An epidemiological diffusion framework for vehicular messaging in general transportation networks
An epidemiological diffusion framework for vehicular messaging in general transportation networks
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DOI：
10.1016/j.trb.2019.11.004
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发表时间：
2020-01-01
影响因子：
6.8
通讯作者：
Starobinski, David
中科院分区：
文献类型：
--
作者：
Kim, Jungyeol;Sarkar, Saswati;Starobinski, David
新兴的车对车（V2V）技术有望为共享交通的安全和增长做出重大贡献，前提是这些技术的部署能够克服挑战。本文关注的是这样一个挑战：描述在不同交通和通信条件下，接收到信息的车辆比例，作为空间和时间的函数。车对车技术连接了两个基础设施：通信和运输。这些基础设施相互联系，相互依赖。为了捕捉这种可能随时间和空间变化的相互依赖性，我们提出了一种新的方法来建模支持v2v的车辆之间的信息传播。该模型基于连续时间马尔可夫链，在适当的条件下，该链收敛于一组聚类流行病学微分方程。接收到信息的车辆的比例，作为空间和时间的函数，可以作为这些微分方程的解得到，它可以有效地求解，而不依赖于车辆的数量。这些特征可以构成评估V2V系统的几个属性的基础，我们将演示其中的一些属性。这些特征使它们能够进行各种概括，并捕捉到通信和移动之间的各种相互依赖关系。作为模型的测试，我们在使用微观交通轨迹的现实环境中以及在中断和系统扰动的假设场景中提供了应用程序：我们从两个实际轨迹数据集以及从原点/目的地矩阵生成的合成轨迹数据集中发现了与微观轨迹的良好模型一致性。(C) 2019 Elsevier Ltd.版权所有。
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.