Speed Prediction

Speed prediction research focuses on accurately forecasting the speed of vehicles or processes, aiming to improve efficiency and decision-making in various applications. Current research employs diverse approaches, including machine learning models like deep neural networks (particularly LSTMs and GRUs), general regression neural networks, and shared-weight multilayer perceptrons, often incorporating topographical features and traffic information for enhanced accuracy. These advancements are significant for optimizing traffic flow in intelligent transportation systems, improving energy efficiency in adaptive cruise control, and enhancing the performance of scheduling algorithms in resource-constrained environments.