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EigenTransitions with hypothesis testing: The anatomy of urban mobility

Zhang, K and Lin, YR and Pelechrinis, K (2016) EigenTransitions with hypothesis testing: The anatomy of urban mobility. In: UNSPECIFIED.

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Abstract

© Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Identifying the patterns in urban mobility is important for a variety of tasks such as transportation planning, urban resource allocation, emergency planning etc. This is evident from the large body of research on the topic, which has exploded with the vast amount of geo-tagged user-generated content from online social media. However, most of the existing work focuses on a specific setting, taking a statistical approach to describe and model the observed patterns. On the contrary in this work we introduce EigenTransitions, a spectrum-based, generic framework for analyzing spatiotemporal mobility datasets. EigenTransitions capture the anatomy of the aggregate and/or individuals' mobility as a compact set of latent mobility patterns. Using a large corpus of geo-tagged content collected from Twitter, we utilize EigenTransitions to analyze the structure of urban mobility. In particular, we identify the EigenTransitions of a flow network between urban areas and derive hypothesis testing framework to evaluate urban mobility from both temporal and demographic perspectives. We further show how EigenTransitions not only identify latent mobility patterns, but also have the potential to support applications such as mobility prediction and inter-city comparisons. In particular, by identifying neighbors with similar latent mobility patterns and incorporating their historical transition behaviors, we proposed an EigenTransitions-based k-nearest neighbor algorithm, which can significantly improve the performance of individual mobility prediction. The proposed method is especially effective in "cold-start" scenarios where traditional methods are known to perform poorly.


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Details

Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Zhang, K
Lin, YRYURULIN@pitt.eduYURULIN
Pelechrinis, Kkpele@pitt.eduKPELE
Date: 1 January 2016
Date Type: Publication
Journal or Publication Title: Proceedings of the 10th International Conference on Web and Social Media, ICWSM 2016
Page Range: 486 - 495
Event Type: Conference
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
ISBN: 9781577357582
Date Deposited: 28 Jun 2016 15:01
Last Modified: 02 Feb 2019 13:55
URI: http://d-scholarship.pitt.edu/id/eprint/28288

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