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

Guo, QL and Karimi, HA (2017) A novel methodology for prediction of spatial-temporal activities using latent features. Computers, Environment and Urban Systems, 62. 74 - 85. ISSN 0198-9715

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Abstract

© 2016 Elsevier Ltd 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.


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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Guo, QL
Karimi, HAhkarimi@pitt.eduHKARIMI
Date: 1 March 2017
Date Type: Publication
Journal or Publication Title: Computers, Environment and Urban Systems
Volume: 62
Page Range: 74 - 85
DOI or Unique Handle: 10.1016/j.compenvurbsys.2016.10.006
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
ISSN: 0198-9715
Date Deposited: 26 Jun 2017 20:12
Last Modified: 13 Oct 2017 18:55
URI: http://d-scholarship.pitt.edu/id/eprint/32480

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