An Attention-based Ambient Network with 3D Convolutional Network for Incomplete Traffic Flow Prediction

摘要：

　　The improvement of traffic flow prediction accuracy is of great sig-nificance for an Intelligent Transportation System.However,most current prediction methods are based on the complete or relatively complete datasets.However,complete traffic datasets are not easy to obtain.In this paper,we propose a 3D Convolutional Ambient Generative Adversarial Network to predict traffic flow by using the incomplete datasets.The proposed model is able to learn the under-lying distribution of traffic flow from the incomplete traffic data and utilize the captured spatio-temporal features of traffic data for traffic flow prediction.In addition,we also introduce an attention mecha-nism into the model to improve the prediction accuracy by explor-ing the global regional structure correlation.The simulation results demonstrate that the proposed model outperforms the state-of-the-art prediction methods for traffic flow prediction when incomplete data is utilized for training.

关键词： Ambient Generative Adversarial Networks 3DConvolutional Neu-ral Networks Attention Mechanisms Traffic Data Prediction

作者: Feng Lin Haifeng Zheng Xinxin Feng

作者单位: College of Physics and Information Engineering,Fuzhou University

会议类型: 国际会议

会议名称: 2019国图灵大会(ACM Turing Celebration conference-China 2019 )

会议地点: 成都

会议语种:英文

页码: 547-551

在线出版日期: 2019-05-17（万方平台首次上网日期，不代表论文的发表时间）