Heterogeneous Graph Neural Networks for Data-driven Traffic Assignment
Tong Liu Hadi Meidani
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 traffic assignment and
traffic flow learning. The proposed model is capable of capturing spatial
traffic patterns across different links, yielding highly accurate results. 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, training loss,
and prediction accuracy. Notably, 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.