A GNN Approach for Turn-Level Traffic Prediction: Dynamic Relation Awareness and Hypergraph Modeling

Le résumé fourni par la source

It cannot be emphasized too much to predict traffic flow accurately in modern intelligent transportation systems. Though big progress has been made, few works focus on the turn-level traffic flow prediction, which is important to inspect fine-grained urban traffic dynamics closely. In this work, we develop a GNN (Graph Neural Network) approach built upon Dynamic Relation Awareness and Hypergraph modeling toward turn-level traffic flow prediction, namely DrahGNN. First, we construct a dynamic graph sequence where each snapshot denotes a turn-level traffic flow picture on top of a real-world road network. Second, we innovate a relation-aware spatiotemporal diffusion convolution network to capture road segments’ differences and relatedness explicitly. Third, we construct a hypergraph in each time frame to capture high-order and manifold correlations between road segments and design an attentive two-stage message-passing mechanism for aggregating infor- mation from non-directly connected nodes. We conduct empirical studies on real-world data which demonstrate the effectiveness of our proposed framework.

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