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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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 |
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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 |
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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. |
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LIU, Tongtong YANG, Zheng ZHAO, Yi WU, Chenshu ZHOU, Zimu LIU, Yunhao |
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LIU, Tongtong YANG, Zheng ZHAO, Yi WU, Chenshu ZHOU, Zimu LIU, Yunhao |
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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 |
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Temporal understanding of human mobility: A multi-time scale analysis |
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Temporal understanding of human mobility: A multi-time scale analysis |
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temporal understanding of human mobility: a multi-time scale analysis |
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Institutional Knowledge at Singapore Management University |
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2017 |
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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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