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Fuzzy Logic Applications in Traffic and Transportation Engineering by Momčilo Dobrodolac, Stefan Jovčić, Marjana Čubranić-Dobrodolac ISBN 9783725840229, 9783725840212, 3725840229, 3725840210 instant download

  • SKU: EBN-236129900
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Authors:Momčilo Dobrodolac, Stefan Jovčić, Marjana Čubranić-Dobrodolac
Pages:244 pages
Year:2025
Publisher:MDPI
Language:english
File Size:19.07 MB
Format:pdf
ISBNS:9783725840229, 9783725840212, 3725840229, 3725840210
Categories: Ebooks

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Fuzzy Logic Applications in Traffic and Transportation Engineering by Momčilo Dobrodolac, Stefan Jovčić, Marjana Čubranić-Dobrodolac ISBN 9783725840229, 9783725840212, 3725840229, 3725840210 instant download

Traffic and transportation engineering involves the application of scientific principles to the planning, design, and operation of facilities for any mode of transportation and to human resource management in industry, which are required to enable the safe, efficient, economical, and environmentally compatible movement of people and goods. 
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Given the complexity of transportation engineering processes, fuzzy logic is a convenient tool for modeling these processes. Fuzzy logic, based on approximate human-like reasoning, offers diverse applications in transportation, a field marked by constant changes, uncertainty, and imprecision. A particular advantage of fuzzy systems is the ability to include multiple goals in calculations and, using adequate optimization algorithms, to achieve high similarity to real-world phenomena. 
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This Special Issue of Mathematics, titled “Fuzzy Logic Applications in Traffic and Transportation Engineering”, is devoted to examples of implementing fuzzy logic to solve various traffic and transportation engineering problems for all modes, including road, rail, air, waterborne transport, postal, and logistics industries, as well as telecommunications. 
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The Guest Editors were inspired by the current gap in the literature regarding this topic and the significant potential of fuzzy logic for applications within the field of transportation. Their previous research involved modeling driver behavior using fuzzy inference systems, optimizing them with metaheuristic algorithms, and incorporating fuzzy sets into decision-making theory.
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