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...

Full description

Bibliographic Details
Main Authors: Luis Cruz-Piris, Diego Rivera, Susel Fernandez, Ivan Marsa-Maestre
Format: Article
Language:English
Published: MDPI AG 2018-02-01
Series:Sensors
Subjects:
Online Access:http://www.mdpi.com/1424-8220/18/2/435
id doaj-7034d2b22af44f45bf1c1f977bc84f76
record_format Article
spelling doaj-7034d2b22af44f45bf1c1f977bc84f762020-11-25T00:08:12ZengMDPI AGSensors1424-82202018-02-0118243510.3390/s18020435s18020435Optimized Sensor Network and Multi-Agent Decision Support for Smart Traffic Light ManagementLuis Cruz-Piris0Diego Rivera1Susel Fernandez2Ivan Marsa-Maestre3Departamento de Automática, Escuela Politécnica Superior, Universidad de Alcalá, 28805 Alcalá de Henares, Madrid, SpainDepartamento de Automática, Escuela Politécnica Superior, Universidad de Alcalá, 28805 Alcalá de Henares, Madrid, SpainDepartamento de Automática, Escuela Politécnica Superior, Universidad de Alcalá, 28805 Alcalá de Henares, Madrid, SpainDepartamento de Automática, Escuela Politécnica Superior, Universidad de Alcalá, 28805 Alcalá de Henares, Madrid, SpainOne 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.http://www.mdpi.com/1424-8220/18/2/435sensor networksoptimized sensor deploymentmulti-agents systemintelligent transportation systemsmart citiestraffic simulationstraffic light management
collection DOAJ
language English
format Article
sources DOAJ
author Luis Cruz-Piris
Diego Rivera
Susel Fernandez
Ivan Marsa-Maestre
spellingShingle Luis Cruz-Piris
Diego Rivera
Susel Fernandez
Ivan Marsa-Maestre
Optimized Sensor Network and Multi-Agent Decision Support for Smart Traffic Light Management
Sensors
sensor networks
optimized sensor deployment
multi-agents system
intelligent transportation system
smart cities
traffic simulations
traffic light management
author_facet Luis Cruz-Piris
Diego Rivera
Susel Fernandez
Ivan Marsa-Maestre
author_sort Luis Cruz-Piris
title 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_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_sort optimized sensor network and multi-agent decision support for smart traffic light management
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2018-02-01
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.
topic sensor networks
optimized sensor deployment
multi-agents system
intelligent transportation system
smart cities
traffic simulations
traffic light management
url http://www.mdpi.com/1424-8220/18/2/435
work_keys_str_mv AT luiscruzpiris optimizedsensornetworkandmultiagentdecisionsupportforsmarttrafficlightmanagement
AT diegorivera optimizedsensornetworkandmultiagentdecisionsupportforsmarttrafficlightmanagement
AT suselfernandez optimizedsensornetworkandmultiagentdecisionsupportforsmarttrafficlightmanagement
AT ivanmarsamaestre optimizedsensornetworkandmultiagentdecisionsupportforsmarttrafficlightmanagement
_version_ 1725416208756375552