Ramp Vehicle

Ramp vehicle integration into highway traffic flow is a crucial area of transportation research focused on optimizing merging efficiency and safety. Current research emphasizes developing accurate models of driver behavior during merging, often employing machine learning techniques like transfer learning and inverse reinforcement learning to predict and manage interactions between on-ramp and mainline vehicles. These advancements aim to improve traffic flow, reduce congestion, and enhance the safety of both human-driven and autonomous vehicles, particularly in the context of ramp metering strategies and connected vehicle technologies. Ultimately, this research contributes to the development of more efficient and safer highway systems.