Traffic Flow

Traffic flow research aims to understand and predict vehicular movement patterns for improved transportation management. Current research heavily utilizes deep learning, particularly graph neural networks and recurrent neural networks (like LSTMs and GRUs), to model the complex spatiotemporal dependencies in traffic data, often incorporating external factors like weather and road capacity. These models are applied to various tasks, including short-term traffic forecasting, congestion detection, and optimization of traffic signal control and autonomous vehicle maneuvers. The ultimate goal is to enhance traffic efficiency, reduce congestion, and improve safety and sustainability in transportation systems.