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Smart city article details

Title	Digital Twins For Traffic Congestion In Smart Cities: A Novel Solution Using Data Mining Techniques
ID_Doc	20296
Authors	Anniciello A.; Fioretto S.; Masciari E.; Napolitano E.V.
Year	2023
Published	International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K - Proceedings, 3
DOI	http://dx.doi.org/10.5220/0012208100003598
Abstract	This article serves as a position paper that explores the complex issue of traffic management in smart cities and the challenges it presents. The problem of urban traffic is particularly relevant in our modern world, where more and more people are moving to urban environments, leading to congestion, pollution and reduced quality of life. To address this challenge, we propose an innovative methodology based on Digital Twins. The paper proposes an extended approach that integrates Digital Twins with other existing techniques such as Trajectory Mining, Process Mining, and Decision Making. These techniques, which combine motion data, process analysis, and data-driven Decision Making, can enrich the Digital Twin model, provide a deeper understanding of traffic flows, and deliver more targeted and effective traffic management solutions. This proposal represents a significant step forward in the search for innovative and sustainable solutions for urban traffic management, and lays the foundation for further research and development in this critical area. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda.
Author Keywords	Data Mining; Digital Twins; Smart Cities

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