Research on carbon emission analysis considering heterogeneous traffic flow with HDV driving style

Traditional models struggle to accurately capture the interaction between platoon dynamic evolution and heterogeneous Human-driven Vehicle (HDV) behaviors in mixed traffic flows. This study proposes a hybrid traffic flow modeling framework integrating discrete motion rules and dynamic platoon evolution. First, a discrete motion safety distance model is developed to resolve the inconsistency between continuous acceleration modeling and discrete cellular space by introducing integer decision rules based on cellular spacing. Second, a platoon size transition probability matrix is constructed using Markov chains to dynamically characterize the splitting, merging, and reorganization processes of platoons. Finally, multi-scenario simulation data are employed to quantify the impact mechanisms of intra-platoon spacing, reaction time, driving style, lane-changing behavior, and platoon size on CO₂ emissions. Key findings include: (1) When the Connected Automated Vehicle (CAV) penetration rate exceeds 0.6, platoon mode significantly improves traffic flow efficiency and reduces CO₂ emissions by 18.2 %–25.1 % compared to discrete mode.; (2) Optimizing intra-platoon spacing achieves a peak emission reduction of 42.5 %; (3) Lane-changing strategies must adapt to traffic density—enhancing HDV lane-changing probability under low density yields measurable reductions, while restricting CAV lane-changing probability to 0.4–0.6 under high density achieves reductions of 1.3 %–1.7 %; (4) Platoon sizes of 3–5 vehicles demonstrate optimal emission reduction efficiency. This study reveals the mechanism of queue dynamic parameters on carbon emissions, and provides theoretical support for the optimization of fleet cooperative control strategies and the design of low-carbon transportation systems.