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A V2I-Based Real-Time Traffic Density Estimation System in Urban Scenarios

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Abstract

The number of vehicles in our roads is drastically increasing, especially in developing countries. In addition, these vehicles tend to be concentrated in urban areas which present a large population. Since traffic jams have important and mostly negative consequences, such as increasing travel time, fuel consumption, and air pollution, governments are making efforts to alleviate the increasing traffic pressure, being vehicular density one of the main metrics used for assessing the road traffic conditions. However, vehicle density is highly variable in time and space, making it difficult to be estimated accurately. In this paper, we present a solution to estimate the density of vehicles in urban scenarios. Our proposal, that has been specially designed for vehicular networks, allows intelligent transportation systems to continuously estimate vehicular density by accounting for the number of beacons received per road side unit (RSU), and also considering the roadmap topology where the RSUs are located. Using V2I communications, we are able to estimate the traffic density in a certain area, which represents a key parameter to perform efficient traffic redirection, thereby reducing the vehicles’ travel time and avoiding traffic jams. Simulation results reveal that, unlike previous proposals, our approach accurately estimates the vehicular density in all the scenarios, presenting an average relative error is of only 3.04 %.

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Acknowledgments

This work was partially supported by the Ministerio de Ciencia e Innovación, Spain, under Grant TIN2011-27543-C03-01, and by the Government of Aragón and the European Social Fund (T91 Research Group).

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Authors and Affiliations

  1. University of Zaragoza, Teruel, Spain

    Javier Barrachina, Piedad Garrido, Manuel Fogue & Francisco J. Martinez

  2. Universitat Politècnica de València, Valencia, Spain

    Juan-Carlos Cano, Carlos T. Calafate & Pietro Manzoni

Authors
  1. Javier Barrachina
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  2. Piedad Garrido
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  3. Manuel Fogue
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  4. Francisco J. Martinez
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  5. Juan-Carlos Cano
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  6. Carlos T. Calafate
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  7. Pietro Manzoni
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Corresponding author

Correspondence to Francisco J. Martinez.

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Barrachina, J., Garrido, P., Fogue, M. et al. A V2I-Based Real-Time Traffic Density Estimation System in Urban Scenarios. Wireless Pers Commun 83, 259–280 (2015). https://doi.org/10.1007/s11277-015-2392-4

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