Directed hypergraph attention network for traffic forecasting

Xiaoyi Luo, Jiaheng Peng,Jun Liang


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In traffic systems, traffic forecasting is a critical issue, which has attracted much interest from researchers. It is a challenging task due to the complex spatial-temporal patterns of traffic data. Previous works focus on designing complex graph-based neural networks to model spatial-temporal dependencies from data. By using graphs to represent road networks, these works capture spatial patterns with graph convolutions. However, graphs cannot fully represent spatial relations from road networks. It limits the performance of graph-based methods. In this paper, we propose a directed hypergraph neural network architecture, Directed Hypergraph Attention Network(DHAT), for traffic forecasting. Unlike previous works, DHAT introduces a directed hypergraph to represent road networks. Compared with graphs, directed hypergraphs could represent spatial information from graphs and outperform them in modeling complex directed relations among multiple nodes. Based on the directed hypergraph, a directed hypergraph convolution is proposed to exploit spatial relations among traffic series. By combining the proposed convolution and attention mechanisms, DHAT can effectively achieve promising predictions for traffic forecasting. To evaluate the performance of DHAT, we have conducted extensive experiments on four real-world traffic datasets. Compared with other baselines, experimental results show that DHAT reduces Mean Absolute Error by 0.03-0.64 on these datasets.
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