ABEONA Monitored Traffic: VANET-Assisted Cooperative Traffic Congestion Forecasting

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.

[1]  Cecilia Mascolo,et al.  Persistent content-based information dissemination in hybrid vehicular networks , 2009, 2009 IEEE International Conference on Pervasive Computing and Communications.

[2]  Javier Gozálvez,et al.  Traffic congestion detection in large-scale scenarios using vehicle-to-vehicle communications , 2013, J. Netw. Comput. Appl..

[3]  Samir Ranjan Das,et al.  Predictive methods for improved vehicular WiFi access , 2009, MobiSys '09.

[4]  Alexey V. Vinel,et al.  3GPP LTE Versus IEEE 802.11p/WAVE: Which Technology is Able to Support Cooperative Vehicular Safety Applications? , 2012, IEEE Wireless Communications Letters.

[5]  Reinhard German,et al.  Bidirectionally Coupled Network and Road Traffic Simulation for Improved IVC Analysis , 2011, IEEE Transactions on Mobile Computing.

[6]  B. Kerner THE PHYSICS OF TRAFFIC , 1999 .

[7]  Marco Gramaglia,et al.  Virtual Induction Loops Based on Cooperative Vehicular Communications , 2013, Sensors.

[8]  Fethi Filali,et al.  Efficient and unique identifier for V2X events aggregation in the Local Dynamic Map , 2011, 2011 11th International Conference on ITS Telecommunications.

[9]  S. Tsukahara,et al.  Short-term traffic prediction using fuzzy c-means and cellular automata in a wide-area road network , 2005, Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005..

[10]  Marco Gramaglia,et al.  Overhearing-Assisted Optimization of Address Autoconfiguration in Position-Aware VANETs , 2011, IEEE Transactions on Vehicular Technology.

[11]  Giovanni Pau,et al.  On the Effectiveness of an Opportunistic Traffic Management System for Vehicular Networks , 2011, IEEE Transactions on Intelligent Transportation Systems.

[12]  Serge P. Hoogendoorn,et al.  State-of-the-art of vehicular traffic flow modelling , 2001 .

[13]  Roland Chrobok,et al.  Different methods of traffic forecast based on real data , 2004, Eur. J. Oper. Res..

[14]  Tom Thomas,et al.  Predictions of Urban Volumes in Single Time Series , 2010, IEEE Transactions on Intelligent Transportation Systems.