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The traffic assignment problem is one of the significant components oftraffic flow analysis for which various solution approaches have been proposed.However, deploying these approaches for large-scale networks poses significantchallenges. In this paper, we leverage the power of heterogeneous graph neuralnetworks to propose a novel end-to-end surrogate model for traffic assignment,specifically user equilibrium traffic assignment problems. Our model integratesan adaptive graph attention mechanism with auxiliary "virtual" links connectingorigin-destination node pairs, This integration enables the model to capturespatial traffic patterns across different links, By incorporating thenode-based flow conservation law into the overall loss function, the modelensures the prediction results in compliance with flow conservation principles,resulting in highly accurate predictions for both link flow and flow-capacityratios. We present numerical experiments on urban transportation networks andshow that the proposed heterogeneous graph neural network model outperformsother conventional neural network models in terms of convergence rate andprediction accuracy. Notably, by introducing two different training strategies,the proposed heterogeneous graph neural network model can also be generalizedto different network topologies. This approach offers a promising solution forcomplex traffic flow analysis and prediction, enhancing our understanding andmanagement of a wide range of transportation systems.

End-to-End Heterogeneous Graph Neural Networks for Traffic Assignment