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MuTraff: A Smart-City Multi-Map Traffic Routing Framework

Urban traffic routing is deemed to be a significant challenge in intelligent transportation systems. Existing implementations suffer from several intrinsic issues such as scalability in centralized systems, unnecessary complexity of mechanisms and communication in distributed systems, and lack of pr...

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Autores principales: Paricio, Alvaro, Lopez-Carmona, Miguel Angel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7720143/
https://www.ncbi.nlm.nih.gov/pubmed/31817144
http://dx.doi.org/10.3390/s19245342
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author Paricio, Alvaro
Lopez-Carmona, Miguel Angel
author_facet Paricio, Alvaro
Lopez-Carmona, Miguel Angel
author_sort Paricio, Alvaro
collection PubMed
description Urban traffic routing is deemed to be a significant challenge in intelligent transportation systems. Existing implementations suffer from several intrinsic issues such as scalability in centralized systems, unnecessary complexity of mechanisms and communication in distributed systems, and lack of privacy. These imply force intensive computational tasks in the traffic control center, continuous communication in real-time with involved stakeholders which require drivers to reveal their location, origin, and destination of their trips. In this paper we present an innovative urban traffic routing framework and reference architecture (multimap traffic control architecture, MuTraff), which is based on the strategical generation and distribution of a set of traffic network maps (traffic weighted multimaps, TWM) to vehicle categories or fleets. Each map in a TWM map set has the same topology but a different distribution of link weights, which are computed by considering policies and constraints that may apply to different vehicle groups. MuTraff delivers a traffic management system (TMS), where a traffic control center generates and distributes maps, while routing computation is performed at the vehicles. We show how this balance between generation, distribution, and routing computation improves scalability, eases communication complexities, and solves former privacy issues. Our study presents case studies in a real city environment for (a) global congestion management using random maps; (b) congestion control on road incidents; and c) emergency fleets routing. We show that MuTraff is a promising foundation framework that is easy to deploy, and is compatible with other existing TMS frameworks.
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spelling pubmed-77201432020-12-08 MuTraff: A Smart-City Multi-Map Traffic Routing Framework Paricio, Alvaro Lopez-Carmona, Miguel Angel Sensors (Basel) Article Urban traffic routing is deemed to be a significant challenge in intelligent transportation systems. Existing implementations suffer from several intrinsic issues such as scalability in centralized systems, unnecessary complexity of mechanisms and communication in distributed systems, and lack of privacy. These imply force intensive computational tasks in the traffic control center, continuous communication in real-time with involved stakeholders which require drivers to reveal their location, origin, and destination of their trips. In this paper we present an innovative urban traffic routing framework and reference architecture (multimap traffic control architecture, MuTraff), which is based on the strategical generation and distribution of a set of traffic network maps (traffic weighted multimaps, TWM) to vehicle categories or fleets. Each map in a TWM map set has the same topology but a different distribution of link weights, which are computed by considering policies and constraints that may apply to different vehicle groups. MuTraff delivers a traffic management system (TMS), where a traffic control center generates and distributes maps, while routing computation is performed at the vehicles. We show how this balance between generation, distribution, and routing computation improves scalability, eases communication complexities, and solves former privacy issues. Our study presents case studies in a real city environment for (a) global congestion management using random maps; (b) congestion control on road incidents; and c) emergency fleets routing. We show that MuTraff is a promising foundation framework that is easy to deploy, and is compatible with other existing TMS frameworks. MDPI 2019-12-04 /pmc/articles/PMC7720143/ /pubmed/31817144 http://dx.doi.org/10.3390/s19245342 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Paricio, Alvaro
Lopez-Carmona, Miguel Angel
MuTraff: A Smart-City Multi-Map Traffic Routing Framework
title MuTraff: A Smart-City Multi-Map Traffic Routing Framework
title_full MuTraff: A Smart-City Multi-Map Traffic Routing Framework
title_fullStr MuTraff: A Smart-City Multi-Map Traffic Routing Framework
title_full_unstemmed MuTraff: A Smart-City Multi-Map Traffic Routing Framework
title_short MuTraff: A Smart-City Multi-Map Traffic Routing Framework
title_sort mutraff: a smart-city multi-map traffic routing framework
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7720143/
https://www.ncbi.nlm.nih.gov/pubmed/31817144
http://dx.doi.org/10.3390/s19245342
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