SHKD: A framework for traffic prediction based on Sub-Hypergraph and Knowledge Distillation

Abstract

Traffic prediction is a critical function of Intelligent Transportation Systems. Inspired by Graph/HyperGraph Neural Networks theory, researchers have proposed a series of effective methods for traffic prediction that have been proved as significant successes. Most methods construct an unchanging graph or hypergraph based on a fixed traffic network topology during prediction. These methods treat all traffic data (flow, speed, occupancy) equally, ignoring the different inherent attributes of traffic data. Other methods construct dynamic graph or hypergraph based on traffic data but ignore the topological structure of the road network itself. These methods will decrease the accuracy of prediction results. In this paper, we propose an innovative framework for traffic data prediction based on Sub-Hypergraph and Knowledge Distillation (SHKD), which effectively extracts traffic data features and combines them with road network topology. Specifically, we first cluster traffic data based on the inherent attributes and construct hypergraphs for data with similar attributes to represent their relationships, referred to as sub-hypergraphs. Then a teacher network is built from these sub-hypergraphs to extract the data features in traffic, while a student network is constructed based on the geographical connectivity among roads to extract global topological features. To integrate the two types of features, we apply a knowledge distillation method to transfer the data features learned by the teacher network into the training process of the student network, yielding the final prediction results. The proposed method has been assessed with several real-world datasets in predicting traffic status. The experimental results demonstrate the effectiveness of the proposed method.