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ISPRS Int. J. Geo-Inf. 2016, 5(6), 78; doi:10.3390/ijgi5060078

Analyzing Urban Human Mobility Patterns through a Thematic Model at a Finer Scale

1
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1,2
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1,2,* , 3,* , 1,2
and
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1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2
Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China
3
Department of Geography, Kent State University, Kent, OH 44240, USA
4
School of Navigation, Wuhan University of Technology, Wuhan 430063, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Constantinos Antoniou and Wolfgang Kainz
Received: 7 April 2016 / Revised: 15 May 2016 / Accepted: 23 May 2016 / Published: 1 June 2016
(This article belongs to the Special Issue Geospatial Big Data and Transport)
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Abstract

Taxi trajectories reflect human mobility over a road network. Pick-up and drop-off locations in different time periods represent origins and destinations of trips, respectively, demonstrating the spatiotemporal characteristics of human behavior. Each trip can be viewed as a displacement in the random walk model, and the distribution of extracted trips shows a distance decay effect. To identify the spatial similarity of trips at a finer scale, this paper investigates the distribution of trips through topic modeling techniques. Firstly, trip origins and trip destinations were identified from raw GPS data. Then, different trips were given semantic information, i.e., link identification numbers with a semantic enrichment process. Each taxi trajectory was composed of a series of trip destinations corresponding to the same taxi. Subsequently, each taxi trajectory was analogous to a document consisting of different words, and all taxi’s trajectories could be regarded as document corpora, enabling a semantic analysis of massive trip destinations. Finally, we obtained different trip destination topics reflecting the spatial similarity and regional property of human mobility through LDA topic model training. The effectiveness of this approach was illustrated by a case study using a large dataset of taxi trajectories collected from 2 to 8 June 2014 in Wuhan, China. View Full-Text
Keywords: mobility; semantic enrichment; LDA topic model; finer scale; China mobility; semantic enrichment; LDA topic model; finer scale; China
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Zhang, F.; Zhu, X.; Guo, W.; Ye, X.; Hu, T.; Huang, L. Analyzing Urban Human Mobility Patterns through a Thematic Model at a Finer Scale. ISPRS Int. J. Geo-Inf. 2016, 5, 78.

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