Sparse Heterogeneous Grid Traffic Prediction with Cross-Adaptive Multi-Graph Attention

Traffic prediction plays a vital role in urban transportation systems for effective traffic management, congestion mitigation, and resource allocation. Traditional approaches often overlook the heterogeneity and complexities of real-world traffic systems. In this paper, we propose a novel approach, Sparse Heterogeneous Grid Traffic Prediction with Cross-Adaptive Multi-Graph Attention, which leverages graph neural networks (GNNs) to capture the intricate dependencies among road segments within a sparse and heterogeneous grid framework. The proposed model incorporates cross-adaptive multi-graph attention mechanisms to adaptively capture the varying influences and correlations among different road segments. Real-world traffic datasets are used to evaluate the performance of the proposed model against baseline methods. The results demonstrate the superiority of our approach in terms of prediction accuracy, robustness, and adaptability. The findings from this study contribute to the advancement of intelligent transportation systems and pave the way for more efficient and sustainable urban transportation networks.