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What Does a Crowd Routing Behavior Change Reveal?

Published: 30 October 2018 Publication History

Abstract

Human mobility patterns can be identified through the analysis of GPS and smartphone data. This identification has been the theme of several studies, particularly in the smart cities domain. Considering that patterns identify human routines, changes in these patterns may also provide useful information. This paper reports the analysis of GPS data from a group of Chinese in Beijing from April 2009 till October 2012, identifying routine routing patterns and sudden collective breaks on those patterns. Artificial Intelligence techniques were used to identify mobility patterns. Olympic games dates and the 2009 pollution peak were identified though our method. We believe it can be a powerful tool to infer mass events occurring in any part of the world.

Supplementary Material

ZIP File (cscwp092.zip)
PDF of our posterboard presentation.

References

[1]
Michele Acuto, Susan Parnell, and Karen C Seto. 2018. Building a global urban science. Nature Sustainability 1, 1 (2018), 2.
[2]
Shan Jiang, Joseph Ferreira, and Marta C Gonzalez. 2017. Activity-based human mobility patterns inferred from mobile phone data: A case study of Singapore. IEEE Transactions on Big Data 3, 2 (2017), 208--219.
[3]
Yunji Liang, Xingshe Zhou, Bin Guo, and Zhiwen Yu. 2012. Understanding the regularity and variability of human mobility from geo-trajectory. In Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology-Volume 01. IEEE Computer Society, 409--414.
[4]
Feng Xia, Jinzhong Wang, Xiangjie Kong, Zhibo Wang, Jianxin Li, and Chengfei Liu. 2018. Exploring human mobility patterns in urban scenarios: A trajectory data perspective. IEEE Communications Magazine 56, 3 (2018), 142--149.
[5]
Yu Zheng, Lizhu Zhang, Xing Xie, and Wei-Ying Ma. 2009. Mining interesting locations and travel sequences from GPS trajectories. In Proceedings of the 18th international conference on World wide web. ACM, 791--800.

Cited By

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  • (2021)Characterizing Student Engagement Moods for Dropout Prediction in Question Pool WebsitesProceedings of the ACM on Human-Computer Interaction10.1145/34490865:CSCW1(1-22)Online publication date: 22-Apr-2021

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Published In

cover image ACM Conferences
CSCW '18 Companion: Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing
October 2018
518 pages
ISBN:9781450360180
DOI:10.1145/3272973
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 October 2018

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Author Tags

  1. collective intelligence
  2. crowdsourcing
  3. data analysis
  4. mobility
  5. smart cities

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CSCW '18
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CSCW '18 Companion Paper Acceptance Rate 105 of 385 submissions, 27%;
Overall Acceptance Rate 2,235 of 8,521 submissions, 26%

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View all
  • (2021)Characterizing Student Engagement Moods for Dropout Prediction in Question Pool WebsitesProceedings of the ACM on Human-Computer Interaction10.1145/34490865:CSCW1(1-22)Online publication date: 22-Apr-2021

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