The future of mobility involves the interconnection of the entities of the transportation system (vehicles, roads, traffic lights, pedestrians) in high speed networks providing real-time information to drivers, entertainment for passengers, and a wide variety of applications and systems dedicated to smart transportation. Furthermore, in a few years, autonomous vehicles are going to massively reach the streets, and their interconnection may drastically improve the urban mobility by reducing the travel time and the number of accidents. In this work, we consider the design and management of the network infrastructure for vehicular communication focusing on streaming delivery. We intend to allow a given share of vehicles driving along the road network permanently playing streams received from the network infrastructure, and our main question is where we must provide coverage for achieving a given share of vehicles receiving the media. As parameters, we consider the download data rate that vehicles receive content from the infrastructure, and data consumption rate inside vehicles. An Integer Linear Program formulation along with a tabu search-based heuristic are presented. We consider as baseline the intuitive deployment strategy of covering the most popular locations of the road network. All strategies are evaluated considering a realistic vehicular mobility trace composed of 75, 515 vehicles. Results indicate that the tabu search heuristic is able to solve a large instance composed of 75, 515 vehicles requiring less covered area than greedy heuristics. Considering the optimal solution, we investigate the solutions on a reduced subset composed of 100 vehicle trips and, considering this reduced scenario, the tabu search heuristic is able to find the optimal solution.

未来的移动性涉及高速网络中运输系统实体（车辆，道路，交通信号灯，行人）的互连，从而为驾驶员提供实时信息，为乘客提供娱乐，以及各种专用于此的应用程序和系统智能交通。此外，在短短几年内，自动驾驶汽车将大量到达街道，它们的相互连接可以通过减少出行时间和减少事故数量来大大改善城市的机动性。在这项工作中，我们考虑了针对车载通信的网络基础架构的设计和管理，重点是流传输。我们打算允许一定份额的沿道路网络行驶的车辆永久播放从网络基础设施收到的信息流，我们的主要问题是，我们必须在哪些方面提供覆盖，以实现一定比例的接收媒体车辆。作为参数，我们考虑车辆从基础设施接收内容的下载数据速率以及车辆内部的数据消耗速率。整数线性程序公式以及提出了基于禁忌搜索的启发式算法。我们以涵盖道路网最流行位置的直观部署策略为基线。评估所有策略时均考虑到由75、515辆车组成的逼真的车辆行驶轨迹。结果表明，禁忌搜索 启发式算法能够解决由75，515辆车辆组成的大型实例，与贪婪启发式算法相比，其需要的覆盖区域更少。考虑到最佳解决方案，我们研究了由100次车辆出行组成的减少子集上的解决方案，考虑到这种减少的情况，禁忌搜索 启发法能够找到最佳解决方案。