关键词：汽车;通信网络;算法;表征;高速公路通信;图像;流动性;建模;交通控制;交通密度
摘 要：Future vehicular networks shall enable new classes of services and applications for car-to-car and car-to-roadside communication. The underlying vehicular mobility patterns significantly impact the operation and effectiveness of these services, and hence it is essential to model and characterize such patterns. In this paper, we examine the mobility of vehicles as a function of traffic density of more than 800 locations from six major metropolitan regions around the world. The traffic densities are generated from more than 25 million images and processed using background subtraction algorithm. The resulting vehicular density time series and distributions are then analyzed. It is found using the goodness-of-fit test that the vehicular density distribution follows heavy-tail distributions such as Log-gamma, Loglogistic, and Weibull in over 90of these locations. Moreover, a heavy-tail gives rise to long-range dependence and self-similarity, which we studied by estimating the Hurst exponent (H).