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Forecasting traffic flows is a central task in intelligent transportationsystem management. Graph structures have shown promise as a modeling framework,with recent advances in spatio-temporal modeling via graph convolution neuralnetworks, improving the performance or extending the prediction horizon ontraffic flows. However, a key shortcoming of state-of-the-art methods is theirinability to take into account information of various modalities, for instancethe impact of maintenance downtime on traffic flows. This is the issue weaddress in this paper. Specifically, we propose a novel model to predicttraffic speed under the impact of construction work. The model is based on thepowerful attention-based spatio-temporal graph convolution architecture bututilizes various channels to integrate different sources of information,explicitly builds spatio-temporal dependencies among traffic states, capturesthe relationships between heterogeneous roadway networks, and then predictschanges in traffic flow resulting from maintenance downtime events. The modelis evaluated on two benchmark datasets and a novel dataset we have collectedover the bustling Tyson's corner region in Northern Virginia. Extensivecomparative experiments and ablation studies show that the proposed model cancapture complex and nonlinear spatio-temporal relationships across atransportation corridor, outperforming baseline models.

Traffic Flow Forecasting with Maintenance Downtime via Multi-Channel Attention-Based Spatio-Temporal Graph Convolutional Networks