Javier Barrachina, Manuel Fogué, Piedad Garrido, F. Martinez, Juan-Carlos Cano, C. Calafate, P. Manzoni
{"title":"Assessing vehicular density estimation using vehicle-to-infrastructure communications","authors":"Javier Barrachina, Manuel Fogué, Piedad Garrido, F. Martinez, Juan-Carlos Cano, C. Calafate, P. Manzoni","doi":"10.1109/WoWMoM.2013.6583416","DOIUrl":null,"url":null,"abstract":"Vehicle density is one of the main metrics used for assessing the road traffic conditions. In this paper, we present 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.","PeriodicalId":158378,"journal":{"name":"2013 IEEE 14th International Symposium on \"A World of Wireless, Mobile and Multimedia Networks\" (WoWMoM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE 14th International Symposium on \"A World of Wireless, Mobile and Multimedia Networks\" (WoWMoM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WoWMoM.2013.6583416","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Vehicle density is one of the main metrics used for assessing the road traffic conditions. In this paper, we present 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.