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.

利用异构图神经网络提出了一种新的数据驱动交通分配和交通流学习方法，该模型能够捕捉不同链接上的空间交通模式，具有高精度的结果，并在城市交通网络上进行了数值实验，表明该模型在收敛速度、训练损失和预测准确性方面优于其他传统神经网络模型，同时也具有泛化到不同网络拓扑的能力，为复杂交通流分析和预测提供了有希望的解决方案，增强了对各种交通系统的理解和管理能力。