Urban areas worldwide grapple with the persistent issue of traffic management. Various intelligent techniques has emerged to tackle this problem, but they often rely on costly traffic lights and struggle with emergency situations. This research introduces three innovative: (i) Deep Mutual Exclusion Algorithm based on Single Instruction (D-MEASIR), (ii) D-Mutual Exclusion Algorithm based on optimal path (D-MEAPRI), and (iii) Deep Mutual Exclusion Algorithm based on Multi-Agent Systems (D-MEAMAS). These algorithms facilitate management within a group through a queue structure using traffic prediction dataset, employing external elements like routers for internal communication. Beyond presenting experimental and simulation outcomes, the article conducts a comprehensive statistical analysis, comparing the efficiency of D-MEASIR, D-MEAPRI, and D-MEAMAS with existing alternatives. Finally, these algorithms demonstrate remarkable efficiency while maintaining a CC of O(n) for accessing critical sections.
Revolutionizing Urban Traffic Management: IoT-Driven Algorithms for Intelligent Transportation Systems
Int. J. ITS Res.
International Journal of Intelligent Transportation Systems Research ; 23 , 2 ; 648-675
01.08.2025
28 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
D-MEASIR , Routers , Traffic congestion , D-MEAMAS , D-MEAPRI Automotive Engineering , Civil Engineering , Electrical Engineering , Information and Computing Sciences , Engineering , Artificial Intelligence and Image Processing , User Interfaces and Human Computer Interaction , Robotics and Automation , Computer Imaging, Vision, Pattern Recognition and Graphics
REVOLUTIONIZING SPACE TRAFFIC MANAGEMENT
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