Barrachina Villalba et al 2013a - Revision history

Scipediacontent at 12:22, 21 January 2021

2021-01-21T12:22:29Z

Scipediacontent: Scipediacontent moved page Draft Content 857915373 to Barrachina Villalba et al 2013a

2020-10-14T13:23:51Z

Scipediacontent moved page Draft Content 857915373 to Barrachina Villalba et al 2013a

Scipediacontent: Created page with " == Abstract == The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-39247-4_6 Road traffic is experiencing a drastic increase in recent ye..."

2020-10-14T13:23:48Z

Created page with " == Abstract == The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-39247-4_6 Road traffic is experiencing a drastic increase in recent ye..."

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== Abstract ==

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-39247-4_6 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. Barrachina Villalba, J.; Garrido Picazo, MP.; Fogue, M.; Martínez, FJ.; Cano Escribá, JC.; Tavares De Araujo Cesariny Calafate, CM.; Manzoni, P. (2013). I-VDE: A Novel Approach to Estimate Vehicular Density by Using Vehicular Networks. En Ad-hoc, Mobile, and Wireless Network. Springer. 63-74. doi:10.1007/978-3-642-39247-4_6 Senia 63 74

Document type: Part of book or chapter of book

== Full document ==
<pdf>Media:Draft_Content_857915373-beopen774-2109-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [http://hdl.handle.net/10251/71132 http://hdl.handle.net/10251/71132]

* [https://riunet.upv.es/bitstream/10251/71132/3/paper_adhocnow_v2_Calafate.pdf https://riunet.upv.es/bitstream/10251/71132/3/paper_adhocnow_v2_Calafate.pdf]

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 == Abstract ==
-The final publication is available at Springer via  http://dx.doi.org/10.1007/978-3-642-39247-4_6 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. Barrachina Villalba, J.; Garrido Picazo, MP.; Fogue, M.; Martínez, FJ.; Cano Escribá, JC.; Tavares De Araujo Cesariny Calafate, CM.; Manzoni, P. (2013). I-VDE: A Novel Approach to Estimate Vehicular Density by Using Vehicular Networks. En Ad-hoc, Mobile, and Wireless Network. Springer. 63-74. doi:10.1007/978-3-642-39247-4_6 Senia 63 74
+The final publication is available at Springer via  http://dx.doi.org/10.1007/978-3-642-39247-4_6 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. This work was partially supported by the Ministerio de Ciencia e InnovaciÂ´on, Spain, under Grant TIN2011-27543-C03-01, as well as by the FundaciÂ´on Universitaria Antonio Gargallo (FUAG), and the Caja de Ahorros de la Inmaculada (CAI) Barrachina Villalba, J.; Garrido Picazo, MP.; Fogue, M.; MartÃnez, FJ.; Cano EscribÃ¡, JC.; Tavares De Araujo Cesariny Calafate, CM.; Manzoni, P. (2013). I-VDE: A Novel Approach to Estimate Vehicular Density by Using Vehicular Networks. En Ad-hoc, Mobile, and Wireless Network. Springer. 63-74. doi:10.1007/978-3-642-39247-4_6 S 63 74
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-Document type: Part of book or chapter of book
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-== Full document ==
-<pdf>Media:Draft_Content_857915373-beopen774-2109-document.pdf</pdf>
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 * [https://riunet.upv.es/bitstream/10251/71132/3/paper_adhocnow_v2_Calafate.pdf https://riunet.upv.es/bitstream/10251/71132/3/paper_adhocnow_v2_Calafate.pdf]

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