Accounting for Work Zone Disruptions in Traffic Flow Forecasting via Multi‐Channel Attention‐Based Spatio‐Temporal Graph Convolutional Networks

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Abstract
Traffic speed forecasting is an important task in intelligent transportation system management. In current forecasting models, information modalities other than speed priors are largely not considered. While state of the art performance is achieved on speed forecasting with graph neural network methods, these methods do not incorporate information on roadway maintenance work zones and their impacts on predicted traffic flows; yet the impacts of construction work zones are of significant interest to roadway management agencies because they translate to impacts on the local economy and public well‐being. In this paper, we build over the convolutional graph neural network architecture and present a new ‘Graph Convolutional Network for Roadway Work Zones’ model that includes a novel data fusion mechanism and a new heterogeneous graph aggregation methodology to accommodate work zone information in spatio‐temporal dependencies among traffic states. The model is evaluated on two data sets that capture traffic flows in the presence of work zones in the Commonwealth of Virginia. Extensive comparative evaluation and ablation studies show that the proposed model can capture complex and nonlinear spatio‐temporal relationships across a transportation corridor, outperforming baseline models, particularly when predicting traffic flow during a work zone event.

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