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Increasing Intelligence in Inter-Vehicle Communications to Reduce Traffic Congestions: Experiments in Urban and Highway Environments

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  • Rodolfo I Meneguette
  • Geraldo P R Filho
  • Daniel L Guidoni
  • Gustavo Pessin
  • Leandro A Villas
  • Jó Ueyama

Abstract

Intelligent Transportation Systems (ITS) rely on Inter-Vehicle Communication (IVC) to streamline the operation of vehicles by managing vehicle traffic, assisting drivers with safety and sharing information, as well as providing appropriate services for passengers. Traffic congestion is an urban mobility problem, which causes stress to drivers and economic losses. In this context, this work proposes a solution for the detection, dissemination and control of congested roads based on inter-vehicle communication, called INCIDEnT. The main goal of the proposed solution is to reduce the average trip time, CO emissions and fuel consumption by allowing motorists to avoid congested roads. The simulation results show that our proposed solution leads to short delays and a low overhead. Moreover, it is efficient with regard to the coverage of the event and the distance to which the information can be propagated. The findings of the investigation show that the proposed solution leads to (i) high hit rate in the classification of the level of congestion, (ii) a reduction in average trip time, (iii) a reduction in fuel consumption, and (iv) reduced CO emissions

Suggested Citation

  • Rodolfo I Meneguette & Geraldo P R Filho & Daniel L Guidoni & Gustavo Pessin & Leandro A Villas & Jó Ueyama, 2016. "Increasing Intelligence in Inter-Vehicle Communications to Reduce Traffic Congestions: Experiments in Urban and Highway Environments," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-25, August.
  • Handle: RePEc:plo:pone00:0159110
    DOI: 10.1371/journal.pone.0159110
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    Cited by:

    1. Prajakta Desai & Seng W Loke & Aniruddha Desai, 2017. "Cooperative vehicles for robust traffic congestion reduction: An analysis based on algorithmic, environmental and agent behavioral factors," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-19, August.
    2. Iago C. Cavalcante & Rodolfo I. Meneguette & Renato H. Torres & Leandro Y. Mano & Vinícius P. Gonçalves & Jó Ueyama & Gustavo Pessin & Georges D. Amvame Nze & Geraldo P. Rocha Filho, 2022. "Federated System for Transport Mode Detection," Energies, MDPI, vol. 15(23), pages 1-17, December.
    3. Kassens-Noor, Eva & Dake, Dana & Decaminada, Travis & Kotval-K, Zeenat & Qu, Teresa & Wilson, Mark & Pentland, Brian, 2020. "Sociomobility of the 21st century: Autonomous vehicles, planning, and the future city," Transport Policy, Elsevier, vol. 99(C), pages 329-335.

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