Temporal understanding of human mobility: A multi-time scale analysis

The recent availability of digital traces generated by cellphone calls has significantly increased the scientific understanding of human mobility. Until now, however, based on low time resolution measurements, previous works have ignored to study human mobility under various time scales due to spars...

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Main Authors: LIU, Tongtong, YANG, Zheng, ZHAO, Yi, WU, Chenshu, ZHOU, Zimu, LIU, Yunhao
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Language:English
Published: Institutional Knowledge at Singapore Management University 2017
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Online Access:https://ink.library.smu.edu.sg/sis_research/4529
https://ink.library.smu.edu.sg/context/sis_research/article/5532/viewcontent/Temporal_Human_mobility_plos_2017_pv.pdf
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-55322023-04-12T01:49:20Z Temporal understanding of human mobility: A multi-time scale analysis LIU, Tongtong YANG, Zheng ZHAO, Yi WU, Chenshu ZHOU, Zimu LIU, Yunhao The recent availability of digital traces generated by cellphone calls has significantly increased the scientific understanding of human mobility. Until now, however, based on low time resolution measurements, previous works have ignored to study human mobility under various time scales due to sparse and irregular calls, particularly in the era of mobile Internet. In this paper, we introduced Mobile Flow Records, flow-level data access records of online activity of smartphone users, to explore human mobility. Mobile Flow Records collect high-resolution information of large populations. By exploiting this kind of data, we show the models and statistics of human mobility at a large-scale (3,542,235 individuals) and finer-granularity (7.5min). Next, we investigated statistical variations and biases of mobility models caused by different time scales (from 7.5min to 32h), and found that the time scale does influence the mobility model, which indicates a deep coupling of human mobility and time. We further show that mobility behaviors like transportation modes contribute to the diversity of human mobility, by exploring several novel and refined features (e.g., motion speed, duration, and trajectory distance). Particularly, we point out that 2-hour sampling adopted in previous works is insufficient to study detailed motion behaviors. Our work not only offers a macroscopic and microscopic view of spatial-temporal human mobility, but also applies previously unavailable features, both of which are beneficial to the studies on phenomena driven by human mobility. 2017-11-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4529 info:doi/10.1371/journal.pone.0207697 https://ink.library.smu.edu.sg/context/sis_research/article/5532/viewcontent/Temporal_Human_mobility_plos_2017_pv.pdf http://creativecommons.org/licenses/by/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Algorithms Cell Phone Data Collection Geographic Information Systems Spatio-Temporal Analysis Time Factors Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Algorithms
Cell Phone
Data Collection
Geographic Information Systems
Spatio-Temporal Analysis
Time Factors
Software Engineering
spellingShingle Algorithms
Cell Phone
Data Collection
Geographic Information Systems
Spatio-Temporal Analysis
Time Factors
Software Engineering
LIU, Tongtong
YANG, Zheng
ZHAO, Yi
WU, Chenshu
ZHOU, Zimu
LIU, Yunhao
Temporal understanding of human mobility: A multi-time scale analysis
description The recent availability of digital traces generated by cellphone calls has significantly increased the scientific understanding of human mobility. Until now, however, based on low time resolution measurements, previous works have ignored to study human mobility under various time scales due to sparse and irregular calls, particularly in the era of mobile Internet. In this paper, we introduced Mobile Flow Records, flow-level data access records of online activity of smartphone users, to explore human mobility. Mobile Flow Records collect high-resolution information of large populations. By exploiting this kind of data, we show the models and statistics of human mobility at a large-scale (3,542,235 individuals) and finer-granularity (7.5min). Next, we investigated statistical variations and biases of mobility models caused by different time scales (from 7.5min to 32h), and found that the time scale does influence the mobility model, which indicates a deep coupling of human mobility and time. We further show that mobility behaviors like transportation modes contribute to the diversity of human mobility, by exploring several novel and refined features (e.g., motion speed, duration, and trajectory distance). Particularly, we point out that 2-hour sampling adopted in previous works is insufficient to study detailed motion behaviors. Our work not only offers a macroscopic and microscopic view of spatial-temporal human mobility, but also applies previously unavailable features, both of which are beneficial to the studies on phenomena driven by human mobility.
format text
author LIU, Tongtong
YANG, Zheng
ZHAO, Yi
WU, Chenshu
ZHOU, Zimu
LIU, Yunhao
author_facet LIU, Tongtong
YANG, Zheng
ZHAO, Yi
WU, Chenshu
ZHOU, Zimu
LIU, Yunhao
author_sort LIU, Tongtong
title Temporal understanding of human mobility: A multi-time scale analysis
title_short Temporal understanding of human mobility: A multi-time scale analysis
title_full Temporal understanding of human mobility: A multi-time scale analysis
title_fullStr Temporal understanding of human mobility: A multi-time scale analysis
title_full_unstemmed Temporal understanding of human mobility: A multi-time scale analysis
title_sort temporal understanding of human mobility: a multi-time scale analysis
publisher Institutional Knowledge at Singapore Management University
publishDate 2017
url https://ink.library.smu.edu.sg/sis_research/4529
https://ink.library.smu.edu.sg/context/sis_research/article/5532/viewcontent/Temporal_Human_mobility_plos_2017_pv.pdf
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