Traffic Estimation

Traffic estimation aims to accurately predict traffic flow and travel times, crucial for efficient transportation management and planning. Current research focuses on developing sophisticated models, including macroscopic flow models enhanced by machine learning (e.g., neural networks, LSTM networks, and graph neural networks), and vision-based systems using deep learning for real-time traffic intensity assessment. These advancements improve prediction accuracy, particularly in areas with limited sensor coverage, leading to better traffic control strategies and reduced congestion. The ultimate impact is improved transportation efficiency and reduced societal costs associated with traffic delays.