Abstract
Road traffic is experiencing a drastic increase in recent years, thereby increasing the every day traffic congestion problems, especially in cities. Vehicle density is one of the main metrics used for assessing the road traffic conditions. Currently, most of the existing vehicle density estimation approaches, such as inductive loop detectors or traffic surveillance cameras, require infrastructure-based traffic information systems to be installed at various locations. In this paper, we present I-VDE, a solution to estimate the density of vehicles that has been specially designed for Vehicular Networks. Our proposal allows Intelligent Transportation Systems to continuously estimate the vehicular density by accounting for the number of beacons received per Road Side Unit, as well as the roadmap topology. Simulation results indicate that our approach accurately estimates the vehicular density, and therefore automatic traffic controlling systems may use it to predict traffic jams and introduce countermeasures.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Akhtar, N., Ergen, S., Ozkasap, O.: Analysis of distributed algorithms for density estimation in VANETs. In: IEEE Vehicular Networking Conference (VNC), pp. 157–164 (November 2012)
Barrachina, J., Garrido, P., Fogue, M., Martinez, F.J., Cano, J.C., Calafate, C.T., Manzoni, P.: D-RSU: A Density-Based Approach for Road Side Unit Deployment in Urban Scenarios. In: International Workshop on IPv6-based Vehicular Networks (Vehi6), Collocated with the 2012 IEEE Intelligent Vehicles Symposium, pp. 1–6 (June 2012)
Fall, K., Varadhan, K.: ns notes and documents. The VINT Project. UC Berkeley, LBL, USC/ISI, and Xerox PARC (February 2000), http://www.isi.edu/nsnam/ns/ns-documentation.html
Fogue, M., Garrido, P., Martinez, F.J., Cano, J.C., Calafate, C.T., Manzoni, P.: A Realistic Simulation Framework for Vehicular Networks. In: 5th International ICST Conference on Simulation Tools and Techniques (SIMUTools 2012), Desenzano, Italy, pp. 37–46 (March 2012)
Fogue, M., Garrido, P., Martinez, F.J., Cano, J.C., Calafate, C.T., Manzoni, P.: Evaluating the impact of a novel message dissemination scheme for vehicular networks using real maps. Transportation Research Part C: Emerging Technologies 25, 61–80 (2012)
Garelli, L., Casetti, C., Chiasserini, C., Fiore, M.: MobSampling: V2V Communications for Traffic Density Estimation. In: IEEE 73rd Vehicular Technology Conference (VTC Spring), pp. 1–5 (May 2011)
Krajzewicz, D., Rossel, C.: Simulation of Urban MObility (SUMO), Centre for Applied Informatics (ZAIK) and the Institute of Transport Research at the German Aerospace Centre (2012), http://sumo.sourceforge.net
Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Physical Review E 55(5), 5597–5602 (1997)
Martinez, F.J., Toh, C.K., Cano, J.C., Calafate, C.T., Manzoni, P.: Determining the representative factors affecting warning message dissemination in VANETs. Wireless Personal Communications 67(2), 295–314 (2012)
Martinez, F.J., Cano, J.C., Calafate, C.T., Manzoni, P., Barrios, J.M.: Assessing the feasibility of a VANET. In: ACM Workshop on Performance Monitoring, Measurement and Evaluation of Heterogeneous Wireless and Wired Networks (PM2HW2N 2009, held with MSWiM), pp. 39–45. ACM, NY (2009)
OpenStreetMap: Collaborative project to create a free editable map of the world (2012), http://www.openstreetmap.org
Soldo, F., Lo Cigno, R., Gerla, M.: Cooperative synchronous broadcasting in infrastructure-to-vehicles networks. In: Fifth Annual Conference on Wireless on Demand Network Systems and Services (WONS), pp. 125–132 (January 2008)
Stanica, R., Chaput, E., Beylot, A.: Local density estimation for contention window adaptation in vehicular networks. In: IEEE 22nd International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC), pp. 730–734 (September 2011)
Tan, E., Chen, J.: Vehicular traffic density estimation via statistical methods with automated state learning. In: IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 164–169 (September 2007)
Tyagi, V., Kalyanaraman, S., Krishnapuram, R.: Vehicular traffic density state estimation based on cumulative road acoustics. IEEE Transactions on Intelligent Transportation Systems 13(3), 1156–1166 (2012)
ZunZun: Online Curve Fitting and Surface Fitting Web Site (2013), http://www.zunzun.com
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Barrachina, J. et al. (2013). I-VDE: A Novel Approach to Estimate Vehicular Density by Using Vehicular Networks. In: Cichoń, J., Gȩbala, M., Klonowski, M. (eds) Ad-hoc, Mobile, and Wireless Network. ADHOC-NOW 2013. Lecture Notes in Computer Science, vol 7960. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39247-4_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-39247-4_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39246-7
Online ISBN: 978-3-642-39247-4
eBook Packages: Computer ScienceComputer Science (R0)