Vehicular ad hoc networks (VANETs) provide the infrastructure for the intelligent transportation system (ITS). The number of vehicles that are capable of communicating with each other through VANET is increasing. In this paper, we provide several analytical results regarding the message propagation on the highway in VANET. In our scenario, there is a stationary message source (i.e., alert messages are generated at an accident). Vehicles can receive the message and get informed when they reach the radio transmission range of the message source or another informed vehicle. Most papers published on the analysis of message propagation assume that the inter arrival times between vehicles follow a Poisson process; there are very few results available with more general traffic model. In this paper, we show that the Poisson process is not always suitable for modeling vehicle traffic. Instead of the Poisson process, we propose to use the more general Markovian arrival process (MAP) to model the vehicle headway times and derive the probability that the message propagates beyond a certain distance from the accident under this traffic assumption. Through several numerical examples, we demonstrate how much the statistics of the vehicle arrival process impacts the message propagation distance.

中文翻译：

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假设马尔可夫车辆到达过程的VANET中高速公路警报消息的传播分析

车载自组织网络（VANET）为智能运输系统（ITS）提供基础设施。能够通过VANET相互通信的车辆数量正在增加。在本文中，我们提供了有关VANET在高速公路上的消息传播的几种分析结果。在我们的方案中，有一个固定的消息源（即，在意外情况下生成警报消息）。车辆可以到达消息源或另一辆知情车辆的无线电传输范围时接收消息并得到通知。有关消息传播分析的大多数论文都假设车辆之间的到达时间遵循泊松过程；对于更通用的流量模型，几乎没有可用的结果。在本文中，我们证明了泊松过程并不总是适合于对车辆交通进行建模。代替泊松过程，我们建议使用更通用的马尔可夫到达过程（MAP）对车辆行驶时间进行建模，并在此交通量假设下得出消息传播距事故一定距离的概率。通过几个数值示例，我们演示了车辆到达过程的统计数据对消息传播距离的影响。