  Traffic speed forecasting is an important task in intelligent transportation
system management. The objective of much of the current computational research
is to minimize the difference between predicted and actual speeds, but
information modalities other than speed priors are largely not taken into
account. In particular, though 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 novel
``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 workzone event.
