## 6 Empirical Research on New Heterogeneous Traffic Control Mechanisms
### Summary  
Table 3 summarizes the main methods and results of empirical research on new heterogeneous traffic control mechanisms. Through systematic analysis of simulation environments, real environments, and exploratory research oriented toward the future, we can draw the following conclusions:  
1. During the transition period with relatively low penetration rates, utilizing limited CAV resources to stabilize traffic flow and optimize intersection operations has already demonstrated technical feasibility. Through carefully designed control strategies, even a small number of CAVs can positively impact overall traffic. 2. Multi-level, distributed control architectures perform excellently in balancing computational complexity and control effectiveness, serving as an effective approach to address the complexity of heterogeneous traffic environments. Decomposing control problems into different levels or modules can achieve system collaborative optimization while maintaining computational efficiency. 3. Vehicle-infrastructure collaboration is a key enabling technology for heterogeneous traffic control. Real environment tests show that through information interaction between vehicles and infrastructure, system performance can be significantly enhanced, especially in structured environments such as signalized corridors. 4. Autonomous intersection management without signals and regional-level collaborative control oriented toward future high penetration rate environments demonstrate the revolutionary transformation potential of traffic control systems. Although these technologies are still distant from large-scale applications, they provide important theoretical and technical reserves for long-term development. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Evaluation Dimension** & **Research From** & **Main Results** & **Key Indications** & **Existing Problems** & **Development Direction** \\ \hline \multirow{9}{*}{Simulation Environment Research} & \multirow{6}{*}{Traffic Flow Stability Control} & Oscillation Management Model & Effectively, stepwise, cross collimations, & Low CAV penetration rate & \\  & & Predictive Control (GR-MPC) & even with shorter CAV distances (100 m) & even temperature intermittent traffic & \\  & & Predictive Control (GR-MPC) & even with shorter CAV distances (100 m) & wave conditions & \\ \cline{2-6}  & & & Contain uncertainties in preceding and & Highly uncertain homogeneous traffic & \\  & & & Schwartz studies in expectation & Heavy versus independent traffic & \\  & & & & & \\  & & Robust Fuzzy MPC (GR-MPC) Strategy & & stability and control of mixed patterns & Reinforcement vehicle-dynamics & \\  & & & & & \\  & & & Quantities the energy bound of & & & \\  & & Ring-String Stability (DS) & established by a single MC simulation & Theoretical research-optimizes a & \\  & & Analysis Framework & power control through multi-rotor flow & deepnessness traffic & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \multirow{9}{*}{Simulation Environment Research} & \multirow{6}{*}{Two-level Hierarchical Framework} & \multirow{6}{*}{Two-level Hierarchical Framework} & Upper Input application using  
The comprehensive comparison tables presented throughout this review (Tables 1-3) provide essential technical and performance insights but reveal a critical gap in practical deployment assessment. Future research should systematically evaluate deployment readiness through standardized maturity frameworks, infrastructure requirement specifications, and cost-benefit analyses that are currently absent from the literature. This evaluation framework should distinguish between research prototypes, pilot-ready technologies, and commercially viable solutions while documenting the specific infrastructure investments, communication requirements, and system integration costs necessary for implementation. A critical limitation identified in our review is the absence of standardized performance metrics across different studies, preventing meaningful quantitative synthesis and meta-analysis.