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Modeling connectivity of inter-vehicle communication networks along discrete traffic streams

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  • Jin, W L

Abstract

Rapid developments in various frontiers of telecommunications and information technologies could enable the development of next-generation Intelligent Transportation Systems (ITS) that rely on inter-vehicle communications (IVC) to disseminate time-critical and location-based traffic information. In this study, we present a new model for computing the instantaneous multihop connectivity and end node probability for vehicles along a line in a transportation network, where the distribution of vehicles may be non-uniform, and vehicles' positions may depend on each other. With given locations of all vehicles, the proposed model can be used to estimate the connectivity when vehicles have different probabilities to be equipped with IVC units. With the model, we study how the distribution patterns of vehicles can affect information propagation, formulate the choice of the location of a road-side station as anoptimization problem, and examine the time-dependent connectivity properties on an inhomogeneous ring road. The new models yield results consistent with those in the literature but are simpler in formulation and computation. The new model for estimating multihop connectivity of an IVC network with realistic trac patterns have both theoretical and practical contributions in understanding and designing communication routing protocols, vehicle-infrastructure integration systems, and so on.

Suggested Citation

  • Jin, W L, 2010. "Modeling connectivity of inter-vehicle communication networks along discrete traffic streams," University of California Transportation Center, Working Papers qt2jd4m0ck, University of California Transportation Center.
    • Handle: RePEc:cdl:uctcwp:qt2jd4m0ck
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    References listed on IDEAS

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    1. Wang, Xiubin, 2007. "Modeling the process of information relay through inter-vehicle communication," Transportation Research Part B: Methodological, Elsevier, vol. 41(6), pages 684-700, July.
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