  Platooning of connected and autonomous vehicles (CAVs) is an emerging
technology with a strong potential for throughput improvement and fuel
reduction. Adequate macroscopic models are critical for system-level efficiency
and reliability of platooning. In this paper, we consider a hybrid queuing
model for a mixed-autonomy highway section and develop an easy-to-use training
algorithm. The model predicts CAV and non-CAV counts according to the traffic
demand as well as key parameters of the highway section. The training algorithm
learns the highway parameters from observed data in real time. We test the
model and the algorithm in Simulation of Urban Mobility (SUMO) and show that
the prediction error is around 15% in a stationary setting and around 25% in a
non-stationary setting. We also show that the trained model leads to a platoon
headway regulation policy very close to the simulated optimum. The proposed
model and algorithm can directly support model-predictive decision-making for
platooning in mixed autonomy.
