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Understanding Urban Human Mobility through Crowdsensed Data
Social and Information Networks · Submitted 2 May 2018
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Beyond demonstration purpose, the two case studies also make contributions to their category of certain crowdsensed data type and mobility subject.
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The abstract, sentence by sentence
Understanding how people move in the urban area is important for solving urbanization issues, such as traffic management, urban planning, epidemic control, and communication network improvement. Leveraging recent availability of large amounts of diverse crowdsensed data, many studies have made contributions to this field in various aspects. They need proper review and summary. In this paper, therefore, we first review these recent studies with a proper taxonomy with corresponding examples. Then, based on the experience learnt from the studies, we provide a comprehensive tutorial for future research, which introduces and discusses popular crowdsensed data types, different human mobility subjects, and common data preprocessing and analysis methods. Special emphasis is made on the matching between data types and mobility subjects. Finally, we present two research projects as case studies to demonstrate the entire process of understanding urban human mobility through crowdsensed data in city-wide scale and building-wide scale respectively. Beyond demonstration purpose, the two case studies also make contributions to their category of certain crowdsensed data type and mobility subject.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Understanding how people move in the urban area is important for solving urbanization issues, such as traffic management, urban planning, epidemic control, and communication network improvement. | background 1.00 | 0.04 | 0.03 | asserted 1.00 |
| s1 | Leveraging recent availability of large amounts of diverse crowdsensed data, many studies have made contributions to this field in various aspects. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s2 | They need proper review and summary. | gap 0.80 | 0.05 | 0.03 | asserted 0.96 |
| s3 | In this paper, therefore, we first review these recent studies with a proper taxonomy with corresponding examples. | contribution 0.97 | 0.81 | 0.04 | asserted 0.90 |
| s4 | Then, based on the experience learnt from the studies, we provide a comprehensive tutorial for future research, which introduces and discusses popular crowdsensed data types, different human mobility subjects, and common data preprocessing and analysis methods. | contribution 0.93 | 0.87 | 0.09 | asserted 0.99 |
| s5 | Special emphasis is made on the matching between data types and mobility subjects. | contribution 0.85 | 0.65 | 0.08 | asserted 0.84 |
| s6 | Finally, we present two research projects as case studies to demonstrate the entire process of understanding urban human mobility through crowdsensed data in city-wide scale and building-wide scale respectively. | contribution 0.95 | 0.92 | 0.11 | asserted 1.00 |
| s7 | Beyond demonstration purpose, the two case studies also make contributions to their category of certain crowdsensed data type and mobility subject. | contribution 0.97 | 0.85 | 0.16 | asserted 0.99 |
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The paper
Original abstract
Understanding how people move in the urban area is important for solving urbanization issues, such as traffic management, urban planning, epidemic control, and communication network improvement. Leveraging recent availability of large amounts of diverse crowdsensed data, many studies have made contributions to this field in various aspects. They need proper review and summary. In this paper, therefore, we first review these recent studies with a proper taxonomy with corresponding examples. Then, based on the experience learnt from the studies, we provide a comprehensive tutorial for future research, which introduces and discusses popular crowdsensed data types, different human mobility subjects, and common data preprocessing and analysis methods. Special emphasis is made on the matching between data types and mobility subjects. Finally, we present two research projects as case studies to demonstrate the entire process of understanding urban human mobility through crowdsensed data in city-wide scale and building-wide scale respectively. Beyond demonstration purpose, the two case studies also make contributions to their category of certain crowdsensed data type and mobility subject.
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