City buses’ future velocity prediction for multiple driving cycle: A meta supervised learning solution

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.