智能网联环境下基于混合深度学习的交通流预测模型
摘要
To achieve refined traffic flow prediction under connected and automated vehicle highway (CAVH) environment, this study proposes a lane-level traffic flow prediction model based on the hybrid deep learning (HDL). The proposed method takes the advantages of powerful data collection and calculation capability of the CAVH system. The HDL model divided the raw traffic speed series into several intrinsic mode function components and one residual component, and used the components as the input of the model. The bidirectional long short-term memory neural network and attention mechanism were used to establish the framework of the deep learning model. The lane-level speeds of the 2nd Ring road in Beijing, China were utilized to examine the accuracy and reliability of the proposed model. The results illustrate that the HDL model has ideal prediction performance at different types of lanes. Meanwhile, the prediction accuracy of the HDL model is significantly higher than that of previous models in terms of single-step-ahead prediction and multi-step-ahead prediction.