In the near future, autonomous vehicles (AVs) will travel sharing the current freeways with human driven vehicles. The efficiency of this mixed traffic scenario will depend on the ability of AVs to behave cooperatively. Otherwise, the introduction of uncoordinated AVs might lead to capacity reductions. Connected AVs (CAVs) traveling in platoons represents a promising management strategy to get the most from the AVs’ technological revolution. Most of previous research has used traffic microsimulation tools to assess platooning and other CAVs’ cooperative driving strategies, achieving good results. However, the robust macroscopic modeling alternative, which typically yields the necessary insights and fundamental knowledge to set the foundations for the development of management and control strategies, remains almost unexplored. This paper contributes to fill this research gap by providing a generalized macroscopic model to estimate the average CAVs platoon length for a given traffic demand and penetration rate of CAVs. Two different platooning schemes are compared (i.e. cooperative and opportunistic) representing the best and worst case scenarios. The estimation of CAVs platoon length is of much importance as it is the main factor driving capacity improvements on freeways, which under the appropriate conditions could exceed 10.000 vehicles per hour and lane.

在不久的将来，自动驾驶汽车将与人类驾驶的汽车共享当前的高速公路。这种混合流量场景的效率将取决于AV协同行为的能力。否则，引入不协调的AV可能会导致容量减少。在排中行驶的联网自动驾驶汽车（CAV）代表了一种有前途的管理策略，可以从自动驾驶汽车的技术革命中获得最大收益。以前的大多数研究都使用交通微仿真工具来评估排和其他CAV的协同驾驶策略，取得了良好的效果。但是，健壮的宏观建模替代方法（通常会产生必要的见识和基础知识，为发展管理和控制策略奠定基础）几乎尚未得到开发。本文通过提供一个广义的宏观模型来估计给定的交通需求和CAV的平均CAV排长，从而弥补了这一研究空白。比较了代表最佳和最坏情况的两种不同的排班方案（即合作和机会方案）。CAV排长的估计非常重要，因为这是高速公路上提高驾驶能力的主要因素，在适当的条件下，每小时和每车道可超过10.000辆车。