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Networks and Spatial Economics

, Volume 10, Issue 2, pp 209–240 | Cite as

The Relative Mobility of Vehicles Improves the Performance of Information Flow in Vehicle Ad Hoc Networks

  • Lili Du
  • Satish UkkusuriEmail author
Article

Abstract

Vehicular ad hoc networks (VANET) are receiving significant attention due to their potential to provide a wide range of benefits. One key distinguishing feature of VANET from other ad hoc networks is their relative vehicular mobility. Therefore, to fully understand the significant benefits of these systems, it is necessary to understand the interaction between traffic flow characteristics and information propagation in VANET. This research presents an analytical model to characterize information flow in VANET incorporating macroscopic traffic characteristics, such as traffic density, relative speed between adjacent lanes, and driver composition. The information flow in VANET is characterized using an information flow network (IFN). The analytical expressions for the expected degree of the individual nodes as well as the reachability of an IFN are provided. Moreover, a state of the art simulation model is developed to validate the analytical results. The proposed analytical results not only provide us significant insights to evaluate the performance of information propagation in VANET, but also provide theoretical basis for the design of algorithms for the efficient routing of information based on average end-to-end performance.

Keywords

Transportation networks Vehicular ad hoc networks Information flow Reachability Simulation 

Notes

Acknowledgements

We would like to thank the Blitman Career Development Chair Professorship and the anonymous reviewers for the helpful comments which improved the quality of this paper.

