Spatio-Temporal Graph Convolutional Network for Stochastic Traffic Speed Imputation

The rapid increase of traffic data generated by different sensing systems opens many opportunities to improve transportation services.
An important opportunity is to enable stochastic routing that computes the arrival time probabilities for each suggested route instead
of only the expected travel time. However, traffic datasets typically
have many missing values, which prevents the construction of stochastic speeds. To address this limitation, we propose the Stochastic
Spatio-Temporal Graph Convolutional Network (SST-GCN) architecture that accurately imputes missing speed distributions in a
road network. SST-GCN combines Temporal Convolutional Networks and Graph Convolutional Networks into a single framework
to capture both spatial and temporal correlations between road
segments and time intervals. Moreover, to cope with datasets with
many missing values, we propose a novel self-adaptive contextaware diffusion process that regulates the propagated information
around the network, avoiding the spread of false information. We
extensively evaluate the effectiveness of SST-GCN on real-world
datasets, showing that it achieves from 4.6% to 50% higher accuracy than state-of-the-art baselines using three different evaluation
metrics. Furthermore, multiple ablation studies confirm our design
choices and scalability to large road networks.