Traffic Density Estimation Protocol Using Vehicular Networks

  • Adnan Noor Mian
  • Ishrat Fatima
  • Roberto Beraldi
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 120)

Abstract

Traffic density estimates on maps not only assist drivers to decide routes that are time and fuel economical due to less congestions and but also help in preventing accidents that may occur due to the lack of not being able to see far ahead. In this paper, we propose a protocol that exploit vehicle-to-vehicle ad hoc communication for the estimation of vehicular density and the amount of congestion on roads. The protocol forms cluster heads by a voting algorithm. These cluster heads aggregate density information and spread it to the network via few selected forwarding vehicles. The protocol does not assume all vehicles to be equipped with Global Positioning System. We analytically study the cluster head formation part of the protocol and then simulate the proposed protocol using network simulator NS2 to understand different characteristics of the protocol.

Keywords

Vehicular networks traffic congestion wireless ad hoc networks 
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Network simulator ns-2, http://www.isi.edu/nsnam/ns/
  2. 2.
    Ababneh, N., Labiod, H.: Safety message dissemination in vanets: Flooding or trajectory-based? In: Med-Hoc-Net, pp. 1–8. IEEE (2010)Google Scholar
  3. 3.
    Almahorg, K., Basir, O.: Simulation-based performance comparison of vanets backbone formation algorithms. In: 12th IEEE/ACM International Symposium on Distributed Simulation and Real-Time Applications, DS-RT 2008, pp. 236–242 (October 2008)Google Scholar
  4. 4.
    Hofmann-Wellenho, H.L.B., Collins, J.: Global positioning system: Theory and practice, vol. 4. Springer (1997)Google Scholar
  5. 5.
    Bilstrup, K., Uhlemann, E., Ström, E.G., Bilstrup, U.: On the ability of the 802.11p mac method and stdma to support real-time vehicle-to-vehicle communication. EURASIP J. Wirel. Commun. Netw., 5:1–5:13 (January 2009)Google Scholar
  6. 6.
    Chang, W.-R., Lin, H.-T., Chen, B.-X.: Trafficgather: An efficient and scalable data collection protocol for vehicular ad hoc networks. In: 5th IEEE Consumer Communications and Networking Conference, CCNC 2008., pp. 365–369 (January 2008)Google Scholar
  7. 7.
    Hsiao, H.-C., Studer, A., Chen, C., Perrig, A., Bai, F., Bellur, B., Iyer, A.: Flooding-resilient broadcast authentication for vanets. In: Proceedings of the 17th Annual International Conference on Mobile Computing and Networking, MobiCom 2011, pp. 193–204. ACM (2011)Google Scholar
  8. 8.
    Huang, D., Shere, S., Ahn, S.: Dynamic highway congestion detection and prediction based on shock waves. In: Proceedings of the Seventh ACM International Workshop on VehiculAr Inter-NETworking, Systems, and Applications, VANET 2010, pp. 11–20. ACM (2010)Google Scholar
  9. 9.
    Ibrahim, K., Weigle, M.: Cascade: Cluster-based accurate syntactic compression of aggregated data in vanets. In: 2008 IEEE GLOBECOM Workshops, November 30–December 4, vol. 30, pp. 1–10 (2008)Google Scholar
  10. 10.
    Lee, E.-K., Yang, S., Oh, S., Gerla, M.: Rf-gps: Rfid assisted localization in vanets. In: IEEE 6th International Conference on Mobile Adhoc and Sensor Systems, MASS 2009, Macau, pp. 621–626 (2009)Google Scholar
  11. 11.
    Nadeem, T., Dashtinezhad, S., Liao, C., Iftode, L.: Trafficview: traffic data dissemination using car-to-car communication. SIGMOBILE Mob. Comput. Commun. Rev. 8(3), 6–19 (2004)CrossRefGoogle Scholar
  12. 12.
    Padron, F.M.: Traffic congestion detection using vanet. Master thesis, Florida Atlantic University, USA (2009)Google Scholar
  13. 13.
    Parker, R., Valaee, S.: Vehicle localization in vehicular networks. In: 2006 IEEE 64th Vehicular Technology Conference, VTC-2006 Fall, pp. 1–5 (September 2006)Google Scholar
  14. 14.
    Porikli, F.: Traffic congestion estimation using hmm model without vehicle tracking. In: IEEE Intelligent Vehicles Symposium, pp. 188–193 (2004)Google Scholar
  15. 15.
    Ros, F., Ruiz, P., Stojmenovic, I.: Acknowledgment-based broadcast protocol for reliable and efficient data dissemination in vehicular ad hoc networks. IEEE Transactions on Mobile Computing 11(1), 33–46 (2012)CrossRefGoogle Scholar
  16. 16.
    Singh, R.P., Gupta, A.: Traffic congestion estimation in vanets and its application to information dissemination. In: Aguilera, M.K., Yu, H., Vaidya, N.H., Srinivasan, V., Choudhury, R.R. (eds.) ICDCN 2011. LNCS, vol. 6522, pp. 376–381. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  17. 17.
    Tonguz, O.K., Wisitpongphan, N., Parikh, J.S., Bai, F., Mudalige, P., Sadekar, V.K.: On the Broadcast Storm Problem in Ad hoc Wireless Networks. In: 3rd International Conference on Broadband Communications, Networks and Systems, BROADNETS 2006, pp. 1–11 (2006)Google Scholar
  18. 18.
    Pattara-atikom, R.P.W., Luckana, R.: Estimating road traffic congestion using cell dwell time with simple threshold and fuzzy logic techniques. In: IEEE Intelligent Transportation Systems Conference, pp. 956–961 (2007)Google Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2013

Authors and Affiliations

  • Adnan Noor Mian
    • 1
  • Ishrat Fatima
    • 1
  • Roberto Beraldi
    • 2
  1. 1.National University of Computer and Emerging SciencesLahorePakistan
  2. 2.DISUniversità di Roma “La Sapienza”RomaItaly

Personalised recommendations