City buses’ future velocity prediction for multiple driving cycle: A meta supervised learning solution - Jianfei Cao; Hongwen He; Xing Cui (IET Intelligent Transport Systems, 2021)
Abstract Vehicular velocity prediction is of great significance to intelligent transportation system, as it provides a possible future velocity sequence for vehicle's decision‐making system. A velocity prediction method via meta learning is proposed, which provides an adaptive and generative framework for multiple‐driving cycles. The prediction model is devised using a deep neural network structure. The model's training is performed by the recently proposed meta‐supervised learning, which ensures that one trained model could meet the adaptability to multiple driving cycles. The complete framework consists of three parts: Pre‐training, fine‐tune‐training and real‐time prediction, which is tested to predict the hybrid electric city buses’ future velocity in a variable traffic scenario. The average prediction accuracy of 3, 5 and 10 s horizons is 0.51, 0.63 and 0.88 m s−1, which is 25.9%, 16.78% and 7.47% higher than that trained by the conventional supervised learning method. As suggested, the proposed prediction method is effective and could meet the requirement of energy‐saving control for hybrid electric city buses. With further study, potential application of this method may also exist in the field of driving behaviour prediction and transportation mode recognition.