Our new paper, titled “Context-Aware Knowledge Graph Framework for Traffic Speed Forecasting Using Graph Neural Networks,” has been accepted for publication in IEEE Transactions on Intelligent Transportation Systems.
This paper introduces a novel context-aware knowledge graph (CKG) framework to enhance traffic speed forecasting by effectively modeling spatial and temporal contexts. Using a relation-dependent integration strategy, the framework generates context-aware representations that capture the intricate spatio-temporal dependencies of urban environments.
Building on this foundation, a CKG-GNN model is developed, integrating the CKG, dual-view multi-head self-attention mechanisms, and graph neural networks (GNNs). This integration not only significantly improves predictive accuracy but also underscores the importance of contextual information in forecasting traffic dynamics.
By bridging domain knowledge with graph neural architectures, the proposed approach demonstrates its potential for advancing intelligent transportation systems.
Please check our paper and codes.
