A wide variety of sensor technologies are recently being adopted for traffic monitoring applications. Since most of these technologies rely on wired infrastructure, the installation and maintenance costs limit the deployment of the traffic monitoring systems. In this paper, we introduce a traffic monitoring approach that exploits physical layer samples in vehicular communications processed by machine learning techniques. We verify the feasibility of our approach with extensive simulations and real-world experiments. First, we simulate wireless channels under realistic traffic conditions using a ray-tracing simulator and a traffic simulator. Next, we conduct experiments in a real-world environment and collect messages transmitted from a roadside unit (RSU). The results show that we are able to predict different levels of service with an accuracy above 80% both on the simulation and experimental data. Further, the proposed approach is capable of estimating the number of vehicles with a low mean absolute error on both data. Our approach is suitable to be deployed alongside the current monitoring systems. It doesn't require additional investment in infrastructure since it relies on existing vehicular networks.

翻译：近年来，多种传感器技术被广泛应用于交通监控领域。由于大多数此类技术依赖有线基础设施，其安装和维护成本限制了交通监控系统的部署规模。本文提出一种交通监控方法，该方法利用机器学习技术处理车载通信中的物理层采样数据。通过大量仿真和真实场景实验，我们验证了该方法的可行性。首先，结合射线追踪仿真器与交通仿真器，在真实交通条件下模拟无线信道。其次，在真实环境中开展实验，收集来自路侧单元（RSU）的消息传输。结果表明，在仿真与实验数据上，我们能够以超过80%的准确率预测不同服务等级。此外，所提方法在两组数据上均能以较低的平均绝对误差估算车辆数量。该方法可部署于现有监控系统旁，由于基于现有车载网络，无需额外的基础设施投资。