Macroscopic modeling of connected, autonomous and human-driven vehicles: A pragmatic perspective

Several interdisciplinary studies have investigated the impact of Connected and Autonomous Vehicles (CAVs) on the performance of traffic networks, which expect positive effects. Nevertheless, there will be a transitional period during which both Human-Driven Vehicles (HDVs) and CAVs shall operate simultaneously. Adequate modeling of the interactions between CAVs and HDVs is vital to understand the mixed traffic dynamics. We propose a second-order macroscopic model by reconstructing the backward propagation speed of perturbation based on the dynamic headway distance between vehicles in mixed traffic. The proposed model is validated using microscopic simulations, and it replicates the given traffic scenarios subjected to assorted Penetration Rate (PR) of CAVs. The proposed model is employed to investigate the dynamics of mixed traffic. The results demonstrate that the average traffic velocity and the Level of Service (LOS) significantly improve with the increase in the PR of CAVs. Additionally, the performance of the proposed model is compared with the well-known Jiang-Qing-Zhu (JQZ) model, and it outperforms the JQZ model. The proposed model can be employed in traffic forecasting and real-time traffic control.