Dynamic Stability Analysis and Optimization of Multi-Vehicle Systems in Heterogeneous Connected Traffic

Area of Science:

• Transportation Engineering
• Traffic Flow Theory
• Intelligent Transportation Systems

Background:

• Mixed traffic flows comprise human-driven vehicles (HDVs), connected autonomous vehicles (CAVs), autonomous vehicles (AVs), and connected human-driven vehicles (CHVs).
• Interactions in heterogeneous traffic are complex, necessitating advanced modeling for stability analysis.

Purpose of the Study:

• To develop and validate a refined CAV car-following model for mixed-traffic environments.
• To identify key parameters that enhance traffic flow stability in heterogeneous traffic conditions.

Main Methods:

• A refined CAV car-following model incorporating multi-vehicle state information (headway, velocity differences, acceleration, memory effects) was developed.
• Theoretical analysis of the model's linear and nonlinear stability was performed.
• Numerical simulations were conducted for braking, start-up, and ring road scenarios.

Main Results:

• The proposed model effectively suppresses traffic congestion and reduces oscillations.
• Key parameters influencing flow stability in mixed environments were identified.
• Simulations validated the model's efficacy in improving traffic flow stability.

Conclusions:

• The refined CAV model offers valuable insights into connected vehicle behavior in mixed traffic.
• CAV-based strategies show significant potential for enhancing safety and efficiency in future transportation systems.