  The traffic assignment problem is one of the significant components of
traffic flow analysis for which various solution approaches have been proposed.
However, deploying these approaches for large-scale networks poses significant
challenges. In this paper, we leverage the power of heterogeneous graph neural
networks to propose a novel data-driven approach for end-to-end traffic
assignment and traffic flow learning. Our model integrates an adaptive graph
attention mechanism with auxiliary "virtual" links connecting
origin-destination node pairs, This integration enables the model to capture
spatial traffic patterns across different links, By incorporating the
node-based flow conservation law into the overall loss function, the model
ensures the prediction results in compliance with flow conservation principles,
resulting in highly accurate predictions for both link flow and flow-capacity
ratios. We present numerical experiments on urban transportation networks and
show that the proposed heterogeneous graph neural network model outperforms
other conventional neural network models in terms of convergence rate and
prediction accuracy. Notably, by introducing two different training strategies,
the proposed heterogeneous graph neural network model can also be generalized
to different network topologies. This approach offers a promising solution for
complex traffic flow analysis and prediction, enhancing our understanding and
management of a wide range of transportation systems.
