Message Passing Period-Aware Imputation Network for Spatial-Temporal Traffic Missing Data

Yunliang Jiang, Yuanqing Tang, Xiongtao Zhang, Jungang Lou, Yong Liu, Zhen Yang

Abstract: Intelligent Transportation System (ITS) is a critical component of smart cities, however, certain issues significantly limit the construction of ITS. On the one hand, as the core resource of ITS, traffic data often suffers from missing values due to sensor failure, communication interruption, and so on. On the other hand, traffic flow change is a complex dynamic process that resulted from periodic changes caused by social activities, which increases the difficulty of data completion. To address these issues, the Message Passing Period-Aware Imputation Network (MPPAIN) is proposed. Firstly, the spatial-temporal information is transmitted sequentially in time order by the Message Passing Block based on gated recurrent unit, and the missing values are preliminarily estimated. Then, the output is fed to the Period-Aware Block to find the main frequency components that represent the changes of the traffic flow in the frequency domain through the Fourier transform. Subsequently, the traffic flow is divided according to the major periods, and the second time estimation is completed by extracting the intra-periodic and inter-periodic features simultaneously through the convolutional neural network. Finally, a bi-directional structure is designed by reversing the input traffic flow in time order to further extract the spatial-temporal and periodic features from the future to the past. Experiments demonstrate that the proposed model has excellent data imputation capabilities in various simulated missing rates and missing scenarios on several real traffic datasets.

External IDs:dblp:journals/tits/JiangTZLLY25