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Optimized Sensor Network and Multi-Agent Decision Support for Smart Traffic Light Management

One of the biggest challenges in modern societies is to solve vehicular traffic problems. Sensor networks in traffic environments have contributed to improving the decision-making process of Intelligent Transportation Systems. However, one of the limiting factors for the effectiveness of these syste...

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Autores principales: Cruz-Piris, Luis, Rivera, Diego, Fernandez, Susel, Marsa-Maestre, Ivan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5856164/
https://www.ncbi.nlm.nih.gov/pubmed/29393884
http://dx.doi.org/10.3390/s18020435
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author Cruz-Piris, Luis
Rivera, Diego
Fernandez, Susel
Marsa-Maestre, Ivan
author_facet Cruz-Piris, Luis
Rivera, Diego
Fernandez, Susel
Marsa-Maestre, Ivan
author_sort Cruz-Piris, Luis
collection PubMed
description One of the biggest challenges in modern societies is to solve vehicular traffic problems. Sensor networks in traffic environments have contributed to improving the decision-making process of Intelligent Transportation Systems. However, one of the limiting factors for the effectiveness of these systems is in the deployment of sensors to provide accurate information about the traffic. Our proposal is using the centrality measurement of a graph as a base to locate the best locations for sensor installation in a traffic network. After integrating these sensors in a simulation scenario, we define a Multi-Agent Systems composed of three types of agents: traffic light management agents, traffic jam detection agents, and agents that control the traffic lights at an intersection. The ultimate goal of these Multi-Agent Systems is to improve the trip duration for vehicles in the network. To validate our solution, we have developed the needed elements for modelling the sensors and agents in the simulation environment. We have carried out experiments using the Simulation of Urban MObility (SUMO) traffic simulator and the Travel and Activity PAtterns Simulation (TAPAS) Cologne traffic scenario. The obtained results show that our proposal allows to reduce the sensor network while still obtaining relevant information to have a global view of the environment. Finally, regarding the Multi-Agent Systems, we have carried out experiments that show that our proposal is able to improve other existing solutions such as conventional traffic light management systems (static or dynamic) in terms of reduction of vehicle trip duration and reduction of the message exchange overhead in the sensor network.
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spelling pubmed-58561642018-03-20 Optimized Sensor Network and Multi-Agent Decision Support for Smart Traffic Light Management Cruz-Piris, Luis Rivera, Diego Fernandez, Susel Marsa-Maestre, Ivan Sensors (Basel) Article One of the biggest challenges in modern societies is to solve vehicular traffic problems. Sensor networks in traffic environments have contributed to improving the decision-making process of Intelligent Transportation Systems. However, one of the limiting factors for the effectiveness of these systems is in the deployment of sensors to provide accurate information about the traffic. Our proposal is using the centrality measurement of a graph as a base to locate the best locations for sensor installation in a traffic network. After integrating these sensors in a simulation scenario, we define a Multi-Agent Systems composed of three types of agents: traffic light management agents, traffic jam detection agents, and agents that control the traffic lights at an intersection. The ultimate goal of these Multi-Agent Systems is to improve the trip duration for vehicles in the network. To validate our solution, we have developed the needed elements for modelling the sensors and agents in the simulation environment. We have carried out experiments using the Simulation of Urban MObility (SUMO) traffic simulator and the Travel and Activity PAtterns Simulation (TAPAS) Cologne traffic scenario. The obtained results show that our proposal allows to reduce the sensor network while still obtaining relevant information to have a global view of the environment. Finally, regarding the Multi-Agent Systems, we have carried out experiments that show that our proposal is able to improve other existing solutions such as conventional traffic light management systems (static or dynamic) in terms of reduction of vehicle trip duration and reduction of the message exchange overhead in the sensor network. MDPI 2018-02-02 /pmc/articles/PMC5856164/ /pubmed/29393884 http://dx.doi.org/10.3390/s18020435 Text en © 2018 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
Cruz-Piris, Luis
Rivera, Diego
Fernandez, Susel
Marsa-Maestre, Ivan
Optimized Sensor Network and Multi-Agent Decision Support for Smart Traffic Light Management
title Optimized Sensor Network and Multi-Agent Decision Support for Smart Traffic Light Management
title_full Optimized Sensor Network and Multi-Agent Decision Support for Smart Traffic Light Management
title_fullStr Optimized Sensor Network and Multi-Agent Decision Support for Smart Traffic Light Management
title_full_unstemmed Optimized Sensor Network and Multi-Agent Decision Support for Smart Traffic Light Management
title_short Optimized Sensor Network and Multi-Agent Decision Support for Smart Traffic Light Management
title_sort optimized sensor network and multi-agent decision support for smart traffic light management
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5856164/
https://www.ncbi.nlm.nih.gov/pubmed/29393884
http://dx.doi.org/10.3390/s18020435
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