Towards sustainable logistics in Turkey: A bi-objective approach to green intermodal freight transportation enhanced by machine learning

Basit Öğe Kaydı
dc.authorid0000-0001-6384-3177
dc.contributor.authorTemizceri, Fatma Talya
dc.contributor.authorKara, Selin Soner
dc.date.accessioned2026-04-04T18:55:36Z
dc.date.available2026-04-04T18:55:36Z
dc.date.issued2024
dc.departmentİstanbul Bilgi Üniversitesi
dc.description.abstractTransportation is a critical contributor to carbon emissions, with road transportation playing a dominant role due to its dense network and versatility. However, the overreliance on road transportation has led to congestion, impacting reliability. As international trade grows, the demand for sustainable logistics practices intensifies. Intermodal transportation systems have emerged as a promising solution, harnessing different modes to reduce emissions and environmental impact while optimizing costs. It is important to underscore the significance of mode combinations in achieving environmental goals, aligning with the broader concept of environmental sustainability that encompasses economic and social dimensions. This article contributes to this evolving landscape by presenting a bi-objective intermodal transportation problem focusing on carbon emission reduction. Leveraging machine learning algorithms, including multiple linear regression, support vector regression, decision tree, and random forest, we predict transportation-based CO2 emissions, offering environmentally friendly logistics plans. Our research responds to the call for green intermodal transportation, addresses financial incentives, emphasizes profit maximization, and reflects the growing influence of government policies. This paper outlines our methodology, presents a real-world case study, and offers computational results, underscoring the significance of sustainable intermodal transportation in the context of global climate goals and government initiatives.
dc.identifier.doi10.1016/j.rtbm.2024.101145
dc.identifier.doi10.1016/j.rtbm.2024.101145
dc.identifier.issn2210-5395
dc.identifier.issn2210-5409
dc.identifier.scopus2-s2.0-85194385618
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.rtbm.2024.101145
dc.identifier.urihttps://hdl.handle.net/11411/10474
dc.identifier.volume55
dc.identifier.wosWOS:001249729100001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofResearch in Transportation Business and Management
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260402
dc.snmzKA_Scopus_20260402
dc.subjectGreen Logistics
dc.subjectCo 2 Emissions
dc.subjectIntermodal Transportation Systems
dc.subjectFreight Transportation
dc.subjectMulti -Objective Optimization
dc.subjectMachine Learning
dc.titleTowards sustainable logistics in Turkey: A bi-objective approach to green intermodal freight transportation enhanced by machine learning
dc.typeArticle

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