utilization and Storage
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1395814

Data-Driven Decarbonization: Optimizing P+R in Istanbul with Machine Learning Energy Modeling and ITS

Provisionally accepted
Dr. MEHMET AKİF KARTAL Dr. MEHMET AKİF KARTAL 1*Ahmet Feyzioğlu Ahmet Feyzioğlu 2
  • 1 Bandirma Onyedi Eylül University, Bandirma, Türkiye
  • 2 Marmara University, Kadikoy, Istanbul, Türkiye

The final, formatted version of the article will be published soon.

    Due 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 everincreasing 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.

    Keywords: Park and ride, Smart city, machine learning, carbon emission, Climate Change, Energy Research

    Received: 04 Mar 2024; Accepted: 30 Jul 2024.

    Copyright: © 2024 MEHMET AKİF KARTAL and Feyzioğlu. 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) or licensor 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.

    * Correspondence: Dr. MEHMET AKİF KARTAL, Bandirma Onyedi Eylül University, Bandirma, Türkiye

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.