Connected and autonomous vehicles (CAVs) are on the way to the field application. In the beginning stage, there will be a mixed traffic flow, containing the regular human-driven vehicles and CAVs with a low penetration rate. Recently, the discussion about the impact of a small proportion of CAVs in the mixed traffic is controversial. This paper investigated the possibility of applying the limited data from these lowly penetrated CAVs to estimate the average freeway link speeds based on the Kalman filtering (KF) method. First, this paper established a VISSIM-based microsimulation model to mimic the mixed traffic with different CAV penetration rates. The characteristics of this mixed traffic were then discussed based on the simulation data, including the sample size distribution, data-missing rate, speed difference, and fundamental diagram. Accordingly, the traditional KF-based method was introduced and modified to adapt data from CAVs. Finally, the evaluations of the estimation accuracy and the sensitive analysis of the proposed method were conducted. The results revealed the possibility and applicability of link speed estimation using data from a small proportion of CAVs.

中文翻译：

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使用低渗透率的互联和自动驾驶汽车的数据估算高速公路混合交通的行车速度

联网和自动驾驶汽车（CAV）即将进入现场应用。在开始阶段，将有混合的交通流，其中包含渗透率低的常规人力车辆和CAV。最近，关于少量CAV在混合交通中的影响的讨论引起争议。本文研究了基于卡尔曼滤波（KF）方法从这些低渗透CAV中应用有限数据来估计平均高速公路连接速度的可能性。首先，本文建立了基于VISSIM的微观仿真模型，以模拟具有不同CAV渗透率的混合交通。然后基于仿真数据讨论了这种混合流量的特性，包括样本大小分布，数据丢失率，速度差和基本图。因此，引入并改进了传统的基于KF的方法，以适应CAV中的数据。最后，对估计精度进行了评估，并对所提方法进行了敏感性分析。结果揭示了使用来自一小部分CAV的数据进行链路速度估计的可能性和适用性。