  While deep learning has shown success in predicting traffic states, most
methods treat it as a general prediction task without considering
transportation aspects. Recently, graph neural networks have proven effective
for this task, but few incorporate external factors that impact roadway
capacity and traffic flow. This study introduces the Roadway Capacity Driven
Graph Convolution Network (RCDGCN) model, which incorporates static and dynamic
roadway capacity attributes in spatio-temporal settings to predict network-wide
traffic states. The model was evaluated on two real-world datasets with
different transportation factors: the ICM-495 highway network and an urban
network in Manhattan, New York City. Results show RCDGCN outperformed baseline
methods in forecasting accuracy. Analyses, including ablation experiments,
weight analysis, and case studies, investigated the effect of capacity-related
factors. The study demonstrates the potential of using RCDGCN for
transportation system management.
