Decreasing greenhouse emissions through an intelligent traffic information system based on inter-vehicle communication

Traffic congestion is an urban mobility problem, which generates stress to drivers and economic losses. In 2012, greenhouse gas emissions from transportation accounted for about 28% of total U.S. greenhouse gas emissions. Intelligent transportation systems can assist in the identification and reduction of vehicular traffic congestion. In this context, this work proposes an intelligent traffic information system based on inter-vehicle communication to avoid vehicle traffic congestion. The main goal of the proposed solution is to decrease CO2 emissions, the average trip time and fuel consumption by avoiding congested roads. Simulation results show that our proposed solution can reduce the average trip time, and the overall CO2 emission and fuel consumption. In particular, the trip time was decreased approximately 86%, the fuel consumption 40% and the CO2 emission 55%. This shows the potential of the proposed solution.

[1]  Fredrik Tufvesson,et al.  Vehicle-to-Vehicle Communications , 2012 .

[2]  Bo Xu,et al.  Disseminating real-time traffic information in vehicular ad-hoc networks , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[3]  Gabriel-Miro Muntean,et al.  VANET-Enabled Eco-Friendly Road Characteristics-Aware Routing for Vehicular Traffic , 2013, 2013 IEEE 77th Vehicular Technology Conference (VTC Spring).

[4]  Luiz Fernando Bittencourt,et al.  A seamless flow mobility management architecture for vehicular communication networks , 2013, Journal of Communications and Networks.

[5]  Falko Dressler,et al.  On the applicability of Two-Ray path loss models for vehicular network simulation , 2012, 2012 IEEE Vehicular Networking Conference (VNC).

[6]  Javier Gozálvez,et al.  Road traffic congestion detection through cooperative Vehicle-to-Vehicle communications , 2010, IEEE Local Computer Network Conference.

[7]  Anupam Joshi,et al.  StreetSmart Traffic: Discovering and Disseminating Automobile Congestion Using VANET's , 2007, 2007 IEEE 65th Vehicular Technology Conference - VTC2007-Spring.

[8]  Ilja Radusch,et al.  V2X-Based Traffic Congestion Recognition and Avoidance , 2009, 2009 10th International Symposium on Pervasive Systems, Algorithms, and Networks.

[9]  Daniel Krajzewicz,et al.  SUMO - Simulation of Urban MObility An Overview , 2011 .

[10]  Nelson Luis Saldanha da Fonseca,et al.  An efficient and robust protocol to disseminate data in highway environments with different traffic conditions , 2014, 2014 IEEE Symposium on Computers and Communications (ISCC).

[11]  Pei-Yin Chen,et al.  Decision-tree based green driving suggestion system for carbon emission reduction , 2012, 2012 12th International Conference on ITS Telecommunications.

[12]  Hesham A. Rakha,et al.  Eco-driving at signalized intersections using V2I communication , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[13]  Azzedine Boukerche,et al.  An efficient and robust data dissemination protocol for vehicular ad hoc networks , 2012, PE-WASUN '12.

[14]  Mónica Aguilar-Igartua,et al.  Smart city for VANETs using warning messages, traffic statistics and intelligent traffic lights , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[15]  David L. Greene,et al.  Reducing Greenhouse Gas Emissions from U.S. Transportation , 2003 .