一般V2V-enabled交通网络中的信息传播建模
Modeling Information Propagation in General V2V-enabled Transportation Networks
摘要：
V2V技术连接了两个基础设施：通信基础设施和交通基础设施。这些基础设施是相互联系和相互依赖的。一方面，通信网络支持V2V交互，而另一方面，启用V2V的道路上的车辆密度和道路上的拥堵程度决定了车辆和基础设施之间的通信速度和质量。预计V2V技术将对共享移动的增长做出重大贡献，反过来，在共享移动服务中部署大量联网车辆将大大推动V2V技术的发展，前提是可以克服部署方面的挑战。车辆移动模式和通信条件不仅是异构的，而且它们还不断演变，导致通信和交通基础设施之间的动态耦合。我们认为在交通网络中的车辆之间的消息的通信，并估计如何快速的消息传播在不同的交通密度条件下（交通拥堵，事故的存在，以及一天中的时间，如早上和晚上的高峰时间）和通信条件。我们开发了一个连续时间马尔可夫链来描述通过使能车辆的信息传播过程。我们的模型收敛到一组群集的流行病学微分方程，这有助于快速计算的解决方案。然后，我们证明了该模型在各种情况下的适用性：无论是现实世界的情况下，假设的停电和系统扰动的情况。我们发现，我们的模型与实际轨迹数据相匹配，误差很小，这表明我们的模型适用于研究通过联网车辆网络传播的信息。
英文摘要：
V2V technologies bridge two infrastructures: the communications infrastructure and the transportation infrastructure. These infrastructures are interconnected and interdependent. On the one hand, the communications network enables V2V interactions, while, on the other hand, the density of vehicles on the roadway enabled with V2V and the level of congestion on the roadway determine the speed and quality of communications between vehicles and infrastructure. The V2V technology is expected to contribute significantly to the growth of shared mobility, in turn, receives a significant boost from the deployment of a large number of connected vehicles in shared mobility services, provided challenges towards the deployment can be overcome. Vehicle mobility patterns and communication conditions are not only heterogeneous, but they also evolve constantly, leading to dynamic coupling between the communication and the transportation infrastructure. We consider the communication of messages amongst the vehicles in a transportation network, and estimate how quickly messages spread under different conditions of traffic density (traffic congestion, the presence of an accident, and time of day such as morning and evening rush hour) and communication conditions. We developed a continuous-time Markov chain to describe the information propagation process through enabled vehicles. Our models converge to a solution of a set of clustered epidemiological differential equations which lend itself to fast computation. We then demonstrate the applicability of this model in various scenarios: both real-world scenarios and hypothesized scenarios of outages and system perturbations. We find that our models match actual trajectory data with very little error, demonstrating the applicability of our models to study the spread of information through a network of connected vehicles.
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