Heterogeneous Traffic

Heterogeneous traffic, encompassing diverse vehicle types and behaviors, presents significant challenges for efficient and safe transportation systems. Current research focuses on developing advanced control and coordination strategies, often employing reinforcement learning and game theory to optimize traffic flow in complex scenarios, including unsignalized intersections and congested urban areas. These efforts leverage both microscopic vehicle models (capturing individual vehicle dynamics) and macroscopic models (analyzing overall traffic flow) to improve traffic management, reduce congestion, and enhance safety, particularly for autonomous vehicles operating within mixed traffic environments. The resulting improvements in traffic efficiency and safety have broad implications for urban planning, transportation infrastructure design, and the development of autonomous driving technologies.