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  4. Vol 9, Issue 2, 2024

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

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DOI: 10.23977/jeis.2024.090208 | Downloads: 12 | Views: 139

Author(s)

Haoyang Duan 1, Feihu Jiang 1

Affiliation(s)

1 School of Computer Science and Technology, USTC, Hefei, China

Corresponding Author

Haoyang Duan

ABSTRACT

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.

KEYWORDS

Turn-level Traffic Flow Prediction, Relation-aware Diffusion Convolution, Hypergraph Learning

CITE THIS PAPER

Haoyang Duan, Feihu Jiang, A GNN Approach for Turn-Level Traffic Prediction: Dynamic Relation Awareness and Hypergraph Modeling. Journal of Electronics and Information Science (2024) Vol. 9: 68-79. DOI: http://dx.doi.org/10.23977/10.23977/jeis.2024.090208.

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