As autonomous driving technology advances, connected and autonomous vehicles (CAVs) will coexist with human-driven vehicles (HDVs) for an extended period. The deployment of CAVs will alter traffic flow characteristics, making it crucial to investigate their impacts on mixed traffic. This study develops a hybrid control framework that integrates a platoon control strategy based on the “catch-up” mechanism with lane management for CAVs. The impacts of the proposed hybrid control framework on mixed traffic flow are evaluated through a series of macroscopic simulations, focusing on fundamental diagrams, traffic oscillations, and safety. The results illustrate a notable increase in road capacity with the rising market penetration rate (MPR) of CAVs, with significant improvements under the hybrid control framework, particularly at high MPRs. Additionally, traffic oscillations are mitigated, reducing shockwave propagation and enhancing efficiency under the hybrid control framework. Four surrogate safety measures, namely time to collision (TTC), criticality index function (CIF), deceleration rate to avoid a crash (DRAC), and total exposure time (TET), are utilized to evaluate traffic safety. The results indicate that collision risk is significantly reduced at high MPRs. The findings of this study provide valuable insights into the deployment of CAVs, using control strategies to improve mixed traffic flow operations.

The rapid development of Connected and Autonomous Vehicles (CAVs) presents challenges in managing mixed traffic flows. Previous studies have primarily focused on mixed traffic flow involving CAVs and Human-Driven Vehicles (HDVs), or on the combination of trucks and cars. However, these studies have not fully addressed the heterogeneous mixed traffic flow consisting of CAVs and HDVs, including trucks and cars, influenced by varying human driving styles. Therefore, this study investigates the influences of the market penetration rate (MPR) of CAVs, truck proportion, and driving style on operational characteristics in heterogeneous mixed traffic flow. A total of 1105 events were extracted from highD dataset to analyze four car-following types: car-following-car (CC), car-following-truck (CT), truck-following-car (TC), and truck-following-truck (TT). Principal Component Analysis (PCA) and clustering techniques were employed to categorize distinct driving styles, while the Intelligent Driver Model (IDM) was calibrated to represent the various car-following behaviors. Subsequently, microscopic simulations were conducted using the Simulation of Urban Mobility (SUMO) platform to evaluate the impact of CAVs on sustainable traffic operations, including road capacity, stability, safety, traffic oscillations, fuel consumption, and emissions under various traffic conditions. The results demonstrate that CAVs can significantly enhance road capacity, improve emissions, and stabilize traffic flow at high MPRs. For instance, when the MPR increases from 40% to 80%, the road capacity improves by approximately 25%, while stability enhances by approximately 33%. In contrast, higher truck proportions lead to reduced capacity, increased emissions, and decreased traffic flow stability. In addition, an increased proportion of mild drivers reduces capacity, raises emissions per kilometer, and improves stability and safety. However, a high proportion of mild human drivers (e.g., 100% mild drivers) may negatively impact traffic safety when CAVs are present. This study provides valuable insights into evaluating heterogeneous traffic flows and supports the development of future traffic management strategies for more sustainable transportation systems.