  Missing data is an inevitable and ubiquitous problem for traffic data
collection in intelligent transportation systems. Despite extensive research
regarding traffic data imputation, there still exist two limitations to be
addressed: first, existing approaches fail to capture the complex
spatiotemporal dependencies in traffic data, especially the dynamic spatial
dependencies evolving with time; second, prior studies mainly focus on randomly
missing patterns while other more complex missing scenarios are less discussed.
To fill these research gaps, we propose a novel deep learning framework called
Dynamic Spatiotemporal Graph Convolutional Neural Networks (DSTGCN) to impute
missing traffic data. The model combines the recurrent architecture with
graph-based convolutions to model the spatiotemporal dependencies. Moreover, we
introduce a graph structure estimation technique to model the dynamic spatial
dependencies from real-time traffic information and road network structure.
Extensive experiments based on two public traffic speed datasets are conducted
to compare our proposed model with state-of-the-art deep learning approaches in
four types of missing patterns. The results show that our proposed model
outperforms existing deep learning models in all kinds of missing scenarios and
the graph structure estimation technique contributes to the model performance.
We further compare our proposed model with a tensor factorization model and
find distinct behaviors across different model families under different
training schemes and data availability.

Machine Learning;Artificial Intelligence