Attention Mechanism With Spatial-Temporal Joint Model for Traffic Flow Speed Prediction
Hexuan Hu, Zhen-Zhou Lin, Qiang Hu, Ye Zhang
Abstract
Intelligent transportation system (ITS) plays an important role in solving today’s transportation problems, and short-term traffic flow prediction is at its core. Deep learning can extract and capture abstract high-order features, and introducing attention mechanism to improve the performance of deep learning algorithm has been verified in many fields. Due to the complexity and randomness of traffic flow, accurate traffic flow prediction is not a simple task. Reasonable use of deep learning to predict traffic flow is of great significance to the whole transportation system. In this paper, the reason of choosing recurrent neural network (RNN) as the basic network for traffic flow prediction is explained. Aiming at the problem of gradient disappearance in practical application, the long short-term memory network (LSTM) is introduced to improve the model, and the model framework, algorithm and training process are described in detail. Attention mechanism is introduced into LSTM-RNN to build a short-term traffic flow prediction model. Applying the proposed model to observed traffic flow data, we found that the proposed model has higher prediction accuracy and model efficiency.