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An epidemiological diffusion framework for vehicular messaging in general transportation networks

机译：通用交通网络中车辆通讯的流行病学扩散框架

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Emerging Vehicle-to-Vehicle (V2V) technologies are expected to significantly contribute to the safety and growth of shared transportation provided challenges towards their deployment can be overcome. This paper focuses on one such challenge: characterizing the fraction of vehicles which have received a message, as a function of space and time, and operating under different traffic and communication conditions. V2V technologies bridge two infrastructures: communication and transportation. These infrastructures are interconnected and interdependent. To capture this inter-dependence, which may vary in time and space, we propose a new methodology for modeling information propagation between V2V-enabled vehicles. The model is based on a continuous-time Markov chain which is shown to converge, under appropriate conditions, to a set of clustered epidemiological differential equations. The fraction of vehicles which have received a message, as a function of space and time may be obtained as a solution of these differential equations, which can be solved efficiently, independently of the number of vehicles. Such characterizations can form the basis of assessing several attributes of V2V systems, some of which we demonstrate. The characterizations lend themselves to a variety of generalizations and capture various interdependencies between communication and mobility. As tests of the model we provide applications both in real-world settings using microscopic traffic traces and in postulated scenarios of outages and system perturbations: we find good model agreement with microscopic trajectory from two actual trajectory datasets, as well as a synthetic trajectory dataset generated from the origin/destination matrix. (C) 2019 Elsevier Ltd. All rights reserved.
机译：如果可以克服新兴的车对车（V2V）技术所面临的挑战，则有望为共享交通的安全和增长做出巨大贡献。本文着眼于这样一个挑战：根据空间和时间来表征接收消息的车辆比例，并在不同的交通和通信条件下运行。 V2V技术桥接了两个基础设施：通信和运输。这些基础架构是相互联系和相互依存的。为了捕获这种可能在时间和空间上变化的相互依存关系，我们提出了一种新的方法来对启用V2V的车辆之间的信息传播进行建模。该模型基于连续时间马尔可夫链，在适当的条件下，该马尔可夫链已收敛到一组聚类的流行病学微分方程。可以作为这些微分方程的解获得作为空间和时间的函数的已接收消息的车辆的比例，这些微分方程的求解可以独立于车辆的数量而有效地求解。这样的表征可以构成评估V2V系统几个属性的基础，我们已经证明了其中一些。这些特征有助于进行各种概括，并捕获了通信和移动性之间的各种相互依赖性。作为模型测试，我们在现实环境中使用微观交通轨迹以及在假设的中断和系统扰动情况下提供了应用程序：我们从两个实际轨迹数据集以及生成的合成轨迹数据集中找到了与微观轨迹的良好模型一致性来自起点/终点矩阵。 （C）2019 Elsevier Ltd.保留所有权利。

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