Fuzzy Logic Applications in Traffic and Transportation Engineering
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...
Gespeichert in:
| Format: | Online |
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| Sprache: | Englisch |
| Veröffentlicht: |
MDPI - Multidisciplinary Digital Publishing Institute
2025
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| Schlagworte: | |
| Online-Zugang: | ONIX_20250812T110751_9783725840212_294 |
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| Zusammenfassung: | 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. 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. 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. 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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