References

  1. Cartalk (2000) http://www.cartalk2000.net/. Accessed 11 July 2007
  2. Fleetnet, inter-vehicle communication (2008) http://www.et2.tu-harburg.de/fleetnet/. Accessed 5 Mar 2008
  3. Now: Network on wheels. http://www.network-on-wheels.de. Accessed 5 March 2008
  4. Artimy M, Robertson W, Phillips W (2004) Connectivity in inter-vehicle ad hoc network. In: CCECE 2004-CCGEI 2004. Niagara Falls, NY, USA, 2–5 May 2004Google Scholar
  5. Artimy M, Phillips W, Robertson W (2005a) Connectivity with static transmission range in vehicular ad hoc networks. In: Proceedings of the 3rd annual communication networks and services research conference. Washington, DC, USA, 16–18 May 2005Google Scholar
  6. Artimy M, Robertson W, Phillips W (2005b) Assignment of dynamic transmission range based on estimation of vehicle density. In: VANET’05. Cologne, Germany, 2 Sept 2005Google Scholar
  7. Ben-Akiva M, Koutsopoulos H, Mukundan, A (1994) A dynamic traffic model system for atms/atis operations. IVHS J 2:1–19Google Scholar
  8. Ben-Akiva ME, Koutsopoulos HN, Mishalani RG, Yang Q (1997) Simulation laboratory for evaluating dynamic traffic management systems. J Trans Engrg 123(4):283–289CrossRefGoogle Scholar
  9. Ben-Akiva M, Davol A, Toledo T, Koutsopoulos HN, Burghout W, Andréasson I, Johansson T, Lundin C (2002) Calibration and evaluation of MITSIMLab in Stockholm. In: Procedings of 81st transportation research board meeting. Washington, DC, USA, 13–17 Jan 2002Google Scholar
  10. Blum J, Eskandarian A, Hoffman L (2004) Challenges of intervehicle ad hoc networks. IEEE Trans Intell Tran Syst 5(4):347–351CrossRefGoogle Scholar
  11. Chandler S (1989) Calculation of number of relay hops requried in randomly located radio network. Electron Lett 25(24):1669–1671CrossRefGoogle Scholar
  12. Chartrand G, Zhang P (2005) Introduction to graph theory. Mc Graw Hill Higer Education, SingaporeGoogle Scholar
  13. Chorus C, Arentze T, Molin E, Timmermans H (2005) Value of travel information: theoretical framework and numerical examples. Transportation Research Record: Journal of the Transportation Research Board, 1926Google Scholar
  14. Diggavi S, Grossglauser M, Tse D (2002) Even one-dimensional mobility increase ad hoc wireless capacity. In: IEEE international symposium on information theory (ISIT), Lausanne, Switzerland, 30 Jun – 5 July 2002Google Scholar
  15. Gilbert E (1961) Random plane networks. J Soc Indust Appl Math 9(4):533–543CrossRefGoogle Scholar
  16. Grossglauser M, Tse D (2002) Mobility increase the capacity of ad hoc wireless network. IEEE/ACM Trans Netw 10(4):477–486CrossRefGoogle Scholar
  17. Jin W, Recker W (2006) Instantaneous information propagaton in a traffic stream through inter-vehicle communication. Trans Res Part B 40(3):230–250CrossRefGoogle Scholar
  18. Mahmassani H, Peeta S, Hu T, Ziliaskopoulos A (1993) Dynamic traffic assignment with multiple user classes for real-time ATIS/ATMS applications. In: Proceedings of the advanced traffic management conferenceGoogle Scholar
  19. Martin P, Feng U, Wang X (2003) Detector technology evaluation. Technical report, University of Utah Traffic Lab. http://www.mountain-plains.org/pubs/pdf/MPC03-154.pdf. Accessed Dec 2007
  20. Ni J, Chabdker S (1994) Connectivity properties of a random radio network. IEE Pro Commun 141(4):289–296CrossRefGoogle Scholar
  21. Ni J, Chandler S (1994) Connectivity properties of a random radio network. IEE Pro Commun 141(4):289–296CrossRefGoogle Scholar
  22. Petur S, Iyer S (2006) Reachability: an alternative to connectivity for sparse wireless multi-hop networks. In: Poster at IEEE infocom. Barcelona, Catalunya, Spain, 23–29 Apr 2006Google Scholar
  23. Philips T, Panwar S, Tantawi A (1989) Connectivity properties of a packet radio network model. IEEE Trans IT 3(5):1044–1047CrossRefGoogle Scholar
  24. Rybicki J, Scheuermann B, Kiess W, Lochert C, Fallahi P, Mauve M (2007) Challenge: peers on wheels—a road to new traffic information systems. In: MobiCom’07, 9–14 Sept 2007Google Scholar
  25. Saito H (2006) Performance analysis of combined vehicular communication. IEICE Trans Commun E89B(5):1486–1494CrossRefGoogle Scholar
  26. Schönhof M, Kesting A, Treiber M, Helbing D (2006) Coupled vehicle and information flows: message transport on a dynamic vehicle network. Physica A-Stat Mech Appl 363(1):73–81CrossRefGoogle Scholar
  27. Spanos D, Murray R (2004) Robust connectivity of netword vehicles. In: Procedings of 43rd IEEE conference on decision and control. Atlantis, Paradise Island, Bahamas, 14–17 Dec 2004Google Scholar
  28. Ukkusuri S, Du L (2008) Geometric connectivity of vehicular ad hoc networks: analytical characterization. Accepted for publication in Transportation Research Part CGoogle Scholar
  29. User’s Guide for MITSIMlab and Road Network Editor (RNE) (2002) MIT Intelligent Transportation Systems Program (Nov). Available: http://web.mit.edu/its/papers/Manual.pdf. Accessed Apr 2007
  30. Wang X (2007) Modeling the process of information relay through inter-vehicle communication. Trans Res Part B 41(6):684–700CrossRefGoogle Scholar
  31. Wu H, Fujimoto R, Riley G (2004) Analytical models for information propagation in vehicle-to-vehicle networks. In: IEEE vehicular technology conference. Los Angeles, CA, USA, 26–29 Sept 2004Google Scholar
  32. Xue F, Kumar P (2004) The number of neighbors needed for connectivity of wireless network. Wireless Netw 10:169–181CrossRefGoogle Scholar
  33. Zheng Z (2006) Routing in intermittently connected mobile ad hoc networks: overview and challanges. IEEE Commun Surveys Tutorials 8(1)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.Decision Sciences and Engineering SystemsRensselaer Polytechnic InstituteTroyUSA
  2. 2.Blitman Career Development ChairRensselaer Polytechnic InstituteTroyUSA

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