Research on the macroscopic characteristics of heterogeneous traffic flow under the influence of autonomous vehicle takeover behavior

The study of macroscopic characteristics in heterogeneous traffic flows incorporating L3 automated vehicle(TOV) is of significant importance for advancing traffic flow theory, enhancing the robustness of cooperative control strategies for connected and automated vehicles, and supporting high precision traffic management. To address the research gap concerning takeover behavior in macroscopic fundamental diagram(MFD) studies, this paper develops an integrated methodology combining theoretical and experimental modeling. The theoretical framework incorporates the dual phase operation of TOV, heterogeneous car following modes, and cooperative adaptive cruise control(CACC) degradation mechanisms. For experimental framework, we implemented a two phase, multi cycle experimental framework through Sumo and Python cosimulation, where an enhanced underwood model was calibrated against experimental data to establish the experimental MFD. The validation demonstrated strong consistency between theoretical and experimental models. The analysis reveals that while increased penetration of TOV substantially improves road capacity (achieving up to 184.1 % enhancement in takeover free scenarios), takeover behavior significantly undermines this benefit (causing up to 62.3 % capacity reduction under high takeover rates). These findings provide valuable insights for developing effective traffic management strategies and designing resilient automated driving systems.