Traffic state estimation is a fundamental task of Intelligent Transportation Systems (ITS). Recent advances in sensor technology and emerging computer and vehicular communications paradigms have brought the task of estimating traffic state parameters in real time within reach. Recognizing this, the US-DOT started promoting the Connected Vehicles (CV) initiative. By using wireless connectivity between the vehicles participating in the traffic, the CV initiative aims to promote an increased awareness of real-time traffic conditions and, as a result, to reduce the number and severity of crashes.

A number of recent papers have proposed CV-based approaches to estimating traffic state parameters including density and flow. However, virtually all the CV-based approaches for density estimation also rely on communication with stationary detectors and other pre-deployed roadside infrastructure. This assumption is problematic since such infrastructure is often not available.

The main contribution of this paper is to propose a simple and easy to implement real-time traffic density estimation method that uses only vehicle-to-vehicle communications and the on-board sensing capabilities of present-day vehicles. In our method, using their on-board devices, vehicles maintain a tally that keeps track of the difference between the number of times other vehicles pass them and the number of times they pass other vehicles. Notice that since vehicles may vary their speed as they please, they may pass and be passed by the same vehicle multiple times and, consequently, maintaining a correct tally is a non-trivial task. We show that the tallies computed by vehicles relate, in an interesting way, to traffic density.

We provide a detailed proof of our method using techniques that avoid the use of common simplifications inherent to visual time-space traffic diagrams. Furthermore, we demonstrate the accuracy of our method through extensive simulations using real NGSIM traffic traces along with SUMO-generated synthetic traffic traces.

交通状态估计是智能交通系统（ITS）的一项基本任务。传感器技术以及新兴的计算机和车辆通信范例的最新进展带来了实时估算交通状况参数的任务。认识到这一点，美国交通运输部（DOT）开始推广互联汽车（CV）计划。通过使用参与交通的车辆之间的无线连接，CV计划旨在提高人们对实时交通状况的认识，从而减少交通事故的数量和严重程度。

最近的许多论文提出了基于CV的方法来估计交通状态参数，包括密度和流量。但是，实际上，所有基于CV的密度估计方法也都依赖于与固定探测器和其他预先部署的路边基础设施的通信。该假设是有问题的，因为这种基础结构通常不可用。

本文的主要贡献是提出一种简单且易于实现的实时交通密度估计方法，该方法仅使用车对车通信和当今车辆的车载感应功能。在我们的方法中，车辆使用其车载设备来保持一个记号，以跟踪其他车辆通过它们的次数与它们通过其他车辆的次数之间的差异。请注意，由于车辆可能会随意改变速度，因此它们可能会多次通过同一辆车辆，因此，保持正确的提示是一项艰巨的任务。我们显示，车辆计算的计数以一种有趣的方式与交通密度相关。

我们使用避免使用视觉时空交通图固有的常见简化方法的技术，提供了我们方法的详细证明。此外，我们通过使用真实的NGSIM交通轨迹以及SUMO生成的合成交通轨迹进行广泛的仿真，证明了我们方法的准确性。