Matrix Factorization for Spatio-Temporal Neural Networks with Applications to Urban Flow Prediction

Abstract

Predicting urban flow is essential for city risk assessment and traffic management, which profoundly impacts people's lives and property. Recently, some deep learning models, focusing on capturing spatio-temporal (ST) correlations between urban regions, have been proposed to predict urban flows. However, these models overlook latent region functions that impact ST correlations greatly. Thus, it is necessary to have a framework to assist these deep models in tackling the region function issue. However, it is very challenging because of two problems: 1) how to make deep models predict flows taking into consideration latent region functions; 2) how to make the framework generalize to a variety of deep models. To tackle these challenges, we propose a novel framework that employs matrix factorization for spatio-temporal neural networks (MF-STN), capable of enhancing the state-of-the-art deep ST models. MF-STN consists of two components: 1) a ST feature learner, which obtains features of ST correlations from all regions by the corresponding sub-networks in the existing deep models; and 2) a region-specific predictor, which leverages the learned ST features to make region-specific predictions. In particular, matrix factorization is employed on the neural networks, namely, decomposing the region-specific parameters of the predictor into learnable matrices, i.e., region embedding matrices and parameter embedding matrices, to model latent region functions and correlations among regions. Extensive experiments were conducted on two real-world datasets, illustrating that MF-STN can significantly improve the performance of some representative ST models while preserving model complexity.