Urban Traffic Flow Completion with Multi-view Attention Mechanism

Traffic flow information is an important basis for intelligent transportation systems and urban computing.Traffic flow data is a new type of time series data.Due to the data collection method and the influence of external complex factors,the phenomenon of data loss is common and unavoidable.How to effectively mine the spatial-temporal characteristics of traffic flow data and the correlation between the data becomes the key to improve the missing data completion accuracy.Traditional statistical methods cannot meet the increasingly complex data requirements,and the application of deep learning promotes the development of missing data completion methods to higher accuracy.The article deeply analyzes the spatial-temporal characteristics of traffic flow,makes assumptions about the missing traffic flow,and proposes a UMAtNet (U-net with Multi-view Attention Mechanisms) traffic flow complement model.The model fuses closeness,trend and period time data with spatial data,and adopts diffe-rent data correlation measurement methods to fuse a multi-view attention mechanism,which can optimize the impact of the model on the spatial correlation of missing data.In order to verify the model,we use the open source data set of Beijing traffic data in the experiment,and analyzes in detail the influence of each part of the model and the loss function on the completion accuracy.The experimental results show that the fusion of UMAtNet and corresponding components can further improve the completion accuracy.