Abstract: Traffic prediction aims to forecast future traffic conditions based on historical traffic conditions and is a crucial research topic in the field of intelligent transportation systems.Although existing traffic prediction models have achieved considerable progress in modeling complex spatio-temporal patterns,they still heavily rely on advanced deep learning techniques and exhibit limitations in representing multi-source contextual knowledge and integrating global graph-topological information.Consequently,they fail to fully capture the diverse context about external environments and internal networks,which constrains their perfor-mance.To address these issues,this paper proposes MCK-GWN(Multi-source Contextual Knowledge-enhanced Graph WaveNet),a graph-based traffic prediction model enhanced by multi-source contextual knowledgegraph.In terms of multi-source knowledge representation,a multi-source contextual knowledge graph is constructed to enrich the POI(Point of Interest) context of traffic nodes and the semantic context between nodes,effectively characterizing both external and internal contextual knowledge.In terms of multi-source knowledge fusion,the POI heterogeneity-aware unit focuses on modeling external environmental attributes,while the semantic path-aware unit focuses on internal network semantic relations,thereby enhancing the integration capability of global graph-topological information.Experimental results on the SZ-TAXI dataset demonstrate that MCK-GWN achieves the best performance across all evaluation metrics,reducing the mean absolute error(MAE) by 3.15% compared with the state-of-the-art baseline model KMHNet.

Key words: Traffic prediction, Knowledge graph, Graph neural network, External factor fusion