Dynamic graph convolutional recurrent imputation network for spatiotemporal traffic missing data

In real-world intelligent transportation systems, the spatiotemporal traffic data collected from sensors often exhibit missing or corrupted data, significantly hindering the development of traffic data research. Missing data imputation is a classic research topic that encompasses a wide range of methods. However, these methods are typically underdeveloped in two aspects: the dynamic spatial dependencies of the road network over time, and the information extraction and utilization of diverse data. In this study, we design a novel deep learning architecture - Dynamic Graph Convolutional Recurrent Imputation Network (DGCRIN) - as a tool to impute missing traffic data. The DGCRIN employs a graph generator and dynamic graph convolutional gated recurrent unit (DGCGRU) to perform fine-grained modeling of the dynamic spatiotemporal dependencies of road network. Additionally, an auxiliary GRU learns the missing pattern information of the data, and a fusion layer with a decay mechanism is introduced to fuse a diverse range of information. This architecture enables the DGCRIN to be highly adaptable to complex scenarios involving missing data. Extensive experiments on two datasets demonstrate the superiority of DGCRIN over multiple baseline models.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

In this study, a novel deep learning architecture named DGCRIN is designed to impute missing traffic data in real-world intelligent transportation systems. DGCRIN utilizes a graph generator and dynamic graph convolutional gated recurrent unit (DGCGRU) to model the dynamic spatiotemporal dependencies of the road network. An auxiliary GRU learns the missing pattern information, and a fusion layer with a decay mechanism is introduced to fuse diverse data. Extensive experiments on two datasets demonstrate the superiority of DGCRIN over multiple baseline models.