I-VDE: A Novel Approach to Estimate Vehicular Density by Using Vehicular Networks

Road traffic is experiencing a drastic increase in recent years, thereby increasing the every day traffic congestion problems, especially in cities. Vehicle density is one of the main metrics used for assessing the road traffic conditions. Currently, most of the existing vehicle density estimation a...

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Detalles Bibliográficos
Autores: Barrachina Villalba, Javier, Garrido Picazo, María Piedad, Fogue, Manuel, Martínez, Francisco J., Cano, Juan-Carlos|||0000-0002-0038-0539, Tavares De Araujo Cesariny Calafate, Carlos Miguel|||0000-0001-5729-3041, Manzoni, Pietro|||0000-0003-3753-0403
Tipo de recurso: capítulo de libro
Fecha de publicación:2013
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/71132
Acceso en línea:https://riunet.upv.es/handle/10251/71132
Access Level:acceso abierto
Palabra clave:Vehicular networks
Vehicular density estimation
Road Side Unit
VANETs
ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES
Descripción
Sumario:Road traffic is experiencing a drastic increase in recent years, thereby increasing the every day traffic congestion problems, especially in cities. Vehicle density is one of the main metrics used for assessing the road traffic conditions. Currently, most of the existing vehicle density estimation approaches, such as inductive loop detectors or traffic surveillance cameras, require infrastructure-based traffic information systems to be installed at various locations. In this paper, we present I-VDE, a solution to estimate the density of vehicles that has been specially designed for Vehicular Networks. Our proposal allows Intelligent Transportation Systems to continuously estimate the vehicular density by accounting for the number of beacons received per Road Side Unit, as well as the roadmap topology. Simulation results indicate that our approach accurately estimates the vehicular density, and therefore automatic traffic controlling systems may use it to predict traffic jams and introduce countermeasures.