A novel methodology for prediction of spatial-temporal activities using latent features

Abstract

In today's era of big data, huge amounts of spatial-temporal data are generated daily from all kinds of citywide infrastructures. Understanding and predicting accurately such a large amount of data could benefit many real-world applications. In this paper, we propose a novel methodology for prediction of spatial-temporal activities such as human mobility, especially the inflow and outflow of people in urban environments based on existing large-scale mobility datasets. Our methodology first identifies and quantifies the latent characteristics of different spatial environments and temporal factors through tensor factorization. Our hypothesis is that the patterns of spatial-temporal activities are highly dependent on or caused by these latent spatial-temporal features. We model this hidden dependent relationship as a Gaussian process, which can be viewed as a distribution over the possible functions to predict human mobility. We tested our proposed methodology through experiments conducted on a case study of New York City's taxi trips and focused on the mobility patterns of spatial-temporal inflow and outflow across different spatial areas and temporal time periods. The results of the experiments verify our hypothesis and show that our prediction methodology achieves a much higher accuracy than other existing methodologies.