Application of vehicular communications for improving the efficiency of traffic in urban areas

This paper studies the impacts of vehicular communications on efficiency of traffic in urban areas. We consider a Green Light Optimized Speed Advisory application implementation in a typical reference area and present the results of its performance analysis using an integrated cooperative intelligent transportation systems simulation platform. In addition, we study route alternation using vehicle-to-infrastructure and vehicle-to-vehicle communications. Our interest was to monitor the impacts of these applications on fuel and traffic efficiency by introducing metrics for average fuel consumption, average stop time behind a traffic light and average trip time, respectively. For gathering the results, we implemented two traffic scenarios defining routes through an urban area including traffic lights. The simulations are varied for different penetration rates of application-equipped vehicles, driver's compliance to the advised speed and traffic density. Our results indicate that Green Light Optimized Speed Advisory systems could improve fuel consumption, reduce traffic congestion in junctions and the total trip time. Copyright © 2011 John Wiley & Sons, Ltd.

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

[2]  Stefan Krauss,et al.  MICROSCOPIC MODELING OF TRAFFIC FLOW: INVESTIGATION OF COLLISION FREE VEHICLE DYNAMICS. , 1998 .

[3]  J.-C. Cano,et al.  Predicting Traffic lights to Improve Urban Traffic Fuel Consumption , 2006, 2006 6th International Conference on ITS Telecommunications.

[4]  J.L. Martins de Carvalho,et al.  Towards the development of intelligent transportation systems , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[5]  Elmar Schoch,et al.  CGGC: Cached Greedy Geocast , 2004, WWIC.

[6]  R. Akcelik,et al.  An energy-related model of instantaneous fuel consumption , 1986 .

[7]  Veronica Martinez,et al.  I2V Communication Driving Assistance System: On-Board Traffic Light Assistant , 2008, 2008 IEEE 68th Vehicular Technology Conference.

[8]  Robbert van Renesse,et al.  JiST: an efficient approach to simulation using virtual machines , 2005, Softw. Pract. Exp..

[9]  Helbing,et al.  Congested traffic states in empirical observations and microscopic simulations , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[10]  Christoph Meinel,et al.  Realistic Simulation of V2X Communication Scenarios , 2008, 2008 IEEE Asia-Pacific Services Computing Conference.

[11]  C. Wewetzer,et al.  VANET Simulation Environment with Feedback Loop and its Application to Traffic Light Assistance , 2008, 2008 IEEE Globecom Workshops.

[12]  Takaaki Hasegawa,et al.  Vehicle fuel consumption and emission estimation in environment-adaptive driving with or without inter-vehicle communications , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[13]  Hannes Hartenstein,et al.  A tutorial survey on vehicular ad hoc networks , 2008, IEEE Communications Magazine.

[14]  Wenhui Zhang,et al.  Car-2-Car Communication Consortium - Manifesto , 2007 .

[15]  Daniel Krajzewicz,et al.  SUMO (Simulation of Urban MObility) - an open-source traffic simulation , 2002 .

[16]  Huang Hai-Jun,et al.  Improving Urban Traffic by Velocity Guidance , 2008, 2008 International Conference on Intelligent Computation Technology and Automation (ICICTA).

[17]  Hannes Hartenstein,et al.  The impact of traffic-light-to-vehicle communication on fuel consumption and emissions , 2010, 2010 Internet of Things (IOT).

[18]  Eddie Curtis,et al.  The National Traffic Signal Report Card , 2012 .

[19]  Ardalan Vahidi,et al.  Predictive Cruise Control: Utilizing Upcoming Traffic Signal Information for Improving Fuel Economy and Reducing Trip Time , 2011, IEEE Transactions on Control Systems Technology.