Geographic-Semantic-Temporal Hypergraph Convolutional Network for Traffic Flow Prediction

摘要

Traffic flow forecasting has become an increasingly important part of intelligent traffic control and management. This task is challenging due to (1) complex geographic and non-geographic spatial correlations; (2) temporal correlations between time slices; (3) dynamics of semantic high-order correlations along the temporal dimension. To address those difficulties, commonly-used methods apply graph convolutional networks for spatial correlations and recurrent neural networks for temporal dependencies. In this work, we distinguish the two aspects of spatial correlations and propose the two types of spatial graphs, named as geographic graph and semantic hypergraph. Furthermore, we extend the traditional convolution into geographic-temporal graph convolution and semantic-temporal hypergraph convolution to jointly capture geographic-temporal correlations and semantic-temporal correlations. Then we propose a geographic-semantic-temporal hypergraph convolutional network (GST-HCN) that combines our graph convolutions and GRU units hierarchically in a unified end-to-end network. The experiment results on the Caltrans Performance Measurement System (PeMS) dataset show that our proposed model significantly outperforms other popular spatio-temporal deep learning models and suggests the effectiveness to explore geographic-semantic-temporal dependencies on deep learning models for traffic flow prediction.