{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T20:53:43Z","timestamp":1786740823491,"version":"build-2736575974"},"reference-count":42,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T00:00:00Z","timestamp":1741651200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>This study focuses on estimating transportation system-related emissions in CO2 eq., considering several socioeconomic and energy- and transportation-related input variables. The proposed approach incorporates artificial neural networks, machine learning, and deep learning algorithms. The case of Turkey was considered as an example. Model performance was evaluated using a dataset of Turkey, and future projections were made based on scenario analysis compatible with Turkey\u2019s climate change mitigation strategies. This study also adopted a transportation type-based analysis, exploring the role of Turkey\u2019s road, air, marine, and rail transportation systems. The findings of this study indicate that the aforementioned models can be effectively implemented to predict transport emissions, concluding that they have valuable and practical applications in this field.<\/jats:p>","DOI":"10.3390\/systems13030194","type":"journal-article","created":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T06:52:30Z","timestamp":1741675950000},"page":"194","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Estimation of CO2 Emissions in Transportation Systems Using Artificial Neural Networks, Machine Learning, and Deep Learning: A Comprehensive Approach"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8248-8924","authenticated-orcid":false,"given":"Seval","family":"Ene Yal\u00e7\u0131n","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering, Bursa Uluda\u011f University, G\u00f6r\u00fckle Campus, 16059 Bursa, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"133310","DOI":"10.1016\/j.jclepro.2022.133310","article-title":"Optimal selection of predictors for greenhouse gas emissions forecast in Hong Kong","volume":"370","author":"Ho","year":"2022","journal-title":"J. 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