Literature Database Entry


M. Gramaglia, M. Calderon and C.J. Bernardos, "ABEONA Monitored Traffic: VANET-Assisted Cooperative Traffic Congestion Forecasting," IEEE Vehicular Technology Magazine, vol. 9 (2), pp. 50–57, June 2014.


The existing mechanisms to monitor vehicular traffic, such as the use of induction loops and cameras, are expensive to deploy and maintain. Vehicular communications opens up a new world of optimization opportunities as each vehicle can be used as a sensor to measure the fundamental variables defining the traffic state (flow, density, and speed). In this article, we propose ABEONA, a beacon-based traffic congestion algorithm and also the name of the Roman goddess of journey, which captures the current and recent-past traffic trends to forecast the near-future road conditions. Compared to the existing monitoring approaches, ABEONA allows for the estimation of the vehicular density and reduces installation and maintenance costs. ABEONA's algorithm incurs low overhead and enables drivers to use forecast traffic congestion events to replan their route accordingly.

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M. Gramaglia
M. Calderon
C.J. Bernardos

BibTeX reference

    author = {Gramaglia, M. and Calderon, M. and Bernardos, C.J.},
    doi = {10.1109/MVT.2014.2312238},
    issn = {1556-6072},
    journal = {IEEE Vehicular Technology Magazine},
    month = {6},
    number = {2},
    pages = {50--57},
    publisher = {Institute of Electrical and Electronics Engineers},
    title = {{ABEONA Monitored Traffic: VANET-Assisted Cooperative Traffic Congestion Forecasting}},
    volume = {9},
    year = {2014},

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