Yuanjie Lu

Yuanjie Lu, Amarda Shehu, David Lattanzi

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.

Yuanjie Lu, Parastoo Kamranfar, David Lattanzi et al.

Forecasting traffic flows is a central task in intelligent transportation system management. Graph structures have shown promise as a modeling framework, with recent advances in spatio-temporal modeling via graph convolution neural networks, improving the performance or extending the prediction horizon on traffic flows. However, a key shortcoming of state-of-the-art methods is their inability to take into account information of various modalities, for instance the impact of maintenance downtime on traffic flows. This is the issue we address in this paper. Specifically, we propose a novel model to predict traffic speed under the impact of construction work. The model is based on the powerful attention-based spatio-temporal graph convolution architecture but utilizes various channels to integrate different sources of information, explicitly builds spatio-temporal dependencies among traffic states, captures the relationships between heterogeneous roadway networks, and then predicts changes in traffic flow resulting from maintenance downtime events. The model is evaluated on two benchmark datasets and a novel dataset we have collected over the bustling Tyson's corner region in Northern Virginia. Extensive comparative experiments and ablation studies show that the proposed model can capture complex and nonlinear spatio-temporal relationships across a transportation corridor, outperforming baseline models.