Abstract: To address the challenges of heterogeneous feature modeling and insufficient interaction fusion in intra-city delivery scenarios,this paper proposes a Prompt-guided bidirectional heterogeneous graph Transformer model(P-BiHGT).The model introduces virtual Prompt nodes as global semantic anchors and explicitly connects them with both vehicle and order nodes,thereby enhancing the fusion and propagation of global semantic information across the graph.Furthermore,to tackle the weak semantic association and unidirectional interaction modeling between vehicle and order nodes,a role-aware bidirectional attention mechanism is designed to model interaction paths in both directions:from vehicles to orders and vice versa.After completing bidirectionalinteraction modeling,a multilayer perceptron(MLP) is employed to make high-level matching decisions on the fused feature pairs,improving the overall matching accuracy.Experimental results on a simulated intra-city delivery dataset show that the proposed model achieves a validation accuracy of 93.6%,significantly outperforming traditional models,thus demonstrating the effectiveness and adaptability of P-BiHGT in heterogeneous matching tasks.

Key words: Prompt node, Graph neural network, Heterogeneous graph, Bidirectional attention, Intra-city delivery