Authors:
Arianna Anniciello
;
Simona Fioretto
;
Elio Masciari
and
Enea Napolitano
Affiliation:
Department of Electrical and Information Technology Engineering, University of Naples Federico II, Italy
Keyword(s):
Smart Cities, Digital Twins, Data Mining.
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 found
ation for further research and development in this critical area.
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