Traffic Efficiency

Traffic efficiency research aims to optimize traffic flow and reduce congestion, improving safety, reducing emissions, and enhancing overall transportation system performance. Current research focuses on developing accurate predictive models using various techniques, including deep learning (e.g., LSTM networks, Graph Neural Networks, U-Nets), reinforcement learning for autonomous vehicle control, and data-driven approaches leveraging large-scale datasets from diverse sources (e.g., LiDAR, GPS, social media). These advancements are crucial for informing urban planning, optimizing traffic management strategies, and improving the safety and efficiency of both human-driven and autonomous transportation systems.