Digital Twins for Traffic Congestion in Smart Cities: A Novel Solution Using Data Mining Techniques

Arianna Anniciello, Simona Fioretto, Elio Masciari, Enea Napolitano

2023

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.

Paper Citation

in Harvard Style

Anniciello A., Fioretto S., Masciari E. and Napolitano E. (2023). Digital Twins for Traffic Congestion in Smart Cities: A Novel Solution Using Data Mining Techniques. In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS; ISBN 978-989-758-671-2, SciTePress, pages 241-248. DOI: 10.5220/0012208100003598

in Bibtex Style

@conference{kmis23,
author={Arianna Anniciello and Simona Fioretto and Elio Masciari and Enea Napolitano},
title={Digital Twins for Traffic Congestion in Smart Cities: A Novel Solution Using Data Mining Techniques},
booktitle={Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS},
year={2023},
pages={241-248},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012208100003598},
isbn={978-989-758-671-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS
TI - Digital Twins for Traffic Congestion in Smart Cities: A Novel Solution Using Data Mining Techniques
SN - 978-989-758-671-2
AU - Anniciello A.
AU - Fioretto S.
AU - Masciari E.
AU - Napolitano E.
PY - 2023
SP - 241
EP - 248
DO - 10.5220/0012208100003598
PB - SciTePress